From 43d06770639f801d0199d67dcdbedd69e6774eaf Mon Sep 17 00:00:00 2001
From: Sinan <sinan_ceviker@yahoo.com>
Date: Sat, 24 May 2025 22:53:32 +0200
Subject: [PATCH 01/32] started repolishing namings and structure of data
 pipeline for final structure

---
 recaptcha_classifier/data/__init__.py       | 15 ++++-----
 recaptcha_classifier/data/augment.py        | 14 ++++-----
 recaptcha_classifier/data/collate_batch.py  |  7 +----
 recaptcha_classifier/data/dataset.py        |  4 +--
 recaptcha_classifier/data/downloader.py     | 19 +++++++-----
 recaptcha_classifier/data/loader_factory.py |  6 ++--
 recaptcha_classifier/data/pair_loader.py    |  6 ++--
 recaptcha_classifier/data/splitter.py       | 14 ++++-----
 recaptcha_classifier/data/types.py          | 34 +++++++--------------
 recaptcha_classifier/data/visualizer.py     | 10 +++---
 recaptcha_classifier/detection_labels.py    |  7 -----
 11 files changed, 56 insertions(+), 80 deletions(-)

diff --git a/recaptcha_classifier/data/__init__.py b/recaptcha_classifier/data/__init__.py
index c341ff9f94..c7d918d6f9 100644
--- a/recaptcha_classifier/data/__init__.py
+++ b/recaptcha_classifier/data/__init__.py
@@ -16,11 +16,9 @@
 from .collate_batch import collate_batch
 
 from .types import (
-    FilePair,
-    FilePairList,
-    DatasetSplitDict,
-    BBoxList,
-    DataPair,
+    ImagePathList,
+    DatasetSplitMap,
+    LoadedImg,
     DataItem,
     DataBatch
 )
@@ -43,10 +41,9 @@
     "collate_batch",
     # Types
     "FilePair",
-    "FilePairList",
-    "DatasetSplitDict",
-    "BBoxList",
-    "DataPair",
+    "ImagePathList",
+    "DatasetSplitMap",
+    "LoadedImg",
     "DataItem",
     "DataBatch",
 ]
diff --git a/recaptcha_classifier/data/augment.py b/recaptcha_classifier/data/augment.py
index ffbf127646..8b622db68f 100644
--- a/recaptcha_classifier/data/augment.py
+++ b/recaptcha_classifier/data/augment.py
@@ -3,7 +3,7 @@
 from PIL import Image
 from typing import List
 from .scaler import YOLOScaler
-from .types import DataPair, BBoxList
+from .types import LoadedImg, BBoxList
 
 
 class Augmentation(ABC):
@@ -11,7 +11,7 @@ class Augmentation(ABC):
     @abstractmethod
     def augment(self,
                 image: Image.Image,
-                annotations: List) -> DataPair:
+                annotations: List) -> LoadedImg:
         """
         Apply the transformation of the image and updates the bounding boxes
         if necessary.
@@ -21,7 +21,7 @@ def augment(self,
             annotations (List): List of annotations associated with the image.
 
         Returns:
-            DataPair: The augmented image and the updated
+            LoadedImg: The augmented image and the updated
             annotations.
         """
         pass
@@ -34,7 +34,7 @@ def __init__(self, transforms=[]) -> None:
 
     def apply_transforms(self,
                          image: Image.Image,
-                         annotations: BBoxList) -> DataPair:
+                         annotations: BBoxList) -> LoadedImg:
         """
         Apply all transformations in the pipeline to the image and
         annotations.
@@ -45,7 +45,7 @@ def apply_transforms(self,
             associated with the image.
 
         Returns:
-            DataPair: The augmented image and the updated
+            LoadedImg: The augmented image and the updated
             annotations.
         """
         for transform in self._transforms:
@@ -62,7 +62,7 @@ def __init__(self, p: float = 0.5) -> None:
 
     def augment(self,
                 image: Image.Image,
-                annotations: BBoxList) -> DataPair:
+                annotations: BBoxList) -> LoadedImg:
         flipped = image.transpose(Image.FLIP_LEFT_RIGHT)
         new_annotations = YOLOScaler.scale_for_flip(annotations)
 
@@ -80,7 +80,7 @@ def __init__(self, degrees: float = 30.0, p: float = 0.5) -> None:
 
     def augment(self,
                 image: Image.Image,
-                annotations: BBoxList) -> DataPair:
+                annotations: BBoxList) -> LoadedImg:
         angle = random.uniform(-self._degrees, self._degrees)
 
         rotated = image.rotate(angle)
diff --git a/recaptcha_classifier/data/collate_batch.py b/recaptcha_classifier/data/collate_batch.py
index 55cc67c688..a1f12afab6 100644
--- a/recaptcha_classifier/data/collate_batch.py
+++ b/recaptcha_classifier/data/collate_batch.py
@@ -10,25 +10,20 @@ def collate_batch(batch: List[DataItem]) -> DataBatch:
 
     Args:
         batch (List[DataItem]): A batch of training items; each item is
-        a tuple of format (image tensor, bounding boxes, class index).
+        a tuple of format (image tensor, class index).
 
     Returns:
         Batch: A single tuple containing:
             - images_tensor (Tensor): Batched images of
             shape (batch_size, 3, H, W)
-            - bboxes (List[BBoxList]): A list of
-            bounding boxes for each image
             - labels_tensor (Tensor): A tensor of shape
             containing the class indices.
     """
     images = [item[0] for item in batch]
-    # bboxes = [item[1] for item in batch]
-    # labels = [item[2] for item in batch]
     labels = [item[1] for item in batch]
 
     # Stack them as (3, H, W) tensors
     images_tensor = torch.stack(images)
     labels_tensor = torch.stack(labels)
 
-    # return images_tensor, bboxes, labels_tensor
     return images_tensor, labels_tensor
diff --git a/recaptcha_classifier/data/dataset.py b/recaptcha_classifier/data/dataset.py
index 803b4b6027..f14ad87f0b 100644
--- a/recaptcha_classifier/data/dataset.py
+++ b/recaptcha_classifier/data/dataset.py
@@ -2,7 +2,7 @@
 from torch.utils.data import Dataset
 from .preprocessor import ImagePrep
 from .augment import AugmentationPipeline
-from .types import FilePairList, DataItem
+from .types import ImagePathList, DataItem
 
 
 class ImageDataset(Dataset):
@@ -17,7 +17,7 @@ class ImageDataset(Dataset):
     https://docs.pytorch.org/tutorials/beginner/basics/data_tutorial.html
     """
     def __init__(self,
-                 pairs: FilePairList,
+                 pairs: ImagePathList,
                  preprocessor: ImagePrep,
                  augmentator: Optional[AugmentationPipeline] = None,
                  class_map: dict = {}
diff --git a/recaptcha_classifier/data/downloader.py b/recaptcha_classifier/data/downloader.py
index 0ba4c6a336..e03c87561c 100644
--- a/recaptcha_classifier/data/downloader.py
+++ b/recaptcha_classifier/data/downloader.py
@@ -1,11 +1,9 @@
 import requests
 import zipfile
-import logging
+import shutil
 from pathlib import Path
 from alive_progress import alive_bar
 
-logger = logging.getLogger(__name__)
-
 
 class DatasetDownloader:
     """
@@ -37,11 +35,11 @@ def download(self) -> None:
         If not, downloads and then unzips it.
         """
         if self._is_downloaded():
-            logger.info("Dataset already exists, skipping download.")
+            print("Dataset already exists, skipping download.")
             return
 
         self._prepare_dest()
-        logger.info(f"Downloading {self._url} to {self._dest}...")
+        print(f"Downloading {self._url} to {self._dest}...")
         self._download_zip()
         self._unzip_and_cleanup()
 
@@ -82,7 +80,7 @@ def _download_zip(self) -> None:
             for chunk in resp.iter_content(chunk_size=8192):
                 f.write(chunk)
                 bar(len(chunk))
-        logger.info("Download completed successfully.")
+        print("Download completed successfully.")
 
     def _unzip_and_cleanup(self) -> None:
         """
@@ -92,7 +90,7 @@ def _unzip_and_cleanup(self) -> None:
         Note: this assumes that the downloaded dataset has exactly
         the structure of our selected Kaggle dataset for simplicity.
         """
-        logger.info("Extracting...")
+        print("Extracting...")
         with zipfile.ZipFile(self._zip_path) as z:
             z.extractall(self._dest)
 
@@ -101,5 +99,10 @@ def _unzip_and_cleanup(self) -> None:
             (root / sub).rename(self._dest / sub)
 
         root.rmdir()
+        
+        label_dir = self._dest / "labels"
+        if label_dir.exists() and label_dir.is_dir():
+            shutil.rmtree(label_dir)
+        
         self._zip_path.unlink()
-        print("Extraction and cleanup completed successfully.")
+        print("Extraction and cleanup completed successfully.")
\ No newline at end of file
diff --git a/recaptcha_classifier/data/loader_factory.py b/recaptcha_classifier/data/loader_factory.py
index 0b804644bb..caa452fbee 100644
--- a/recaptcha_classifier/data/loader_factory.py
+++ b/recaptcha_classifier/data/loader_factory.py
@@ -4,7 +4,7 @@
 from .dataset import ImageDataset
 from .preprocessor import ImagePrep
 from .augment import AugmentationPipeline
-from .types import DatasetSplitDict, FilePairList
+from .types import DatasetSplitMap, ImagePathList
 from .collate_batch import collate_batch
 
 
@@ -40,12 +40,12 @@ def __init__(self,
         #  OPTIONAL: self._loaders to cache response
 
     def create_loaders(self,
-                       splits: DatasetSplitDict) -> Dict[str, DataLoader]:
+                       splits: DatasetSplitMap) -> Dict[str, DataLoader]:
         loaders: Dict[str, DataLoader] = {}
 
         for split_name, cls_dict in splits.items():
             # flatten nested dict of pairs
-            flat_pairs: FilePairList = [pair
+            flat_pairs: ImagePathList = [pair
                                         # traversing over classes
                                         for pairs in cls_dict.values()
                                         # traversing over pairs
diff --git a/recaptcha_classifier/data/pair_loader.py b/recaptcha_classifier/data/pair_loader.py
index c8885cbf2f..828727c5d0 100644
--- a/recaptcha_classifier/data/pair_loader.py
+++ b/recaptcha_classifier/data/pair_loader.py
@@ -1,7 +1,7 @@
 import logging
 from pathlib import Path
 from typing import List, Dict
-from .types import ClassFileDict
+from .types import ClassToImgPaths
 
 logger = logging.getLogger(__name__)
 
@@ -33,9 +33,9 @@ def __init__(self,
         self._classes = classes
         self._images_dir = Path(images_dir)
         # self._labels_dir = Path(labels_dir)
-        self._pairs: ClassFileDict = dict()
+        self._pairs: ClassToImgPaths = dict()
 
-    def find_pairs(self) -> ClassFileDict:
+    def find_pairs(self) -> ClassToImgPaths:
         """
         Returns all pairs loaded for the given classes.
         It caches the response after first run.
diff --git a/recaptcha_classifier/data/splitter.py b/recaptcha_classifier/data/splitter.py
index 4e36bbdb76..31416a9274 100644
--- a/recaptcha_classifier/data/splitter.py
+++ b/recaptcha_classifier/data/splitter.py
@@ -1,6 +1,6 @@
 import random
 from typing import Tuple
-from .types import ClassFileDict, DatasetSplitDict, FilePairList
+from .types import ClassToImgPaths, DatasetSplitMap, ImagePathList
 
 
 class DataSplitter:
@@ -29,8 +29,8 @@ def __init__(self,
         self._validate_ratios()
 
     def split(self,
-              pairs_by_class: ClassFileDict
-              ) -> DatasetSplitDict:
+              pairs_by_class: ClassToImgPaths
+              ) -> DatasetSplitMap:
         """
         Splits each class into train, validation, and test sets.
         It shuffles the data if specified and returns a dictionary
@@ -40,7 +40,7 @@ def split(self,
             items (List): List of items to be split.
 
         Returns:
-            DatasetSplitDict: Nested dictionary containing
+            DatasetSplitMap: Nested dictionary containing
             splits for each class.
         """
         splits = {'train': {}, 'val': {}, 'test': {}}
@@ -69,16 +69,16 @@ def _validate_ratios(self) -> None:
         if any(ratio < 0 for ratio in self._ratios):
             raise ValueError("Ratios must be positive.")
 
-    def _shuffle_items(self, items: FilePairList) -> FilePairList:
+    def _shuffle_items(self, items: ImagePathList) -> ImagePathList:
         """
         Returns a shuffled copied version of the items list,
         using seed if provided.
 
         Args:
-            items (FilePairList): List of items to be shuffled.
+            items (ImagePathList): List of items to be shuffled.
 
         Returns:
-            FilePairList: Shuffled list of items.
+            ImagePathList: Shuffled list of items.
         """
         new_items = items.copy()
         rand = random.Random(self._seed)
diff --git a/recaptcha_classifier/data/types.py b/recaptcha_classifier/data/types.py
index f5ad6b42d7..f5537406a1 100644
--- a/recaptcha_classifier/data/types.py
+++ b/recaptcha_classifier/data/types.py
@@ -3,34 +3,22 @@
 from pathlib import Path
 from torch import Tensor
 
-# OBJECT DETECTION TASK CLASSES
-# A (image, label) pair containing their system paths/ locations
-FilePair = Path  # Tuple[Path, Path]
-
-# A list of (image, label) pairs, for more items in the dataset
-FilePairList = List[FilePair]
+# A list of image paths, for more items in the dataset
+ImagePathList = List[Path]
 
 # A dictionary where the keys are class names and
-# the values are lists of (image, label) pairs, elements of that class
-ClassFileDict = Dict[str, FilePairList]
+# the values are lists of all image paths for that class in the dataset
+ClassToImgPaths = Dict[str, ImagePathList]
 
 # A nested dictionary where main keys are train/val/test
-# and the subkeys are class names
-DatasetSplitDict = Dict[str, ClassFileDict]
-
-# YOLO bounding box format (x_center, y_center, width, height)
-BBox = Tuple[float, float, float, float]
-
-# List of bounding boxes for one image from the dataset
-BBoxList = List[BBox]
+# and the subkeys are class names, containing all image paths of that class
+DatasetSplitMap = Dict[str, ClassToImgPaths]
 
-# A dataset item, of (image, annotations), where both features
-# now loaded in memory, instead of Paths like in FilePair
-DataPair = Tuple[Image.Image, BBoxList]
+# A dataset item, where the image is now loaded from disk
+LoadedImg = Image.Image
 
-# A final dataset item, that now contains the image tensor,
-# the bounding boxes and the class id; ready for model training
-DataItem = Tuple[Tensor, BBoxList, int]
+# A final dataset item, that now contains the image tensor, and the class id; ready for model training
+DataItem = Tuple[Tensor, int]
 
 # Output of the dataloader, a batch of data items
-DataBatch = Tuple[Tensor, List[BBoxList], Tensor]
+DataBatch = Tuple[Tensor, Tensor]
diff --git a/recaptcha_classifier/data/visualizer.py b/recaptcha_classifier/data/visualizer.py
index cca973d9ef..b0315c9dea 100644
--- a/recaptcha_classifier/data/visualizer.py
+++ b/recaptcha_classifier/data/visualizer.py
@@ -1,5 +1,5 @@
 import matplotlib.pyplot as plt
-from .types import DatasetSplitDict
+from .types import DatasetSplitMap
 import numpy as np
 
 
@@ -10,12 +10,12 @@ class Visualizer:
     visualization of classes across the splits.
     """
     @classmethod
-    def print_counts(cls, splits: DatasetSplitDict) -> None:
+    def print_counts(cls, splits: DatasetSplitMap) -> None:
         """
         Prints the counts of samples in each class, for each split.
 
         Args:
-            splits (DatasetSplitDict): the dataset splits
+            splits (DatasetSplitMap): the dataset splits
                 containing the pairs for each class.
         """
         for split, cls_dict in splits.items():
@@ -26,14 +26,14 @@ def print_counts(cls, splits: DatasetSplitDict) -> None:
 
     @classmethod
     def plot_splits(cls,
-                    splits: DatasetSplitDict,
+                    splits: DatasetSplitMap,
                     title: str = "Class Distribution in Splits") -> None:
         """
         Plots a bar chart showing the amount and percentage of samples
         present in each class, for each of the splits.
 
         Args:
-            splits (DatasetSplitDict): the dataset splits
+            splits (DatasetSplitMap): the dataset splits
                 containing the pairs for each class.
             title (str): the title of the plot.
         """
diff --git a/recaptcha_classifier/detection_labels.py b/recaptcha_classifier/detection_labels.py
index 5c9fe8872c..2ca79204d7 100644
--- a/recaptcha_classifier/detection_labels.py
+++ b/recaptcha_classifier/detection_labels.py
@@ -5,13 +5,6 @@ class DetectionLabels(Enum):
     """
     Enum for improving readability of the object classes.
     """
-
-    """
-    OBJECT DETECTION TASK CLASSES
-    CROSSWALK = 0
-    CHIMNEY = 1
-    STAIR = 2
-    """
     BICYCLE = 0
     BRIDGE = 1
     BUS = 2

From e0710793c2824d59678bb9a46307dd5f8bf4de41 Mon Sep 17 00:00:00 2001
From: Sinan <sinan_ceviker@yahoo.com>
Date: Sat, 24 May 2025 23:35:18 +0200
Subject: [PATCH 02/32] revamped download process to cleanup structure of
 dataset

---
 main.py                                   |  6 +-
 recaptcha_classifier/data/__init__.py     |  2 -
 recaptcha_classifier/data/augment.py      | 31 +++-----
 recaptcha_classifier/data/dataset.py      |  6 --
 recaptcha_classifier/data/downloader.py   | 70 +++++++++++++-----
 recaptcha_classifier/data/pipeline.py     |  2 +-
 recaptcha_classifier/data/preprocessor.py | 23 ------
 recaptcha_classifier/data/scaler.py       | 90 -----------------------
 8 files changed, 65 insertions(+), 165 deletions(-)
 delete mode 100644 recaptcha_classifier/data/scaler.py

diff --git a/main.py b/main.py
index 48501a3fe8..40a717dcdf 100644
--- a/main.py
+++ b/main.py
@@ -10,8 +10,8 @@ def main():
         balance=True
         )
 
-    loaders = pipeline.run()
-
+    loaders = pipeline._downloader.download()
+    """
     print("Data loaders built successfully.")
 
     for split, loader in loaders.items():
@@ -21,7 +21,7 @@ def main():
         print(f" - images.shape: {images.shape}")
         print(f" - labels.shape: {labels.shape}")
         print(f" - class IDs: {labels.tolist()}")
-
+    """
 
 if __name__ == '__main__':
     main()
diff --git a/recaptcha_classifier/data/__init__.py b/recaptcha_classifier/data/__init__.py
index c7d918d6f9..7fdb9a5551 100644
--- a/recaptcha_classifier/data/__init__.py
+++ b/recaptcha_classifier/data/__init__.py
@@ -11,7 +11,6 @@
     HorizontalFlip,
     RandomRotation
 )
-from .scaler import YOLOScaler
 
 from .collate_batch import collate_batch
 
@@ -36,7 +35,6 @@
     "AugmentationPipeline",
     "HorizontalFlip",
     "RandomRotation",
-    "YOLOScaler",
     # Methods
     "collate_batch",
     # Types
diff --git a/recaptcha_classifier/data/augment.py b/recaptcha_classifier/data/augment.py
index 8b622db68f..ca51e07e3a 100644
--- a/recaptcha_classifier/data/augment.py
+++ b/recaptcha_classifier/data/augment.py
@@ -2,8 +2,7 @@
 from abc import ABC, abstractmethod
 from PIL import Image
 from typing import List
-from .scaler import YOLOScaler
-from .types import LoadedImg, BBoxList
+from .types import LoadedImg
 
 
 class Augmentation(ABC):
@@ -33,15 +32,13 @@ def __init__(self, transforms=[]) -> None:
         self._transforms: List[Augmentation] = transforms
 
     def apply_transforms(self,
-                         image: Image.Image,
-                         annotations: BBoxList) -> LoadedImg:
+                         image: Image.Image) -> LoadedImg:
         """
         Apply all transformations in the pipeline to the image and
         annotations.
 
         Args:
             image (Image.Image): The image to be augmented.
-            annotations (BBoxList): List of annotations
             associated with the image.
 
         Returns:
@@ -51,43 +48,33 @@ def apply_transforms(self,
         for transform in self._transforms:
             if hasattr(transform, 'prob') and random.random() > transform.prob:
                 continue
-            image, annotations = transform.augment(image, annotations)
+            image, annotations = transform.augment(image)
         return image, annotations
 
 
 class HorizontalFlip(Augmentation):
-    """Flips the image horizontally, with probability p and updates bboxes."""
+    """Flips the image horizontally, with probability p."""
     def __init__(self, p: float = 0.5) -> None:
         self.prob = p
 
     def augment(self,
-                image: Image.Image,
-                annotations: BBoxList) -> LoadedImg:
+                image: Image.Image) -> LoadedImg:
         flipped = image.transpose(Image.FLIP_LEFT_RIGHT)
-        new_annotations = YOLOScaler.scale_for_flip(annotations)
 
-        return flipped, new_annotations
+        return flipped
 
 
 class RandomRotation(Augmentation):
     """
-    Rotates the image by a random angle,
-    also updates bboxes to reflect the rotation.
+    Rotates the image by a random angle.
     """
     def __init__(self, degrees: float = 30.0, p: float = 0.5) -> None:
         self._degrees = degrees
         self.prob = p
 
     def augment(self,
-                image: Image.Image,
-                annotations: BBoxList) -> LoadedImg:
+                image: Image.Image) -> LoadedImg:
         angle = random.uniform(-self._degrees, self._degrees)
 
         rotated = image.rotate(angle)
-        new_annotations = (YOLOScaler
-                           .scale_for_rotation(annotations,
-                                               angle,
-                                               image.size)
-                           )
-
-        return rotated, new_annotations
+        return rotated
diff --git a/recaptcha_classifier/data/dataset.py b/recaptcha_classifier/data/dataset.py
index f14ad87f0b..8adda5a9a8 100644
--- a/recaptcha_classifier/data/dataset.py
+++ b/recaptcha_classifier/data/dataset.py
@@ -52,10 +52,6 @@ def __getitem__(self,
 
         # Load image and label
         img = self._prep.load_image(img_path)
-        # bb = self._prep.load_labels(lbl_path)
-
-        # if not bb:
-        #    raise ValueError(f"Bounding box list is empty for {lbl_path}")
 
         # Apply augmentation if passed
         if self._aug:
@@ -71,6 +67,4 @@ def __getitem__(self,
             raise KeyError(f"Class name '{c_name}' not found in classes.")
         c_id = self._class_map[c_name]
 
-        # Return image tensor, bounding box and class index
         return tensor, self._prep.class_id_to_tensor(c_id)
-        # return tensor, bb, c_id
diff --git a/recaptcha_classifier/data/downloader.py b/recaptcha_classifier/data/downloader.py
index e03c87561c..1e597b6b7e 100644
--- a/recaptcha_classifier/data/downloader.py
+++ b/recaptcha_classifier/data/downloader.py
@@ -3,6 +3,7 @@
 import shutil
 from pathlib import Path
 from alive_progress import alive_bar
+from recaptcha_classifier.detection_labels import DetectionLabels
 
 
 class DatasetDownloader:
@@ -28,6 +29,7 @@ def __init__(self,
         self._dest: Path = Path(dest)
         self._zip_path: Path = self._dest / "dataset.zip"
         self._progress = alive_bar
+        self._expected_folder_names = DetectionLabels.dataset_classnames()
 
     def download(self) -> None:
         """
@@ -41,16 +43,25 @@ def download(self) -> None:
         self._prepare_dest()
         print(f"Downloading {self._url} to {self._dest}...")
         self._download_zip()
-        self._unzip_and_cleanup()
+        self._extract_zip()
+        self._move_subfolders()
+        self._delete_labels()
+        self._flatten_images_folder()
+        self._zip_path.unlink()
+        print("Download and extraction completed successfully.")
 
     def _is_downloaded(self) -> bool:
         """
-        Checks if the dataset is already downloaded.
+        Checks if the dataset is already downloaded and in the expected format.
 
         Returns:
             bool: True if the dataset is already downloaded, False otherwise.
         """
-        return self._dest.exists() and any(self._dest.iterdir())
+        if not self._dest.exists():
+            return False
+        folders = {p.name for p in self._dest.iterdir() if p.is_dir()}
+        expected = set(self._expected_folder_names)
+        return expected.issubset(folders)
 
     def _prepare_dest(self) -> None:
         """
@@ -80,29 +91,52 @@ def _download_zip(self) -> None:
             for chunk in resp.iter_content(chunk_size=8192):
                 f.write(chunk)
                 bar(len(chunk))
-        print("Download completed successfully.")
-
-    def _unzip_and_cleanup(self) -> None:
+    
+    def _extract_zip(self) -> None:
         """
-        Unzips the downloaded dataset.
-        Then it cleans up the zip file and its main extracted directory.
-
-        Note: this assumes that the downloaded dataset has exactly
-        the structure of our selected Kaggle dataset for simplicity.
+        Extracts the downloaded zip file to the destination directory.
         """
-        print("Extracting...")
         with zipfile.ZipFile(self._zip_path) as z:
             z.extractall(self._dest)
 
-        root = next(p for p in self._dest.iterdir() if p.is_dir())
+    def _move_subfolders(self) -> None:
+        """
+        Moves the main images and labels subfolders
+        from the extracted directory to the destination directory.
+        Finally, it removes the main extracted directory that is now empty.
+        """
+        root = next(p for p in self._dest.iterdir() if p.is_dir() and p.name not in self._expected_folder_names)
+        
         for sub in ("images", "labels"):
-            (root / sub).rename(self._dest / sub)
-
-        root.rmdir()
+            source = root / sub
+            if source.exists():
+                target = self._dest / sub
+                if target.exists():
+                    shutil.rmtree(target)
+                source.rename(target)
         
+        if root.exists() and root.is_dir():
+            root.rmdir()
+    
+    def _delete_labels(self) -> None:
+        """
+        Deletes the labels directory if it exists.
+        """
         label_dir = self._dest / "labels"
         if label_dir.exists() and label_dir.is_dir():
             shutil.rmtree(label_dir)
+    
+    def _flatten_images_folder(self) -> None:
+        images_dir = self._dest / "images"
+        if not images_dir.exists():
+            return
+
+        for subfolder in images_dir.iterdir():
+            if subfolder.is_dir():
+                target_path = self._dest / subfolder.name
+                if target_path.exists():
+                    shutil.rmtree(target_path)
+                subfolder.rename(target_path)
         
-        self._zip_path.unlink()
-        print("Extraction and cleanup completed successfully.")
\ No newline at end of file
+        if not any(images_dir.iterdir()):
+            images_dir.rmdir()
diff --git a/recaptcha_classifier/data/pipeline.py b/recaptcha_classifier/data/pipeline.py
index a2949c67c6..961e44a513 100644
--- a/recaptcha_classifier/data/pipeline.py
+++ b/recaptcha_classifier/data/pipeline.py
@@ -114,4 +114,4 @@ def run(self) -> Dict[str, DataLoader]:
         # 4. Create DataLoaders for each split
         print("e. Creating DataLoaders for each split...")
         loaders = self._creator.create_loaders(splits)
-        return loaders
+        return loaders
\ No newline at end of file
diff --git a/recaptcha_classifier/data/preprocessor.py b/recaptcha_classifier/data/preprocessor.py
index c171503a2e..51cd4dd0ea 100644
--- a/recaptcha_classifier/data/preprocessor.py
+++ b/recaptcha_classifier/data/preprocessor.py
@@ -3,7 +3,6 @@
 from PIL import Image
 import numpy as np
 import torch
-from .types import BBoxList
 
 
 class ImagePrep:
@@ -39,28 +38,6 @@ def load_image(self, img_path: Path) -> Image.Image:
         loaded = Image.open(img_path).convert("RGB")
         return self._resize(loaded)
 
-    def load_labels(self, lbl_path: Path) -> BBoxList:
-        """
-        Parses the list of YOLO format labels from the file at the given path.
-
-        Args:
-            lbl_path (Path): Path to the label file.
-
-        Returns:
-            BBoxList: List of bounding boxes in YOLO format
-            (x_center, y_center, width, height).
-        """
-        bounding_boxes = []
-        with open(lbl_path, "r") as f:
-            for line in f:
-                parts = line.strip().split()
-                if len(parts) < 5:
-                    continue  # invalid line skipped
-                _, x_center, y_center, width, height = map(float, parts)
-                bounding_boxes.append((x_center, y_center, width, height))
-
-        return bounding_boxes
-
     def _resize(self, img: Image.Image) -> Image.Image:
         """
         Resizes the image to the target size.
diff --git a/recaptcha_classifier/data/scaler.py b/recaptcha_classifier/data/scaler.py
deleted file mode 100644
index f35a303125..0000000000
--- a/recaptcha_classifier/data/scaler.py
+++ /dev/null
@@ -1,90 +0,0 @@
-from typing import Tuple
-from .types import BBoxList
-
-
-class YOLOScaler:
-    """
-    This class is responsible for scaling the YOLO format bounding boxes
-    based on the transform applied to the image. It is only
-    used inside the AugmentationPipeline class, to adjust
-    the coordinates of the bounding boxes after applying
-    transformations to the image.
-    """
-    @staticmethod
-    def scale_for_flip(bboxes: BBoxList) -> BBoxList:
-        """
-        Adjusts the bounding boxes for horizontal flip.
-
-        Args:
-            bboxes (BBoxList): List of bounding boxes in YOLO format
-            (x_center, y_center, width, height).
-
-        Returns:
-            BBoxList: List of scaled bounding boxes.
-        """
-        return [(1 - x, y, w, h) for (x, y, w, h) in bboxes]
-
-    @staticmethod
-    def scale_for_rotation(bboxes: BBoxList,
-                           angle: float,
-                           size: Tuple[int, int]) -> BBoxList:
-        """
-        Adjusts the bounding boxes for rotation.
-
-        Args:
-            bboxes (BBoxList): List of bounding boxes in YOLO format
-            (x_center, y_center, width, height).
-            angle (float): Angle of rotation.
-            size (Tuple[int, int]): Size of the image. (width, height)
-
-        Returns:
-            BBoxList: List of scaled bounding boxes.
-        """
-        import math
-        width, height = size
-        angle_rad = math.radians(angle)
-        c_x, c_y = width / 2, height / 2  # center coordinates of the image
-
-        n_ann = []  # the new bounding boxes, that we will return
-
-        for x, y, w, h in bboxes:
-            # calculate pixel coordinates from bb
-            x0, y0 = x * width, y * height
-            bw, bh = w * width, h * height
-
-            # calculate corner coordinates
-            corners = [
-                (x0 - bw / 2, y0 - bh / 2),
-                (x0 + bw / 2, y0 - bh / 2),
-                (x0 + bw / 2, y0 + bh / 2),
-                (x0 - bw / 2, y0 + bh / 2)
-            ]
-
-            # rotate the corners
-            new_corners = []
-            for cx, cy in corners:
-                # rotate the corners around the center of the image
-                # formula at https://en.wikipedia.org/wiki/Rotation_matrix
-                x_rot = (math.cos(angle_rad) * (cx - c_x) -
-                         math.sin(angle_rad) * (cy - c_y) +
-                         c_x)
-                y_rot = (math.sin(angle_rad) * (cx - c_x) +
-                         math.cos(angle_rad) * (cy - c_y) +
-                         c_y)
-
-                new_corners.append((x_rot, y_rot))
-
-            # calculate new bounding box
-            x_min = min(c[0] for c in new_corners)
-            x_max = max(c[0] for c in new_corners)
-            y_min = min(c[1] for c in new_corners)
-            y_max = max(c[1] for c in new_corners)
-
-            new_x = max(0, min(1, (x_min + x_max) / (2 * width)))
-            new_y = max(0, min(1, (y_min + y_max) / (2 * height)))
-            new_w = max(0, min(1, (x_max - x_min) / width))
-            new_h = max(0, min(1, (y_max - y_min) / height))
-
-            n_ann.append((new_x, new_y, new_w, new_h))
-
-        return n_ann

From 6f136c6e2ba6c41e07656d4e649ebaa1f3ccb901 Mon Sep 17 00:00:00 2001
From: Sinan <sinan_ceviker@yahoo.com>
Date: Sun, 25 May 2025 00:01:21 +0200
Subject: [PATCH 03/32] polished some more types and docs, given the uodates

---
 main.py                                   |   5 +-
 recaptcha_classifier/data/__init__.py     |   4 +-
 recaptcha_classifier/data/downloader.py   |   5 +-
 recaptcha_classifier/data/pair_loader.py  | 121 ----------------------
 recaptcha_classifier/data/paths_loader.py |  93 +++++++++++++++++
 recaptcha_classifier/data/pipeline.py     |  23 ++--
 recaptcha_classifier/data/splitter.py     |   3 +-
 recaptcha_classifier/data/visualizer.py   |  44 ++------
 recaptcha_classifier/detection_labels.py  |   2 +-
 tests/data/notyet-test_pair_loader.py     |  16 +--
 10 files changed, 130 insertions(+), 186 deletions(-)
 delete mode 100644 recaptcha_classifier/data/pair_loader.py
 create mode 100644 recaptcha_classifier/data/paths_loader.py

diff --git a/main.py b/main.py
index 40a717dcdf..e00783860d 100644
--- a/main.py
+++ b/main.py
@@ -6,11 +6,10 @@
 
 def main():
     pipeline = DataPreprocessingPipeline(
-        DetectionLabels.to_class_map(),
+        DetectionLabels,
         balance=True
         )
-
-    loaders = pipeline._downloader.download()
+    loaders = pipeline.run()
     """
     print("Data loaders built successfully.")
 
diff --git a/recaptcha_classifier/data/__init__.py b/recaptcha_classifier/data/__init__.py
index 7fdb9a5551..b4dde44d2b 100644
--- a/recaptcha_classifier/data/__init__.py
+++ b/recaptcha_classifier/data/__init__.py
@@ -1,6 +1,6 @@
 from .pipeline import DataPreprocessingPipeline
 from .downloader import DatasetDownloader
-from .pair_loader import ImageLabelLoader
+from .paths_loader import ImagePathsLoader
 from .splitter import DataSplitter
 from .visualizer import Visualizer
 from .loader_factory import LoaderFactory
@@ -26,7 +26,7 @@
     # Classes
     "DataPreprocessingPipeline",
     "DatasetDownloader",
-    "ImageLabelLoader",
+    "ImagePathsLoader",
     "DataSplitter",
     "Visualizer",
     "LoaderFactory",
diff --git a/recaptcha_classifier/data/downloader.py b/recaptcha_classifier/data/downloader.py
index 1e597b6b7e..219c7f25f0 100644
--- a/recaptcha_classifier/data/downloader.py
+++ b/recaptcha_classifier/data/downloader.py
@@ -2,8 +2,8 @@
 import zipfile
 import shutil
 from pathlib import Path
+from typing import List
 from alive_progress import alive_bar
-from recaptcha_classifier.detection_labels import DetectionLabels
 
 
 class DatasetDownloader:
@@ -15,6 +15,7 @@ class DatasetDownloader:
     dataset downloading operation, with no other responsibilities.
     """
     def __init__(self,
+                 class_names: List[str],
                  url: str = ("https://www.kaggle.com/api/v1/datasets/"
                              "download/mikhailma/test-dataset"),
                  dest: str = "data") -> None:
@@ -29,7 +30,7 @@ def __init__(self,
         self._dest: Path = Path(dest)
         self._zip_path: Path = self._dest / "dataset.zip"
         self._progress = alive_bar
-        self._expected_folder_names = DetectionLabels.dataset_classnames()
+        self._expected_folder_names = class_names
 
     def download(self) -> None:
         """
diff --git a/recaptcha_classifier/data/pair_loader.py b/recaptcha_classifier/data/pair_loader.py
deleted file mode 100644
index 828727c5d0..0000000000
--- a/recaptcha_classifier/data/pair_loader.py
+++ /dev/null
@@ -1,121 +0,0 @@
-import logging
-from pathlib import Path
-from typing import List, Dict
-from .types import ClassToImgPaths
-
-logger = logging.getLogger(__name__)
-
-
-class ImageLabelLoader:
-    """
-    This class loads all image-label pairs for given classes.
-
-    It scans for all matching images and labels and caches the result.
-
-    It follows Single Responsibility Principle (SRP) as it only handles
-    the loading of the pairs. Also, it uses the Iterator pattern, as
-    it can be looped over to get the list pairs as tuples by class,
-    in format (class, [(img_path, lbl_path), ...].)
-    """
-    def __init__(self,
-                 classes: List[str],
-                 images_dir: str = "data/images",
-                 # labels_dir: str = "data/labels"
-                 ) -> None:
-        """
-        Initializes the PairsLoader instance.
-
-        Args:
-            classes (List[str]): List of class names to load.
-            images_dir (str): Path to the directory containing images.
-            labels_dir (str): Path to the directory containing labels.
-        """
-        self._classes = classes
-        self._images_dir = Path(images_dir)
-        # self._labels_dir = Path(labels_dir)
-        self._pairs: ClassToImgPaths = dict()
-
-    def find_pairs(self) -> ClassToImgPaths:
-        """
-        Returns all pairs loaded for the given classes.
-        It caches the response after first run.
-
-        Returns:
-            List[Tuple[Path, Path]]: List of tuples
-            containing image and label paths. (img_path, lbl_path)
-        """
-        if not self._pairs:
-            self._load_pairs()
-        return self._pairs
-
-    def __iter__(self):
-        """
-        Iterates over classes and their respective list of pairs.
-
-        Yields:
-            Tuple[str, List[Tuple[Path, Path]]]: Class name and list of
-            tuples containing image and label paths.
-        """
-        for cls, pairs in self.find_pairs().items():
-            yield cls, pairs
-
-    def __len__(self) -> int:
-        """
-        Returns total number of matched pairs.
-
-        Returns:
-            int: Number of matched pairs.
-        """
-        return sum(len(pairs) for pairs in self.find_pairs().values())
-
-    def class_count(self) -> Dict[str, int]:
-        """
-        Returns a dictionary with the count of pairs for each class.
-        The keys are class names and the values are the counts.
-
-        Returns:
-            Dict[str, int]: Dictionary with class names as keys and
-            counts as values.
-        """
-        return {cls: len(pairs) for cls, pairs in self.find_pairs().items()}
-
-    def _load_pairs(self) -> None:
-        """
-        Private method to scan directories of given classes and match all
-        available image-label pairs.
-        It ignores images with missing labels.
-        """
-        total_count = 0
-        for cls in self._classes:
-            img_dir = self._images_dir / cls
-            # lbl_dir = self._labels_dir / cls
-            # skipped, cls_count = 0, 0
-
-            if not Path.is_dir(img_dir):  # or not Path.exists(lbl_dir)
-                logger.info(f"Warning: Missing folder for {cls}. Skipping.")
-                continue
-
-            self._pairs[cls] = list(img_dir.glob("*.png"))  # []
-            N = len(self._pairs[cls])
-            logger.info(f"Found {N} images in {cls}.")
-            total_count += N
-            """
-            for img_path in img_dir.glob("*.png"):
-                lbl_path = lbl_dir / img_path.name.replace(".png", ".txt")
-                cls_count += 1
-
-                if not Path.exists(lbl_path):
-                    skipped += 1
-                    continue
-
-                self._pairs[cls].append((img_path, lbl_path))
-
-
-            print(f"Loaded {cls_count - skipped} image-label pairs for {cls}.")
-            if skipped > 0:
-                print(f"Warning: {skipped} missing labels in {cls}. Skipped.")
-
-            total_count += cls_count - skipped
-            """
-
-        print(f"Total pairs loaded: {total_count}")
diff --git a/recaptcha_classifier/data/paths_loader.py b/recaptcha_classifier/data/paths_loader.py
new file mode 100644
index 0000000000..712e09ad90
--- /dev/null
+++ b/recaptcha_classifier/data/paths_loader.py
@@ -0,0 +1,93 @@
+from pathlib import Path
+from typing import List, Dict
+from .types import ClassToImgPaths
+
+
+class ImagePathsLoader:
+    """
+    This class loads all image paths for given classes.
+
+    It scans for all matching images and caches the result.
+
+    It follows Single Responsibility Principle (SRP) as it only handles
+    the loading of the image paths. Also, it uses the Iterator pattern, as
+    it can be looped over to get the list paths as tuples by class,
+    in format (class, [img_path1, ...]).
+    """
+    def __init__(self,
+                 classes: List[str],
+                 images_dir: str = "data") -> None:
+        """
+        Initializes the ImagePathsLoader instance.
+
+        Args:
+            classes (List[str]): List of class names to load.
+            images_dir (str): Path to the directory containing images.
+        """
+        self._classes = classes
+        self._images_dir = Path(images_dir)
+        self._paths: ClassToImgPaths = dict()
+
+    def find_image_paths(self) -> ClassToImgPaths:
+        """
+        Returns all image paths loaded for the given classes.
+        It caches the response after first run.
+
+        Returns:
+            ClassToImgPaths: Dictionary mapping class names to lists
+            of image paths.
+        """
+        if not self._paths:
+            self._load_pairs()
+        return self._paths
+
+    def __iter__(self):
+        """
+        Iterates over classes and their respective list of pairs.
+
+        Yields:
+            Tuple[str, List[Path]]: Class name and list of
+            image paths.
+        """
+        for cls, pairs in self.find_image_paths().items():
+            yield cls, pairs
+
+    def __len__(self) -> int:
+        """
+        Returns total number of matched pairs.
+
+        Returns:
+            int: Number of matched pairs.
+        """
+        return sum(len(pairs) for pairs in self.find_image_paths().values())
+
+    def class_count(self) -> Dict[str, int]:
+        """
+        Returns a dictionary with the count of pairs for each class.
+        The keys are class names and the values are the counts.
+
+        Returns:
+            Dict[str, int]: Dictionary with class names as keys and
+            counts as values.
+        """
+        return {cls: len(pairs) for cls, pairs in self.find_image_paths().items()}
+
+    def _load_pairs(self) -> None:
+        """
+        Private method to scan directories of given classes and match all
+        available image paths.
+        """
+        total_count = 0
+        for cls in self._classes:
+            img_dir = self._images_dir / cls
+
+            if not Path.is_dir(img_dir):
+                print(f"Warning: Missing folder for {cls}. Skipping.")
+                continue
+
+            self._paths[cls] = sorted(img_dir.glob("*.png"))
+            N = len(self._paths[cls])
+            print(f"Found {N} images in {cls}.")
+            total_count += N
+
+        print(f"Total image paths found: {total_count}")
diff --git a/recaptcha_classifier/data/pipeline.py b/recaptcha_classifier/data/pipeline.py
index 961e44a513..b9082fec72 100644
--- a/recaptcha_classifier/data/pipeline.py
+++ b/recaptcha_classifier/data/pipeline.py
@@ -1,8 +1,8 @@
 from typing import Dict, Tuple
 from torch.utils.data import DataLoader
-
+from enum import EnumMeta
 from .downloader import DatasetDownloader
-from .pair_loader import ImageLabelLoader
+from .paths_loader import ImagePathsLoader
 from .splitter import DataSplitter
 from .visualizer import Visualizer
 from .preprocessor import ImagePrep
@@ -33,7 +33,7 @@ class DataPreprocessingPipeline:
     pipeline and it includes all its components.
     """
     def __init__(self,
-                 class_map: Dict[str, int],
+                 class_enum: EnumMeta,
                  ratios: Tuple[float, float, float] = (0.7, 0.2, 0.1),
                  seed: int = 23,  # our group number
                  batch_size: int = 32,
@@ -44,7 +44,7 @@ def __init__(self,
         Initializes the DataPreprocessingPipeline with the given parameters.
 
         Args:
-            class_map (Dict[str, int]): Mappng of class names to indices.
+            class_enum (EnumMeta): Enum class containing dataset classes.
             ratios (Tuple[float, float, float]): Ratios for train,
             val, and test splits.
             seed (int): Random seed for reproducibility.
@@ -53,14 +53,16 @@ def __init__(self,
             balance (bool): Whether to balance the dataset.
             show_plots (bool): Whether to show plots.
         """
-        self._downloader = DatasetDownloader()
-        self._loader = ImageLabelLoader(list(class_map.keys()))
+        self._class_enum = class_enum
+        self._downloader = DatasetDownloader(self._class_enum
+                                             .dataset_classnames())
+        self._loader = ImagePathsLoader(self._class_enum.dataset_classnames())
         self._splitter = DataSplitter(ratios, seed=seed)
         self._show_plots = show_plots
         self._preproc = ImagePrep()
         self._augment = self._build_augmentator()
         self._creator = LoaderFactory(
-            class_map=class_map,
+            class_map=self._class_enum.to_class_map(),
             preprocessor=self._preproc,
             augmentator=self._augment,
             batch_size=batch_size,
@@ -95,7 +97,7 @@ def run(self) -> Dict[str, DataLoader]:
 
         # 2. Finds all pairs of images and YOLO annotations from the dataset
         print("b. Searching for all the data...")
-        pairs_by_class = self._loader.find_pairs()
+        pairs_by_class = self._loader.find_image_paths()
 
         # 3a. Splits the data into train, val, and test sets
         print("c. Splitting the data...")
@@ -113,5 +115,6 @@ def run(self) -> Dict[str, DataLoader]:
 
         # 4. Create DataLoaders for each split
         print("e. Creating DataLoaders for each split...")
-        loaders = self._creator.create_loaders(splits)
-        return loaders
\ No newline at end of file
+        #loaders = self._creator.create_loaders(splits)
+        #return loaders
+        return []
\ No newline at end of file
diff --git a/recaptcha_classifier/data/splitter.py b/recaptcha_classifier/data/splitter.py
index 31416a9274..dcba9fd3b3 100644
--- a/recaptcha_classifier/data/splitter.py
+++ b/recaptcha_classifier/data/splitter.py
@@ -15,7 +15,8 @@ class DataSplitter:
     def __init__(self,
                  ratios: Tuple[float, float, float] = (0.7, 0.2, 0.1),
                  shuffle: bool = True,
-                 seed: int = None) -> None:
+                 seed: int = 23 # our group number
+                 ) -> None:
         """
         Args:
             ratios (Tuple[float, float, float]): Ratios for
diff --git a/recaptcha_classifier/data/visualizer.py b/recaptcha_classifier/data/visualizer.py
index b0315c9dea..6a95de7c33 100644
--- a/recaptcha_classifier/data/visualizer.py
+++ b/recaptcha_classifier/data/visualizer.py
@@ -16,12 +16,12 @@ def print_counts(cls, splits: DatasetSplitMap) -> None:
 
         Args:
             splits (DatasetSplitMap): the dataset splits
-                containing the pairs for each class.
+                containing the items for each class.
         """
         for split, cls_dict in splits.items():
             print(f"{split.upper()}:")
-            for cls, pairs in cls_dict.items():
-                print(f"  {cls:5s}: {len(pairs)}")
+            for cls, items in cls_dict.items():
+                print(f"  {cls:5s}: {len(items)}")
             print()
 
     @classmethod
@@ -34,7 +34,7 @@ def plot_splits(cls,
 
         Args:
             splits (DatasetSplitMap): the dataset splits
-                containing the pairs for each class.
+                containing the items for each class.
             title (str): the title of the plot.
         """
         classes = list(splits['train'].keys())
@@ -51,40 +51,8 @@ def plot_splits(cls,
         # total for each class, simply adding counts of each split
         totals = [t+v+te for t, v, te in
                   zip(counts_train, counts_val, counts_test)]
-
-        """
-        # drawing bars
-        train_bars = plt.bar([i - width for i in x],
-                             counts_train,
-                             width,
-                             label='Train')
-        val_bars = plt.bar(x, counts_val, width, label='Val')
-        test_bars = plt.bar([i + width for i in x],
-                            counts_test,
-                            width,
-                            label='Test')
-
-        # adding perc labels on top of each of the bar
-        for bars, counts in [(train_bars, counts_train),
-                             (val_bars, counts_val),
-                             (test_bars, counts_test)]:
-            for bar, count, total in zip(bars, counts, totals):
-                perc = count / total * 100
-                plt.text(
-                    bar.get_x() + bar.get_width() / 2,  # middle of the bar,
-                    bar.get_height(),  # on top of the bar,
-                    f'{perc:.1f}%',  # percentage, formatted to 1 decimal
-                    ha='center', va='bottom'
-                )
-
-        plt.xticks(x, classes)
-        plt.ylabel('No. of Samples')
-        plt.title(title)
-        plt.legend()
-        plt.tight_layout()
-        plt.show()
-        """
-        fig, ax = plt.subplots(figsize=(num_classes, 6))
+        
+        _, ax = plt.subplots(figsize=(num_classes, 6))
         bar1 = ax.bar(x - width, counts_train, width, label='Train')
         bar2 = ax.bar(x, counts_val, width, label='Val')
         bar3 = ax.bar(x + width, counts_test, width, label='Test')
diff --git a/recaptcha_classifier/detection_labels.py b/recaptcha_classifier/detection_labels.py
index 2ca79204d7..1cbb610f0a 100644
--- a/recaptcha_classifier/detection_labels.py
+++ b/recaptcha_classifier/detection_labels.py
@@ -83,5 +83,5 @@ def dataset_classnames(cls) -> list:
         Returns:
             list: List of class names.
         """
-        return [name.capitalize().replace("_", " ")
+        return [name.replace("_", " ").title()
                 for name in cls.__members__.keys()]
diff --git a/tests/data/notyet-test_pair_loader.py b/tests/data/notyet-test_pair_loader.py
index 987cdf3af3..e7ae89a983 100644
--- a/tests/data/notyet-test_pair_loader.py
+++ b/tests/data/notyet-test_pair_loader.py
@@ -2,10 +2,10 @@
 from pathlib import Path
 from unittest.mock import patch
 
-from recaptcha_classifier.data.pair_loader import ImageLabelLoader
+from recaptcha_classifier.data.pair_loader import ImagePathsLoader
 
 
-class TestImageLabelLoader(unittest.TestCase):
+class TestImagePathsLoader(unittest.TestCase):
     @patch("recaptcha_classifier.data.pair_loader.Path.glob")
     @patch("recaptcha_classifier.data.pair_loader.Path.exists",
            return_value=True)
@@ -20,8 +20,8 @@ def test_load_pairs_with_all_labels(self,
             Path("data/images/class1/img2.png"),
         ]  # 2 images found in the png glob
 
-        loader = ImageLabelLoader(["class1"])
-        pairs = loader.find_pairs()
+        loader = ImagePathsLoader(["class1"])
+        pairs = loader.find_image_paths()
 
         expected_pairs = {
             (Path("data/images/class1/img1.png"),
@@ -49,8 +49,8 @@ def test_load_pairs_with_missing_labels(self,
         # label folder exists, img1.txt exists but img2.txt does not
         exists_mock.side_effect = [True, True, False]
 
-        loader = ImageLabelLoader(["class1"])
-        pairs = loader.find_pairs()
+        loader = ImagePathsLoader(["class1"])
+        pairs = loader.find_image_paths()
 
         self.assertIn("class1", pairs)
         expected_pair = {
@@ -65,14 +65,14 @@ def test_load_pairs_with_missing_labels(self,
     @patch("recaptcha_classifier.data.pair_loader.Path.exists")
     @patch("recaptcha_classifier.data.pair_loader.Path.is_dir")
     def test_caching(self, is_dir_mock, exists_mock, glob_mock):
-        loader = ImageLabelLoader(["class1"])
+        loader = ImagePathsLoader(["class1"])
         loader._pairs = {"test": [(Path("test.png"), Path("test.txt"))]}
 
         # if any mocked method is called, then the cache is not used
         for method in (is_dir_mock, exists_mock, glob_mock):
             method.side_effect = AssertionError(f"{method} called, error!")
 
-        pairs = loader.find_pairs()
+        pairs = loader.find_image_paths()
 
         self.assertIs(pairs, loader._pairs)
         self.assertEqual(pairs, {"test": [(Path("test.png"),

From 8a953d2e11119b718d75c87c5cf5971ffed88086 Mon Sep 17 00:00:00 2001
From: Sinan <sinan_ceviker@yahoo.com>
Date: Sun, 25 May 2025 00:34:27 +0200
Subject: [PATCH 04/32] started redoing tests

---
 main.py                                       |  5 ++--
 recaptcha_classifier/data/augment.py          | 20 +++++++---------
 recaptcha_classifier/data/dataset.py          | 16 ++++++-------
 recaptcha_classifier/data/loader_factory.py   | 24 +++++++++----------
 recaptcha_classifier/data/pipeline.py         |  7 +++---
 recaptcha_classifier/detection_labels.py      |  2 +-
 ...notyet-test_augment.py => test_augment.py} | 16 +++----------
 ...notyet-test_dataset.py => test_dataset.py} | 18 ++++----------
 ...ader_factory.py => test_loader_factory.py} |  0
 ...est_pair_loader.py => test_pair_loader.py} |  0
 ...t_preprocessor.py => test_preprocessor.py} |  0
 .../{notyet-test_scaler.py => test_scaler.py} |  0
 ...tyet-test_splitter.py => test_splitter.py} |  0
 ...-test_visualizer.py => test_visualizer.py} |  0
 14 files changed, 42 insertions(+), 66 deletions(-)
 rename tests/data/{notyet-test_augment.py => test_augment.py} (66%)
 rename tests/data/{notyet-test_dataset.py => test_dataset.py} (71%)
 rename tests/data/{notyet-test_loader_factory.py => test_loader_factory.py} (100%)
 rename tests/data/{notyet-test_pair_loader.py => test_pair_loader.py} (100%)
 rename tests/data/{notyet-test_preprocessor.py => test_preprocessor.py} (100%)
 rename tests/data/{notyet-test_scaler.py => test_scaler.py} (100%)
 rename tests/data/{notyet-test_splitter.py => test_splitter.py} (100%)
 rename tests/data/{notyet-test_visualizer.py => test_visualizer.py} (100%)

diff --git a/main.py b/main.py
index e00783860d..c06ce2ac04 100644
--- a/main.py
+++ b/main.py
@@ -9,8 +9,9 @@ def main():
         DetectionLabels,
         balance=True
         )
+
     loaders = pipeline.run()
-    """
+    
     print("Data loaders built successfully.")
 
     for split, loader in loaders.items():
@@ -20,7 +21,7 @@ def main():
         print(f" - images.shape: {images.shape}")
         print(f" - labels.shape: {labels.shape}")
         print(f" - class IDs: {labels.tolist()}")
-    """
+
 
 if __name__ == '__main__':
     main()
diff --git a/recaptcha_classifier/data/augment.py b/recaptcha_classifier/data/augment.py
index ca51e07e3a..1dcef38bbf 100644
--- a/recaptcha_classifier/data/augment.py
+++ b/recaptcha_classifier/data/augment.py
@@ -9,14 +9,14 @@ class Augmentation(ABC):
     """Abstract class for data augmentation."""
     @abstractmethod
     def augment(self,
-                image: Image.Image,
+                image: LoadedImg,
                 annotations: List) -> LoadedImg:
         """
         Apply the transformation of the image and updates the bounding boxes
         if necessary.
 
         Args:
-            image (Image.Image): The image to be augmented.
+            image (LoadedImg): The image to be augmented.
             annotations (List): List of annotations associated with the image.
 
         Returns:
@@ -32,24 +32,22 @@ def __init__(self, transforms=[]) -> None:
         self._transforms: List[Augmentation] = transforms
 
     def apply_transforms(self,
-                         image: Image.Image) -> LoadedImg:
+                         image: LoadedImg) -> LoadedImg:
         """
         Apply all transformations in the pipeline to the image and
         annotations.
 
         Args:
-            image (Image.Image): The image to be augmented.
-            associated with the image.
+            image (LoadedImg): The image to be augmented.
 
         Returns:
-            LoadedImg: The augmented image and the updated
-            annotations.
+            LoadedImg: The augmented image.
         """
         for transform in self._transforms:
             if hasattr(transform, 'prob') and random.random() > transform.prob:
                 continue
-            image, annotations = transform.augment(image)
-        return image, annotations
+            image = transform.augment(image)
+        return image
 
 
 class HorizontalFlip(Augmentation):
@@ -58,7 +56,7 @@ def __init__(self, p: float = 0.5) -> None:
         self.prob = p
 
     def augment(self,
-                image: Image.Image) -> LoadedImg:
+                image: LoadedImg) -> LoadedImg:
         flipped = image.transpose(Image.FLIP_LEFT_RIGHT)
 
         return flipped
@@ -73,7 +71,7 @@ def __init__(self, degrees: float = 30.0, p: float = 0.5) -> None:
         self.prob = p
 
     def augment(self,
-                image: Image.Image) -> LoadedImg:
+                image: LoadedImg) -> LoadedImg:
         angle = random.uniform(-self._degrees, self._degrees)
 
         rotated = image.rotate(angle)
diff --git a/recaptcha_classifier/data/dataset.py b/recaptcha_classifier/data/dataset.py
index 8adda5a9a8..4d64cccfb7 100644
--- a/recaptcha_classifier/data/dataset.py
+++ b/recaptcha_classifier/data/dataset.py
@@ -7,7 +7,7 @@
 
 class ImageDataset(Dataset):
     """
-    A class to handle the pairs of (image, label) for the dataset.
+    A class to handle the images for the dataset.
     It makes them ready for training, by applying augmentation
     for the training set, preprocessing and makes sure that the
     output format is in PyTorch Tensor format.
@@ -17,7 +17,7 @@ class ImageDataset(Dataset):
     https://docs.pytorch.org/tutorials/beginner/basics/data_tutorial.html
     """
     def __init__(self,
-                 pairs: ImagePathList,
+                 items: ImagePathList,
                  preprocessor: ImagePrep,
                  augmentator: Optional[AugmentationPipeline] = None,
                  class_map: dict = {}
@@ -25,13 +25,13 @@ def __init__(self,
         """
         Initializes the ImageDataset with the given parameters.
         """
-        self._pairs = pairs
+        self._items = items
         self._prep = preprocessor
         self._aug = augmentator
         self._class_map = class_map
 
     def __len__(self) -> int:
-        return len(self._pairs)
+        return len(self._items)
 
     def __getitem__(self,
                     idx: int
@@ -47,16 +47,14 @@ def __getitem__(self,
             preprocessed image in tensor format, the YOLO bound
             box annotations and the label.
         """
-        # img_path, lbl_path = self._pairs[idx]
-        img_path = self._pairs[idx]
+        img_path = self._items[idx]
 
-        # Load image and label
+        # Load image
         img = self._prep.load_image(img_path)
 
         # Apply augmentation if passed
         if self._aug:
-            # img, bb = self._aug.apply_transforms(img, bb)
-            img, _ = self._aug.apply_transforms(img, [])
+            img = self._aug.apply_transforms(img)
 
         # Convert image to tensor
         tensor = self._prep.to_tensor(img)
diff --git a/recaptcha_classifier/data/loader_factory.py b/recaptcha_classifier/data/loader_factory.py
index caa452fbee..e5dc61e784 100644
--- a/recaptcha_classifier/data/loader_factory.py
+++ b/recaptcha_classifier/data/loader_factory.py
@@ -25,7 +25,7 @@ def __init__(self,
 
         Args:
             class_map (dict): A dictionary mapping class names to indices.
-            preprocessor (ImagePrep): The preprocessor to use.
+            preprocessor (ImagePrep): The preprocessor to use for data.
             augmentator (Optional[AugmentationPipeline]): The augmentator used
             batch_size (int): Batch size for DataLoader.
             num_workers (int): Number of workers for DataLoader.
@@ -35,34 +35,33 @@ def __init__(self,
         self._aug = augmentator
         self._batch_size = batch_size
         self._num_workers = num_workers
-        self._balance = balance  # do we use WeightedRandomSampler??
+        self._balance = balance 
         self._class_map = class_map
-        #  OPTIONAL: self._loaders to cache response
 
     def create_loaders(self,
                        splits: DatasetSplitMap) -> Dict[str, DataLoader]:
         loaders: Dict[str, DataLoader] = {}
 
         for split_name, cls_dict in splits.items():
-            # flatten nested dict of pairs
-            flat_pairs: ImagePathList = [pair
+            # flatten nested dict of image_paths
+            flat_image_paths: ImagePathList = [image_path
                                         # traversing over classes
-                                        for pairs in cls_dict.values()
-                                        # traversing over pairs
-                                        for pair in pairs
+                                        for image_paths in cls_dict.values()
+                                        # traversing over image_paths
+                                        for image_path in image_paths
                                         ]
 
             # augmentatr only for training set
             augmentator = self._aug if split_name == 'train' else None
 
             dataset = ImageDataset(
-                pairs=flat_pairs,
+                items=flat_image_paths,
                 preprocessor=self._preprocessor,
                 augmentator=augmentator,
                 class_map=self._class_map
             )
 
-            sampler = (self._build_sampler(flat_pairs) if self._balance
+            sampler = (self._build_sampler(flat_image_paths) if self._balance
                        and split_name == "train" else None)
 
             loader = DataLoader(
@@ -78,14 +77,13 @@ def create_loaders(self,
 
         return loaders
 
-    def _build_sampler(self, pairs):
+    def _build_sampler(self, image_paths):
         """
         Builds a sampler for the dataset to balance the classes.
         """
         class_counts = Counter()
         targets = []
-        # for img_path, _ in pairs:
-        for img_path in pairs:
+        for img_path in image_paths:
             cls = img_path.parent.name
             targets.append(self._class_map[cls])
             class_counts[self._class_map[cls]] += 1
diff --git a/recaptcha_classifier/data/pipeline.py b/recaptcha_classifier/data/pipeline.py
index b9082fec72..612328a8f2 100644
--- a/recaptcha_classifier/data/pipeline.py
+++ b/recaptcha_classifier/data/pipeline.py
@@ -39,7 +39,7 @@ def __init__(self,
                  batch_size: int = 32,
                  num_workers: int = 4,
                  balance: bool = False,
-                 show_plots: bool = True) -> None:
+                 show_plots: bool = False) -> None:
         """
         Initializes the DataPreprocessingPipeline with the given parameters.
 
@@ -115,6 +115,5 @@ def run(self) -> Dict[str, DataLoader]:
 
         # 4. Create DataLoaders for each split
         print("e. Creating DataLoaders for each split...")
-        #loaders = self._creator.create_loaders(splits)
-        #return loaders
-        return []
\ No newline at end of file
+        loaders = self._creator.create_loaders(splits)
+        return loaders
\ No newline at end of file
diff --git a/recaptcha_classifier/detection_labels.py b/recaptcha_classifier/detection_labels.py
index 1cbb610f0a..e4ae0311c4 100644
--- a/recaptcha_classifier/detection_labels.py
+++ b/recaptcha_classifier/detection_labels.py
@@ -53,7 +53,7 @@ def to_class_map(cls) -> dict:
         Returns:
             dict: Dictionary representation of the enum.
         """
-        return {cl.name.capitalize().replace("_", " "):
+        return {cl.name.replace("_", " ").title():
                 cl.value for cl in cls}
 
     @classmethod
diff --git a/tests/data/notyet-test_augment.py b/tests/data/test_augment.py
similarity index 66%
rename from tests/data/notyet-test_augment.py
rename to tests/data/test_augment.py
index b153bf66cd..667a2681c8 100644
--- a/tests/data/notyet-test_augment.py
+++ b/tests/data/test_augment.py
@@ -7,7 +7,6 @@
     HorizontalFlip,
     RandomRotation
 )
-from recaptcha_classifier.data.scaler import YOLOScaler
 
 
 class TestAugmentation(unittest.TestCase):
@@ -17,39 +16,30 @@ def setUp(self):
                     (1, 100, 3))
         )
         self.img = self.img.convert("RGB")
-        self.bb = [(0.5, 0.5, 0.2, 0.2)]
 
     def test_horizontal_flip(self):
         aug = HorizontalFlip(p=1.0)
-        flipped_img, flipped_bb = aug.augment(self.img, self.bb)
+        flipped_img = aug.augment(self.img)
 
         self.assertFalse(np.array_equal(np.array(flipped_img),
                                         np.array(self.img)))
 
-        result = YOLOScaler.scale_for_flip(self.bb)
-        self.assertEqual(flipped_bb, result)
-
     @patch('random.uniform', return_value=30)
     def test_random_rotation(self, _):
         augmenter = RandomRotation(degrees=30)
-        rotated_img, rotated_bb = augmenter.augment(self.img, self.bb)
+        rotated_img = augmenter.augment(self.img)
 
         self.assertFalse(np.array_equal(np.array(rotated_img),
                                         np.array(self.img)))
 
-        result = YOLOScaler.scale_for_rotation(self.bb, 30, self.img.size)
-        for i, j in zip(rotated_bb, result):
-            self.assertAlmostEqual(i, j, places=4)
-
     def test_pipeline(self):
         pipeline = AugmentationPipeline()
         pipeline.add_transform(HorizontalFlip(p=1.0))
         pipeline.add_transform(RandomRotation(degrees=30))
 
-        new_img, new_bb = pipeline.apply_transforms(self.img, self.bb)
+        new_img = pipeline.apply_transforms(self.img)
 
         self.assertIsInstance(new_img, Image.Image)
-        self.assertIsInstance(new_bb, list)
 
 
 if __name__ == "__main__":
diff --git a/tests/data/notyet-test_dataset.py b/tests/data/test_dataset.py
similarity index 71%
rename from tests/data/notyet-test_dataset.py
rename to tests/data/test_dataset.py
index 48ce2e0376..9b8bedc5b8 100644
--- a/tests/data/notyet-test_dataset.py
+++ b/tests/data/test_dataset.py
@@ -10,19 +10,16 @@
 
 class TestImageDataset(unittest.TestCase):
     def setUp(self):
-        self.pairs = [(Path("data/images/c1/i1.png"),
-                       Path("data/labels/c1/i1.txt"))]
+        self.images = [Path("data/images/c1/i1.png")]
         self.class_map = {"c1": 0}
         self.preprocessor = ImagePrep()
         self.augmentator = AugmentationPipeline()
 
     @patch.object(ImagePrep, 'load_image')
-    @patch.object(ImagePrep, 'load_labels')
     @patch.object(ImagePrep, 'to_tensor')
-    def test_loading(self, to_tensor_mock, load_labels_mock, load_image_mock):
+    def test_loading(self, to_tensor_mock, load_image_mock):
         # Mock the return values
         load_image_mock.return_value = MagicMock()
-        load_labels_mock.return_value = [(0.1, 0.2, 0.3, 0.4)]
         to_tensor_mock.return_value = torch.rand(3, 224, 224)  # expect shape
 
         dataset = ImageDataset(
@@ -32,18 +29,15 @@ def test_loading(self, to_tensor_mock, load_labels_mock, load_image_mock):
             class_map=self.class_map
         )
 
-        tensor, bboxes, cid = dataset[0]
+        tensor, cid = dataset[0]
 
         self.assertIsInstance(tensor, torch.Tensor)
         self.assertEqual(tensor.shape, (3, 224, 224))
-        self.assertIsInstance(bboxes, list)
-        self.assertEqual(bboxes, [(0.1, 0.2, 0.3, 0.4)])
         self.assertIsInstance(cid, int)
         self.assertEqual(cid, 0)
 
     @patch.object(ImagePrep, 'load_image')
-    @patch.object(ImagePrep, 'load_labels', return_value=[])
-    def test_empty_bb(self, _, load_image_mock):
+    def test_empty_bb(self, load_image_mock):
         load_image_mock.return_value = MagicMock()
         dataset = ImageDataset(
             pairs=self.pairs,
@@ -55,9 +49,7 @@ def test_empty_bb(self, _, load_image_mock):
             dataset[0]
 
     @patch.object(ImagePrep, 'load_image')
-    @patch.object(ImagePrep, 'load_labels',
-                  return_value=[(0.1, 0.2, 0.3, 0.4)])
-    def test_no_class(self, load_labels_mock, load_image_mock):
+    def test_no_class(self, load_image_mock):
         load_image_mock.return_value = np.ones((224, 224, 3))
         dataset = ImageDataset(
             pairs=self.pairs,
diff --git a/tests/data/notyet-test_loader_factory.py b/tests/data/test_loader_factory.py
similarity index 100%
rename from tests/data/notyet-test_loader_factory.py
rename to tests/data/test_loader_factory.py
diff --git a/tests/data/notyet-test_pair_loader.py b/tests/data/test_pair_loader.py
similarity index 100%
rename from tests/data/notyet-test_pair_loader.py
rename to tests/data/test_pair_loader.py
diff --git a/tests/data/notyet-test_preprocessor.py b/tests/data/test_preprocessor.py
similarity index 100%
rename from tests/data/notyet-test_preprocessor.py
rename to tests/data/test_preprocessor.py
diff --git a/tests/data/notyet-test_scaler.py b/tests/data/test_scaler.py
similarity index 100%
rename from tests/data/notyet-test_scaler.py
rename to tests/data/test_scaler.py
diff --git a/tests/data/notyet-test_splitter.py b/tests/data/test_splitter.py
similarity index 100%
rename from tests/data/notyet-test_splitter.py
rename to tests/data/test_splitter.py
diff --git a/tests/data/notyet-test_visualizer.py b/tests/data/test_visualizer.py
similarity index 100%
rename from tests/data/notyet-test_visualizer.py
rename to tests/data/test_visualizer.py

From 944f66d0c5a0460d429835c6f5c980c9ca1bba49 Mon Sep 17 00:00:00 2001
From: Sinan <sinan_ceviker@yahoo.com>
Date: Sun, 25 May 2025 01:12:31 +0200
Subject: [PATCH 05/32] first model trained!

---
 .gitignore                                    |  3 +-
 Pipfile                                       |  3 ++
 main.py                                       | 53 +++++++++++++++----
 recaptcha_classifier/__init__.py              |  8 ++-
 recaptcha_classifier/detection_labels.py      |  4 +-
 recaptcha_classifier/features/__init__.py     |  5 ++
 .../features/evaluation/__init__.py           |  5 ++
 .../features/evaluation/evaluate.py           |  4 +-
 .../models/main_model/__init__.py             |  5 ++
 .../models/main_model/model_class.py          | 11 ++--
 recaptcha_classifier/train/__init__.py        |  3 ++
 recaptcha_classifier/train/training.py        | 10 ++--
 12 files changed, 86 insertions(+), 28 deletions(-)

diff --git a/.gitignore b/.gitignore
index 1cdd18d520..ef3137528f 100644
--- a/.gitignore
+++ b/.gitignore
@@ -6,4 +6,5 @@ venv/
 .env
 
 private/
-/data/
\ No newline at end of file
+/data/
+/models/
\ No newline at end of file
diff --git a/Pipfile b/Pipfile
index 5f3f93c27c..cc9b6544d7 100644
--- a/Pipfile
+++ b/Pipfile
@@ -10,6 +10,7 @@ verify_ssl = true
 
 [packages]
 numpy = "*"
+pillow= "*"
 opencv-python = "*"
 pre-commit = "*"
 torch = "*"
@@ -18,6 +19,8 @@ torchaudio = "*"
 matplotlib = "*"
 torchmetrics = "*"
 tqdm = "*"
+alive-progress = "*"
+torcheval = "*"
 
 [dev-packages]
 
diff --git a/main.py b/main.py
index c06ce2ac04..b16cd9cf14 100644
--- a/main.py
+++ b/main.py
@@ -1,7 +1,11 @@
 from recaptcha_classifier import (
     DetectionLabels,
-    DataPreprocessingPipeline
+    DataPreprocessingPipeline,
+    MainCNN,
+    Trainer,
+    evaluate_model
 )
+import torch
 
 
 def main():
@@ -12,15 +16,44 @@ def main():
 
     loaders = pipeline.run()
     
-    print("Data loaders built successfully.")
-
-    for split, loader in loaders.items():
-        print(f"{split.upper()} DataLoader:")
-        batch = next(iter(loader))
-        images, labels = batch
-        print(f" - images.shape: {images.shape}")
-        print(f" - labels.shape: {labels.shape}")
-        print(f" - class IDs: {labels.tolist()}")
+    model = MainCNN(
+        n_layers=3,
+        kernel_size=3,
+        num_classes=len(DetectionLabels),
+    )
+    
+    optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)
+    scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=5, gamma=0.5)
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    
+    trainer = Trainer(
+        train_loader=loaders['train'],
+        val_loader=loaders['val'],
+        epochs=10,
+        optimizer=optimizer,
+        scheduler=scheduler,
+        save_folder='models',
+        device=device
+    )
+    
+    trainer.train(model)
+    
+    history = trainer.loss_acc_history
+    print("Training completed. Loss and accuracy history:")
+    print(history)
+    
+    results = evaluate_model(
+        model=model,
+        test_loader=loaders['test'],
+        device=device,
+        num_classes=len(DetectionLabels),
+        class_names=DetectionLabels.dataset_classnames(),
+        plot_cm=True
+    )
+    
+    print("Evaluation results:")
+    for key, value in results.items():
+        print(f"{key}: {value}")
 
 
 if __name__ == '__main__':
diff --git a/recaptcha_classifier/__init__.py b/recaptcha_classifier/__init__.py
index c1a3c4d72c..61c583b299 100644
--- a/recaptcha_classifier/__init__.py
+++ b/recaptcha_classifier/__init__.py
@@ -1,9 +1,15 @@
 from .models import SimpleCNN
+from .models.main_model import MainCNN
 from .detection_labels import DetectionLabels
 from .data import DataPreprocessingPipeline
+from .train import Trainer
+from .features import evaluate_model
 
 __all__ = [
     "DetectionLabels",
     "DataPreprocessingPipeline",
-    'SimpleCNN'
+    'SimpleCNN',
+    'MainCNN',
+    'Trainer',
+    'evaluate_model'
 ]
\ No newline at end of file
diff --git a/recaptcha_classifier/detection_labels.py b/recaptcha_classifier/detection_labels.py
index e4ae0311c4..f99c72e3ee 100644
--- a/recaptcha_classifier/detection_labels.py
+++ b/recaptcha_classifier/detection_labels.py
@@ -76,9 +76,7 @@ def from_id(cls, id: int) -> str:
     @classmethod
     def dataset_classnames(cls) -> list:
         """
-        Returns a list of class names, only with first letter capitalized.
-        We use it for the pair loader, as that is the format of the folders
-        downloaded from the dataset.
+        Returns a list of class names.
 
         Returns:
             list: List of class names.
diff --git a/recaptcha_classifier/features/__init__.py b/recaptcha_classifier/features/__init__.py
index e69de29bb2..915c534014 100644
--- a/recaptcha_classifier/features/__init__.py
+++ b/recaptcha_classifier/features/__init__.py
@@ -0,0 +1,5 @@
+from .evaluation import evaluate_model
+
+__all__ = [
+    "evaluate_model"
+]
\ No newline at end of file
diff --git a/recaptcha_classifier/features/evaluation/__init__.py b/recaptcha_classifier/features/evaluation/__init__.py
index e69de29bb2..95daef105c 100644
--- a/recaptcha_classifier/features/evaluation/__init__.py
+++ b/recaptcha_classifier/features/evaluation/__init__.py
@@ -0,0 +1,5 @@
+from .evaluate import evaluate_model
+
+__all__ = [
+    "evaluate_model"
+]
\ No newline at end of file
diff --git a/recaptcha_classifier/features/evaluation/evaluate.py b/recaptcha_classifier/features/evaluation/evaluate.py
index 03d70f6171..031a042591 100644
--- a/recaptcha_classifier/features/evaluation/evaluate.py
+++ b/recaptcha_classifier/features/evaluation/evaluate.py
@@ -1,4 +1,4 @@
-import recaptcha_classifier.features.evaluation.classification_metrics as cm
+from .classification_metrics import evaluate_classification
 import torch
 from tqdm import tqdm
 
@@ -44,7 +44,7 @@ def evaluate_model(model: torch.nn.Module,
     y_pred = torch.cat(all_preds)
     y_true = torch.cat(all_targets)
 
-    class_results = cm.evaluate_classification(
+    class_results = evaluate_classification(
         y_pred=y_pred,
         y_true=y_true,
         num_classes=num_classes,
diff --git a/recaptcha_classifier/models/main_model/__init__.py b/recaptcha_classifier/models/main_model/__init__.py
index e69de29bb2..5417bd46f3 100644
--- a/recaptcha_classifier/models/main_model/__init__.py
+++ b/recaptcha_classifier/models/main_model/__init__.py
@@ -0,0 +1,5 @@
+from .model_class import MainCNN
+
+__all__ = [
+    "MainCNN"
+]
\ No newline at end of file
diff --git a/recaptcha_classifier/models/main_model/model_class.py b/recaptcha_classifier/models/main_model/model_class.py
index 5b8a988288..9a8aca59a0 100644
--- a/recaptcha_classifier/models/main_model/model_class.py
+++ b/recaptcha_classifier/models/main_model/model_class.py
@@ -1,13 +1,15 @@
 import torch
 import torch.nn as nn
+from recaptcha_classifier.models.base_model import BaseModel
 
-class MainCNN(nn.Module): # should inherit BaseModel(nn.Module)
+
+class MainCNN(BaseModel):
 
     def __init__(self,
                  n_layers: int,
                  # n_heads: int,
                  kernel_size: int,
-                 num_classes: int = 3,
+                 num_classes: int,
                  input_shape: tuple = (3, 224, 224),
                  base_channels: int = 32
                  ) -> None:
@@ -63,8 +65,5 @@ def forward(self, x) -> torch.Tensor:
 
         x = x.view(x.size(0), -1)
         x = self.classifier(x)
-        return x
-
-
-
+        return x  # ! return logits, torch.softmax(x, dim=1) if needed for uncertainty
 
diff --git a/recaptcha_classifier/train/__init__.py b/recaptcha_classifier/train/__init__.py
index e69de29bb2..3d6a085185 100644
--- a/recaptcha_classifier/train/__init__.py
+++ b/recaptcha_classifier/train/__init__.py
@@ -0,0 +1,3 @@
+from .training import Trainer
+
+__all__ = ['Trainer']
\ No newline at end of file
diff --git a/recaptcha_classifier/train/training.py b/recaptcha_classifier/train/training.py
index 331de15cd5..e5385372df 100644
--- a/recaptcha_classifier/train/training.py
+++ b/recaptcha_classifier/train/training.py
@@ -17,10 +17,10 @@ def __init__(self,
                  epochs: int,
                  optimizer: torch.optim.Optimizer,
                  scheduler: torch.optim.lr_scheduler,
-                 save_folder: str,
-                 model_file_name='model.pt',
-                 optimizer_file_name='optimizer.pt',
-                 scheduler_file_name='scheduler.pt',
+                 save_folder: str = "models",
+                 model_file_name: str ='model.pt',
+                 optimizer_file_name: str ='optimizer.pt',
+                 scheduler_file_name: str ='scheduler.pt',
                  device: torch.device |None = None
                  ):
         """
@@ -66,7 +66,7 @@ def select_device(self, device=None):
             self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
 
 
-    def train(self, model, load_checkpoint: bool) -> None:
+    def train(self, model, load_checkpoint: bool = False) -> None:
         """
         Main training loop.
         :param model: Model for training.

From bf67dad7b6fb74868d7c015ba56cc66271ed4208 Mon Sep 17 00:00:00 2001
From: Sinan <sinan_ceviker@yahoo.com>
Date: Sun, 25 May 2025 01:38:48 +0200
Subject: [PATCH 06/32] first complete training!!!

---
 evaluation_results.json                       |   0
 main.py                                       |   3 +-
 recaptcha_classifier/data/__init__.py         |   8 +-
 recaptcha_classifier/data/augment.py          |  69 ++----------------
 recaptcha_classifier/data/pipeline.py         |  14 ++--
 .../evaluation/classification_metrics.py      |  11 ++-
 .../models/main_model/model_class.py          |   2 +-
 reports/figures/first_training.png            | Bin 0 -> 77959 bytes
 8 files changed, 25 insertions(+), 82 deletions(-)
 create mode 100644 evaluation_results.json
 create mode 100644 reports/figures/first_training.png

diff --git a/evaluation_results.json b/evaluation_results.json
new file mode 100644
index 0000000000..e69de29bb2
diff --git a/main.py b/main.py
index b16cd9cf14..f7e2a0808a 100644
--- a/main.py
+++ b/main.py
@@ -17,7 +17,7 @@ def main():
     loaders = pipeline.run()
     
     model = MainCNN(
-        n_layers=3,
+        n_layers=2,#3,
         kernel_size=3,
         num_classes=len(DetectionLabels),
     )
@@ -55,6 +55,5 @@ def main():
     for key, value in results.items():
         print(f"{key}: {value}")
 
-
 if __name__ == '__main__':
     main()
diff --git a/recaptcha_classifier/data/__init__.py b/recaptcha_classifier/data/__init__.py
index b4dde44d2b..ab71a0435a 100644
--- a/recaptcha_classifier/data/__init__.py
+++ b/recaptcha_classifier/data/__init__.py
@@ -6,11 +6,7 @@
 from .loader_factory import LoaderFactory
 from .dataset import ImageDataset
 from .preprocessor import ImagePrep
-from .augment import (
-    AugmentationPipeline,
-    HorizontalFlip,
-    RandomRotation
-)
+from .augment import AugmentationPipeline
 
 from .collate_batch import collate_batch
 
@@ -33,8 +29,6 @@
     "ImageDataset",
     "ImagePrep",
     "AugmentationPipeline",
-    "HorizontalFlip",
-    "RandomRotation",
     # Methods
     "collate_batch",
     # Types
diff --git a/recaptcha_classifier/data/augment.py b/recaptcha_classifier/data/augment.py
index 1dcef38bbf..2ee09cfc16 100644
--- a/recaptcha_classifier/data/augment.py
+++ b/recaptcha_classifier/data/augment.py
@@ -1,41 +1,20 @@
-import random
-from abc import ABC, abstractmethod
-from PIL import Image
 from typing import List
 from .types import LoadedImg
-
-
-class Augmentation(ABC):
-    """Abstract class for data augmentation."""
-    @abstractmethod
-    def augment(self,
-                image: LoadedImg,
-                annotations: List) -> LoadedImg:
-        """
-        Apply the transformation of the image and updates the bounding boxes
-        if necessary.
-
-        Args:
-            image (LoadedImg): The image to be augmented.
-            annotations (List): List of annotations associated with the image.
-
-        Returns:
-            LoadedImg: The augmented image and the updated
-            annotations.
-        """
-        pass
+from torchvision import transforms
 
 
 class AugmentationPipeline:
     """Class to manage a series of augmentations in sequence."""
-    def __init__(self, transforms=[]) -> None:
-        self._transforms: List[Augmentation] = transforms
+    def __init__(self, transforms_list: List) -> None:
+        """
+        Initializes the augmentation pipeline with a list of transformations.
+        """
+        self._pipeline = transforms.Compose(transforms_list)
 
     def apply_transforms(self,
                          image: LoadedImg) -> LoadedImg:
         """
-        Apply all transformations in the pipeline to the image and
-        annotations.
+        Apply all transformations in the pipeline to the image.
 
         Args:
             image (LoadedImg): The image to be augmented.
@@ -43,36 +22,4 @@ def apply_transforms(self,
         Returns:
             LoadedImg: The augmented image.
         """
-        for transform in self._transforms:
-            if hasattr(transform, 'prob') and random.random() > transform.prob:
-                continue
-            image = transform.augment(image)
-        return image
-
-
-class HorizontalFlip(Augmentation):
-    """Flips the image horizontally, with probability p."""
-    def __init__(self, p: float = 0.5) -> None:
-        self.prob = p
-
-    def augment(self,
-                image: LoadedImg) -> LoadedImg:
-        flipped = image.transpose(Image.FLIP_LEFT_RIGHT)
-
-        return flipped
-
-
-class RandomRotation(Augmentation):
-    """
-    Rotates the image by a random angle.
-    """
-    def __init__(self, degrees: float = 30.0, p: float = 0.5) -> None:
-        self._degrees = degrees
-        self.prob = p
-
-    def augment(self,
-                image: LoadedImg) -> LoadedImg:
-        angle = random.uniform(-self._degrees, self._degrees)
-
-        rotated = image.rotate(angle)
-        return rotated
+        return self._pipeline(image)
\ No newline at end of file
diff --git a/recaptcha_classifier/data/pipeline.py b/recaptcha_classifier/data/pipeline.py
index 612328a8f2..0f113a09ef 100644
--- a/recaptcha_classifier/data/pipeline.py
+++ b/recaptcha_classifier/data/pipeline.py
@@ -6,11 +6,8 @@
 from .splitter import DataSplitter
 from .visualizer import Visualizer
 from .preprocessor import ImagePrep
-from .augment import (
-    AugmentationPipeline,
-    HorizontalFlip,
-    RandomRotation
-)
+from .augment import AugmentationPipeline
+from torchvision import transforms
 from .loader_factory import LoaderFactory
 
 
@@ -78,8 +75,11 @@ def _build_augmentator(self) -> AugmentationPipeline:
             AugmentationPipeline: The augmentation pipeline.
         """
         return AugmentationPipeline([
-            HorizontalFlip(p=0.5),
-            RandomRotation(degrees=30, p=0.5)
+            transforms.RandomHorizontalFlip(p=0.5),
+            transforms.RandomRotation(degrees=15),
+            transforms.ColorJitter(brightness=0.3, contrast=0.3, saturation=0.3),
+            transforms.RandomResizedCrop(size=(224, 224), scale=(0.8, 1.0)),
+            transforms.GaussianBlur(kernel_size=(5, 9), sigma=(0.1, 2.0)),
         ])
 
     def run(self) -> Dict[str, DataLoader]:
diff --git a/recaptcha_classifier/features/evaluation/classification_metrics.py b/recaptcha_classifier/features/evaluation/classification_metrics.py
index 0eef54865b..f72741910d 100644
--- a/recaptcha_classifier/features/evaluation/classification_metrics.py
+++ b/recaptcha_classifier/features/evaluation/classification_metrics.py
@@ -11,7 +11,8 @@ def evaluate_classification(y_pred: Tensor,
                             num_classes: int,
                             average: str = 'macro',
                             cm_plot: bool = True,
-                            class_names: Optional[list[str]] = None) -> dict:
+                            class_names: Optional[list[str]] = None,
+                            ) -> dict:
     """
     Evaluate classification model using torchmetrics
 
@@ -37,10 +38,12 @@ def evaluate_classification(y_pred: Tensor,
     # Convert logits to predicted labels
     y_pred = torch.argmax(y_pred, dim=1)
 
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
     # Initialize Metrics
-    acc = Accuracy(task="multiclass", num_classes=num_classes)
-    f1 = F1Score(task="multiclass", num_classes=num_classes, average=average)
-    confmat = MulticlassConfusionMatrix(num_classes=num_classes)
+    acc = Accuracy(task="multiclass", num_classes=num_classes).to(device)
+    f1 = F1Score(task="multiclass", num_classes=num_classes, average=average).to(device)
+    confmat = MulticlassConfusionMatrix(num_classes=num_classes).to(device)
 
     # Compute metrics
     acc_val = acc(y_pred, y_true)
diff --git a/recaptcha_classifier/models/main_model/model_class.py b/recaptcha_classifier/models/main_model/model_class.py
index 9a8aca59a0..ae13a81152 100644
--- a/recaptcha_classifier/models/main_model/model_class.py
+++ b/recaptcha_classifier/models/main_model/model_class.py
@@ -11,7 +11,7 @@ def __init__(self,
                  kernel_size: int,
                  num_classes: int,
                  input_shape: tuple = (3, 224, 224),
-                 base_channels: int = 32
+                 base_channels: int = 16 #32
                  ) -> None:
         super().__init__()
 
diff --git a/reports/figures/first_training.png b/reports/figures/first_training.png
new file mode 100644
index 0000000000000000000000000000000000000000..dedddc444b5be1d1faac7279c43325cced13e538
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HcmV?d00001


From 29157176409e88f226b2f7b1b4bae0a16687f95f Mon Sep 17 00:00:00 2001
From: Sinan <sinan_ceviker@yahoo.com>
Date: Mon, 26 May 2025 15:59:14 +0200
Subject: [PATCH 07/32] small diffs

---
 main.py                                           | 15 +++++++--------
 recaptcha_classifier/__init__.py                  |  3 ++-
 recaptcha_classifier/models/__init__.py           |  3 ++-
 .../models/main_model/__init__.py                 |  4 +++-
 .../models/main_model/model_class.py              |  2 +-
 5 files changed, 15 insertions(+), 12 deletions(-)

diff --git a/main.py b/main.py
index f7e2a0808a..88e4a3034b 100644
--- a/main.py
+++ b/main.py
@@ -17,19 +17,19 @@ def main():
     loaders = pipeline.run()
     
     model = MainCNN(
-        n_layers=2,#3,
+        n_layers=2,
         kernel_size=3,
         num_classes=len(DetectionLabels),
     )
     
-    optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)
+    optimizer = torch.optim.RAdam(model.parameters(), lr=1e-3)
     scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=5, gamma=0.5)
     device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
     
     trainer = Trainer(
         train_loader=loaders['train'],
         val_loader=loaders['val'],
-        epochs=10,
+        epochs=15,
         optimizer=optimizer,
         scheduler=scheduler,
         save_folder='models',
@@ -38,11 +38,13 @@ def main():
     
     trainer.train(model)
     
+    trainer.delete_checkpoints()
+    
     history = trainer.loss_acc_history
     print("Training completed. Loss and accuracy history:")
     print(history)
     
-    results = evaluate_model(
+    evaluate_model(
         model=model,
         test_loader=loaders['test'],
         device=device,
@@ -50,10 +52,7 @@ def main():
         class_names=DetectionLabels.dataset_classnames(),
         plot_cm=True
     )
-    
-    print("Evaluation results:")
-    for key, value in results.items():
-        print(f"{key}: {value}")
+
 
 if __name__ == '__main__':
     main()
diff --git a/recaptcha_classifier/__init__.py b/recaptcha_classifier/__init__.py
index 61c583b299..792c58996c 100644
--- a/recaptcha_classifier/__init__.py
+++ b/recaptcha_classifier/__init__.py
@@ -1,5 +1,5 @@
 from .models import SimpleCNN
-from .models.main_model import MainCNN
+from .models.main_model import MainCNN, HPOptimizer
 from .detection_labels import DetectionLabels
 from .data import DataPreprocessingPipeline
 from .train import Trainer
@@ -10,6 +10,7 @@
     "DataPreprocessingPipeline",
     'SimpleCNN',
     'MainCNN',
+    'HPOptimizer',
     'Trainer',
     'evaluate_model'
 ]
\ No newline at end of file
diff --git a/recaptcha_classifier/models/__init__.py b/recaptcha_classifier/models/__init__.py
index 5f6908ea92..735c490da1 100644
--- a/recaptcha_classifier/models/__init__.py
+++ b/recaptcha_classifier/models/__init__.py
@@ -1,3 +1,4 @@
 from .simple_classifier_model import SimpleCNN
+from .main_model import MainCNN, HPOptimizer
 
-__all__ = ['SimpleCNN']
+__all__ = ['SimpleCNN', 'MainCNN', 'HPOptimizer']
diff --git a/recaptcha_classifier/models/main_model/__init__.py b/recaptcha_classifier/models/main_model/__init__.py
index 5417bd46f3..405ca63fb8 100644
--- a/recaptcha_classifier/models/main_model/__init__.py
+++ b/recaptcha_classifier/models/main_model/__init__.py
@@ -1,5 +1,7 @@
 from .model_class import MainCNN
+from .HPoptimizer import HPOptimizer
 
 __all__ = [
-    "MainCNN"
+    "MainCNN",
+    "HPOptimizer"
 ]
\ No newline at end of file
diff --git a/recaptcha_classifier/models/main_model/model_class.py b/recaptcha_classifier/models/main_model/model_class.py
index ae13a81152..9a8aca59a0 100644
--- a/recaptcha_classifier/models/main_model/model_class.py
+++ b/recaptcha_classifier/models/main_model/model_class.py
@@ -11,7 +11,7 @@ def __init__(self,
                  kernel_size: int,
                  num_classes: int,
                  input_shape: tuple = (3, 224, 224),
-                 base_channels: int = 16 #32
+                 base_channels: int = 32
                  ) -> None:
         super().__init__()
 

From dba0789e2a29132ceea548b9776a50b0fda7c0e1 Mon Sep 17 00:00:00 2001
From: Sinan <sinan_ceviker@yahoo.com>
Date: Mon, 26 May 2025 16:19:20 +0200
Subject: [PATCH 08/32] solved unit tests for other parts of code

---
 Pipfile                                                     | 1 +
 .../features/evaluation/classification_metrics.py           | 6 +++++-
 recaptcha_classifier/models/main_model/HPoptimizer.py       | 4 +++-
 tests/models/test_training.py                               | 1 +
 4 files changed, 10 insertions(+), 2 deletions(-)

diff --git a/Pipfile b/Pipfile
index cc9b6544d7..c6a1cb4dc6 100644
--- a/Pipfile
+++ b/Pipfile
@@ -10,6 +10,7 @@ verify_ssl = true
 
 [packages]
 numpy = "*"
+pandas = "*"
 pillow= "*"
 opencv-python = "*"
 pre-commit = "*"
diff --git a/recaptcha_classifier/features/evaluation/classification_metrics.py b/recaptcha_classifier/features/evaluation/classification_metrics.py
index f72741910d..319ba70ecc 100644
--- a/recaptcha_classifier/features/evaluation/classification_metrics.py
+++ b/recaptcha_classifier/features/evaluation/classification_metrics.py
@@ -37,8 +37,12 @@ def evaluate_classification(y_pred: Tensor,
 
     # Convert logits to predicted labels
     y_pred = torch.argmax(y_pred, dim=1)
-
     device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    y_pred = y_pred.to(device)
+    y_true = y_true.to(device)
+    
+    if num_classes <= 0:
+        raise ValueError("num_classes must be a positive integer")
 
     # Initialize Metrics
     acc = Accuracy(task="multiclass", num_classes=num_classes).to(device)
diff --git a/recaptcha_classifier/models/main_model/HPoptimizer.py b/recaptcha_classifier/models/main_model/HPoptimizer.py
index 850c2629fd..84f922990a 100644
--- a/recaptcha_classifier/models/main_model/HPoptimizer.py
+++ b/recaptcha_classifier/models/main_model/HPoptimizer.py
@@ -6,6 +6,8 @@
 from recaptcha_classifier.models.main_model.model_class import MainCNN
 from recaptcha_classifier.train.training import Trainer
 
+from recaptcha_classifier.detection_labels import DetectionLabels
+
 
 class HPOptimizer(object):
     """Class for optimizing hyperparameters."""
@@ -59,7 +61,7 @@ def optimize_hyperparameters(self,
 
 
     def _train_one_model(self, hp_combo) -> None:
-        model = MainCNN(n_layers=int(hp_combo[0]), kernel_size=int(hp_combo[1]))
+        model = MainCNN(n_layers=int(hp_combo[0]), kernel_size=int(hp_combo[1]), num_classes=len(DetectionLabels.all()))
         self._trainer.optimizer = torch.optim.RAdam(model.parameters(), lr=hp_combo[2])
         self._trainer.train(model=model, load_checkpoint=False)
 
diff --git a/tests/models/test_training.py b/tests/models/test_training.py
index f89b7aac1c..c5a55ea415 100644
--- a/tests/models/test_training.py
+++ b/tests/models/test_training.py
@@ -49,6 +49,7 @@ def test_train_process(self) -> None:
 
 
     def test_train_load_checkpoint(self):
+        self.trainer.train(model=self.model, load_checkpoint=False)
         self.trainer.train(model=self.model, load_checkpoint=True)
         assert_equal(os.path.exists(os.path.join(self.trainer.save_folder, self.trainer.model_file_name)),
                      True,

From 88f31ba5ba85635f6dd2b2bcf0416dac8b77e7e0 Mon Sep 17 00:00:00 2001
From: Sinan <sinan_ceviker@yahoo.com>
Date: Mon, 26 May 2025 16:55:50 +0200
Subject: [PATCH 09/32] fixed data preproc tests

---
 tests/data/test_augment.py        | 29 +++--------
 tests/data/test_dataset.py        | 28 ++++-------
 tests/data/test_loader_factory.py | 19 +++----
 tests/data/test_pair_loader.py    | 83 -------------------------------
 tests/data/test_paths_loader.py   | 35 +++++++++++++
 tests/data/test_preprocessor.py   |  7 ---
 tests/data/test_scaler.py         | 20 --------
 tests/data/test_splitter.py       |  2 +-
 tests/data/test_visualizer.py     | 12 ++---
 9 files changed, 64 insertions(+), 171 deletions(-)
 delete mode 100644 tests/data/test_pair_loader.py
 create mode 100644 tests/data/test_paths_loader.py
 delete mode 100644 tests/data/test_scaler.py

diff --git a/tests/data/test_augment.py b/tests/data/test_augment.py
index 667a2681c8..de6eeef074 100644
--- a/tests/data/test_augment.py
+++ b/tests/data/test_augment.py
@@ -2,11 +2,8 @@
 import numpy as np
 from unittest.mock import patch
 from PIL import Image
-from recaptcha_classifier.data.augment import (
-    AugmentationPipeline,
-    HorizontalFlip,
-    RandomRotation
-)
+from recaptcha_classifier.data.augment import AugmentationPipeline
+from torchvision import transforms
 
 
 class TestAugmentation(unittest.TestCase):
@@ -17,25 +14,11 @@ def setUp(self):
         )
         self.img = self.img.convert("RGB")
 
-    def test_horizontal_flip(self):
-        aug = HorizontalFlip(p=1.0)
-        flipped_img = aug.augment(self.img)
-
-        self.assertFalse(np.array_equal(np.array(flipped_img),
-                                        np.array(self.img)))
-
-    @patch('random.uniform', return_value=30)
-    def test_random_rotation(self, _):
-        augmenter = RandomRotation(degrees=30)
-        rotated_img = augmenter.augment(self.img)
-
-        self.assertFalse(np.array_equal(np.array(rotated_img),
-                                        np.array(self.img)))
-
     def test_pipeline(self):
-        pipeline = AugmentationPipeline()
-        pipeline.add_transform(HorizontalFlip(p=1.0))
-        pipeline.add_transform(RandomRotation(degrees=30))
+        pipeline = AugmentationPipeline([
+            transforms.RandomHorizontalFlip(p=1.0),
+            transforms.RandomRotation(degrees=30)
+        ])
 
         new_img = pipeline.apply_transforms(self.img)
 
diff --git a/tests/data/test_dataset.py b/tests/data/test_dataset.py
index 9b8bedc5b8..1fd9118067 100644
--- a/tests/data/test_dataset.py
+++ b/tests/data/test_dataset.py
@@ -10,20 +10,22 @@
 
 class TestImageDataset(unittest.TestCase):
     def setUp(self):
-        self.images = [Path("data/images/c1/i1.png")]
+        self.items = [Path("data/c1/i1.png")]
         self.class_map = {"c1": 0}
         self.preprocessor = ImagePrep()
-        self.augmentator = AugmentationPipeline()
+        self.augmentator = AugmentationPipeline(transforms_list=[])
 
     @patch.object(ImagePrep, 'load_image')
     @patch.object(ImagePrep, 'to_tensor')
-    def test_loading(self, to_tensor_mock, load_image_mock):
+    @patch.object(ImagePrep, 'class_id_to_tensor')
+    def test_loading(self, class_id_mock, to_tensor_mock, load_image_mock):
         # Mock the return values
         load_image_mock.return_value = MagicMock()
         to_tensor_mock.return_value = torch.rand(3, 224, 224)  # expect shape
+        class_id_mock.return_value = torch.tensor(0)
 
         dataset = ImageDataset(
-            pairs=self.pairs,
+            items=self.items,
             preprocessor=self.preprocessor,
             augmentator=self.augmentator,
             class_map=self.class_map
@@ -33,26 +35,14 @@ def test_loading(self, to_tensor_mock, load_image_mock):
 
         self.assertIsInstance(tensor, torch.Tensor)
         self.assertEqual(tensor.shape, (3, 224, 224))
-        self.assertIsInstance(cid, int)
-        self.assertEqual(cid, 0)
-
-    @patch.object(ImagePrep, 'load_image')
-    def test_empty_bb(self, load_image_mock):
-        load_image_mock.return_value = MagicMock()
-        dataset = ImageDataset(
-            pairs=self.pairs,
-            preprocessor=self.preprocessor,
-            augmentator=self.augmentator,
-            class_map=self.class_map
-        )
-        with self.assertRaises(ValueError):
-            dataset[0]
+        self.assertIsInstance(cid, torch.Tensor)
+        self.assertEqual(cid.item(), 0)
 
     @patch.object(ImagePrep, 'load_image')
     def test_no_class(self, load_image_mock):
         load_image_mock.return_value = np.ones((224, 224, 3))
         dataset = ImageDataset(
-            pairs=self.pairs,
+            items=self.items,
             preprocessor=self.preprocessor,
             augmentator=self.augmentator,
         )
diff --git a/tests/data/test_loader_factory.py b/tests/data/test_loader_factory.py
index cab1187202..18f768b64e 100644
--- a/tests/data/test_loader_factory.py
+++ b/tests/data/test_loader_factory.py
@@ -12,7 +12,7 @@ class TestLoaderFactory(unittest.TestCase):
     def test_create_loaders(self, dataset_mock):
         class_map = {"class1": 0, "class2": 1}
         preprocessor = ImagePrep()
-        augmentator = AugmentationPipeline()
+        augmentator = AugmentationPipeline(transforms_list=[])
         factory = LoaderFactory(class_map, preprocessor, augmentator)
 
         dataset_mock.return_value = dataset_mock
@@ -20,20 +20,17 @@ def test_create_loaders(self, dataset_mock):
 
         splits = {
             "train": {
-                "class1": [(Path("img1.png"), Path("label1.txt")),
-                           (Path("img2.png"), Path("label2.txt"))],
-                "class2": [(Path("img3.png"), Path("label3.txt"))]
+                "class1": [Path("img1.png"), Path("img2.png")],
             },
             "val": {
-                "class1": [(Path("img4.png"), Path("label4.txt"))],
-                "class2": [(Path("img5.png"), Path("label5.txt"))]
+                "class1":
+                    [Path("img3.png")],
             },
             "test": {
-                "class1": [(Path("img6.png"), Path("label6.txt"))],
-                "class2": [(Path("img7.png"), Path("label7.txt"))]
+                "class1": [Path("img4.png"), Path("img5.png"), Path("img6.png")]
             }
         }
-
+        
         loaders = factory.create_loaders(splits)
 
         self.assertIn("train", loaders)
@@ -45,9 +42,7 @@ def test_create_loaders(self, dataset_mock):
 
         # We also need to check that the train set has the augmentator
         dataset_mock.assert_any_call(
-            pairs=[(Path("img1.png"), Path("label1.txt")),
-                   (Path("img2.png"), Path("label2.txt")),
-                   (Path("img3.png"), Path("label3.txt"))],
+            items=[Path("img1.png"),Path("img2.png")],
             preprocessor=preprocessor,
             augmentator=augmentator,
             class_map=class_map
diff --git a/tests/data/test_pair_loader.py b/tests/data/test_pair_loader.py
deleted file mode 100644
index e7ae89a983..0000000000
--- a/tests/data/test_pair_loader.py
+++ /dev/null
@@ -1,83 +0,0 @@
-import unittest
-from pathlib import Path
-from unittest.mock import patch
-
-from recaptcha_classifier.data.pair_loader import ImagePathsLoader
-
-
-class TestImagePathsLoader(unittest.TestCase):
-    @patch("recaptcha_classifier.data.pair_loader.Path.glob")
-    @patch("recaptcha_classifier.data.pair_loader.Path.exists",
-           return_value=True)
-    @patch("recaptcha_classifier.data.pair_loader.Path.is_dir",
-           return_value=True)
-    def test_load_pairs_with_all_labels(self,
-                                        is_dir_mock,
-                                        exists_mock,
-                                        glob_mock):
-        glob_mock.return_value = [
-            Path("data/images/class1/img1.png"),
-            Path("data/images/class1/img2.png"),
-        ]  # 2 images found in the png glob
-
-        loader = ImagePathsLoader(["class1"])
-        pairs = loader.find_image_paths()
-
-        expected_pairs = {
-            (Path("data/images/class1/img1.png"),
-             Path("data/labels/class1/img1.txt")),
-            (Path("data/images/class1/img2.png"),
-             Path("data/labels/class1/img2.txt")),
-        }
-
-        self.assertEqual(set(pairs["class1"]), expected_pairs)
-        self.assertEqual(len(pairs["class1"]), 2)
-
-    @patch("recaptcha_classifier.data.pair_loader.Path.glob")
-    @patch("recaptcha_classifier.data.pair_loader.Path.exists")
-    @patch("recaptcha_classifier.data.pair_loader.Path.is_dir",
-           return_value=True)
-    def test_load_pairs_with_missing_labels(self,
-                                            is_dir_mock,
-                                            exists_mock,
-                                            glob_mock):
-        glob_mock.return_value = [
-            Path("data/images/class1/img1.png"),
-            Path("data/images/class1/img2.png"),
-        ]  # 2 images found in the png glob
-
-        # label folder exists, img1.txt exists but img2.txt does not
-        exists_mock.side_effect = [True, True, False]
-
-        loader = ImagePathsLoader(["class1"])
-        pairs = loader.find_image_paths()
-
-        self.assertIn("class1", pairs)
-        expected_pair = {
-            (Path("data/images/class1/img1.png"),
-             Path("data/labels/class1/img1.txt")),
-        }
-
-        self.assertEqual(set(pairs["class1"]), expected_pair)
-        self.assertEqual(len(pairs["class1"]), 1)
-
-    @patch("recaptcha_classifier.data.pair_loader.Path.glob")
-    @patch("recaptcha_classifier.data.pair_loader.Path.exists")
-    @patch("recaptcha_classifier.data.pair_loader.Path.is_dir")
-    def test_caching(self, is_dir_mock, exists_mock, glob_mock):
-        loader = ImagePathsLoader(["class1"])
-        loader._pairs = {"test": [(Path("test.png"), Path("test.txt"))]}
-
-        # if any mocked method is called, then the cache is not used
-        for method in (is_dir_mock, exists_mock, glob_mock):
-            method.side_effect = AssertionError(f"{method} called, error!")
-
-        pairs = loader.find_image_paths()
-
-        self.assertIs(pairs, loader._pairs)
-        self.assertEqual(pairs, {"test": [(Path("test.png"),
-                                           Path("test.txt"))]})
-
-
-if __name__ == "__main__":
-    unittest.main()
diff --git a/tests/data/test_paths_loader.py b/tests/data/test_paths_loader.py
new file mode 100644
index 0000000000..3eebc66e82
--- /dev/null
+++ b/tests/data/test_paths_loader.py
@@ -0,0 +1,35 @@
+import unittest
+from pathlib import Path
+from unittest.mock import patch
+
+from recaptcha_classifier.data.paths_loader import ImagePathsLoader
+
+
+class TestImagePathsLoader(unittest.TestCase):
+    @patch("recaptcha_classifier.data.paths_loader.Path.glob")
+    @patch("recaptcha_classifier.data.paths_loader.Path.exists",
+           return_value=True)
+    @patch("recaptcha_classifier.data.paths_loader.Path.is_dir",
+           return_value=True)
+    def test_load_paths(self,
+                                        is_dir_mock,
+                                        exists_mock,
+                                        glob_mock):
+        glob_mock.return_value = [
+            Path("data/images/class1/img1.png"),
+            Path("data/images/class1/img2.png"),
+        ]  # 2 images found in the png glob
+
+        loader = ImagePathsLoader(["class1"])
+        pairs = loader.find_image_paths()
+
+        expected_pairs = {
+            Path("data/images/class1/img1.png"),
+            Path("data/images/class1/img2.png"),
+        }
+
+        self.assertEqual(set(pairs["class1"]), expected_pairs)
+        self.assertEqual(len(pairs["class1"]), 2)
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/tests/data/test_preprocessor.py b/tests/data/test_preprocessor.py
index daec7a0f05..9f533df829 100644
--- a/tests/data/test_preprocessor.py
+++ b/tests/data/test_preprocessor.py
@@ -24,13 +24,6 @@ def test_load_image(self, open_mock):
         img_mock.convert.assert_called_once_with("RGB")
         resized_mock.resize.assert_called_once_with((224, 224), Image.LANCZOS)
 
-    @patch("builtins.open", new_callable=unittest.mock.mock_open,
-           read_data="0 0.2 0.3 0.4 0.5\n")
-    def test_load_labels(self, file_mock):
-        result = self.prep.load_labels(Path("test"))
-
-        self.assertEqual(result, [(0.2, 0.3, 0.4, 0.5)])
-
     def test_to_tensor(self):
         img = Image.new("RGB", (224, 224))
         tensor = self.prep.to_tensor(img)
diff --git a/tests/data/test_scaler.py b/tests/data/test_scaler.py
deleted file mode 100644
index 9a54dd26d4..0000000000
--- a/tests/data/test_scaler.py
+++ /dev/null
@@ -1,20 +0,0 @@
-import unittest
-from recaptcha_classifier.data.scaler import YOLOScaler
-
-
-class TestYOLOScaler(unittest.TestCase):
-    def test_scale_for_flip(self):
-        bb = [(0.5, 0.5, 0.2, 0.2), (0.3, 0.4, 0.1, 0.1)]
-        flipped = YOLOScaler.scale_for_flip(bb)
-        self.assertEqual(flipped, [(0.5, 0.5, 0.2, 0.2),
-                                   (0.7, 0.4, 0.1, 0.1)])
-
-    def test_scale_for_rotation(self):
-        bb = [(0.5, 0.5, 0.4, 0.2)]
-        rot = YOLOScaler.scale_for_rotation(bb, 90, (224, 224))
-        self.assertEqual(len(rot), 1)
-        self.assertTrue(all(0 <= v <= 1 for bb in rot for v in bb))
-
-    def test_empty_list(self):
-        self.assertEqual(YOLOScaler.scale_for_flip([]), [])
-        self.assertEqual(YOLOScaler.scale_for_rotation([], 45, (224, 224)), [])
diff --git a/tests/data/test_splitter.py b/tests/data/test_splitter.py
index 406b04dc5f..10796cc6d0 100644
--- a/tests/data/test_splitter.py
+++ b/tests/data/test_splitter.py
@@ -4,7 +4,7 @@
 
 class TestDataSplitter(unittest.TestCase):
     def test_default_split(self):
-        data = {"test_class": [(f"img_{i}", f"label_{i}") for i in range(10)]}
+        data = {"test_class": [f"img_{i}" for i in range(10)]}
         splits = DataSplitter().split(data)
 
         self.assertEqual(len(splits['train']['test_class']), 7)
diff --git a/tests/data/test_visualizer.py b/tests/data/test_visualizer.py
index 43096a5805..d4f0d9e8e8 100644
--- a/tests/data/test_visualizer.py
+++ b/tests/data/test_visualizer.py
@@ -8,16 +8,16 @@ def setUp(self):
         # only written pairs as string, instead of paths, for simplicity
         self.sample_splits = {
             "train": {
-                "class1": [("img1", "label1"), ("img2", "label2")],
-                "class2": [("img3", "label3"), ("img4", "label4")],
+                "class1": ["img1", "img2"],
+                "class2": ["img3", "img4"],
             },
             "val": {
-                "class1": [("img5", "label5")],
-                "class2": [("img6", "label6")],
+                "class1": ["img5"],
+                "class2": ["img6"],
             },
             "test": {
-                "class1": [("img7", "label7")],
-                "class2": [("img8", "label8")],
+                "class1": ["img7"],
+                "class2": ["img8"],
             },
         }
 

From b357308ef07f6e60f4febd449c3e82a4203b0757 Mon Sep 17 00:00:00 2001
From: Sinan <sinan_ceviker@yahoo.com>
Date: Mon, 26 May 2025 17:15:22 +0200
Subject: [PATCH 10/32] setup integration tests

---
 .github/workflows/ci.yml                      | 32 +++++++++++++
 .pre-commit-config.yaml                       |  2 +-
 recaptcha_classifier/data/pipeline.py         |  2 +-
 tests/integration/test_checkpoint.py          | 48 +++++++++++++++++++
 tests/integration/test_data_pipeline.py       | 13 +++++
 tests/{ => unit}/data/__init__.py             |  0
 tests/{ => unit}/data/test_augment.py         |  0
 tests/{ => unit}/data/test_dataset.py         |  0
 tests/{ => unit}/data/test_loader_factory.py  |  0
 tests/{ => unit}/data/test_paths_loader.py    |  0
 tests/{ => unit}/data/test_preprocessor.py    |  0
 tests/{ => unit}/data/test_splitter.py        |  0
 tests/{ => unit}/data/test_visualizer.py      |  0
 tests/{ => unit}/features/__init__.py         |  0
 .../features/test_classification_metrics.py   |  0
 tests/{ => unit}/features/test_evaluate.py    |  0
 tests/{ => unit}/models/__init__.py           |  0
 tests/{ => unit}/models/test_HPoptimizer.py   |  0
 tests/{ => unit/models}/test_classifier.py    |  0
 tests/{ => unit}/models/test_training.py      |  0
 tests/{ => unit}/models/utils_training_hpo.py |  0
 21 files changed, 95 insertions(+), 2 deletions(-)
 create mode 100644 .github/workflows/ci.yml
 create mode 100644 tests/integration/test_checkpoint.py
 create mode 100644 tests/integration/test_data_pipeline.py
 rename tests/{ => unit}/data/__init__.py (100%)
 rename tests/{ => unit}/data/test_augment.py (100%)
 rename tests/{ => unit}/data/test_dataset.py (100%)
 rename tests/{ => unit}/data/test_loader_factory.py (100%)
 rename tests/{ => unit}/data/test_paths_loader.py (100%)
 rename tests/{ => unit}/data/test_preprocessor.py (100%)
 rename tests/{ => unit}/data/test_splitter.py (100%)
 rename tests/{ => unit}/data/test_visualizer.py (100%)
 rename tests/{ => unit}/features/__init__.py (100%)
 rename tests/{ => unit}/features/test_classification_metrics.py (100%)
 rename tests/{ => unit}/features/test_evaluate.py (100%)
 rename tests/{ => unit}/models/__init__.py (100%)
 rename tests/{ => unit}/models/test_HPoptimizer.py (100%)
 rename tests/{ => unit/models}/test_classifier.py (100%)
 rename tests/{ => unit}/models/test_training.py (100%)
 rename tests/{ => unit}/models/utils_training_hpo.py (100%)

diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
new file mode 100644
index 0000000000..88812b3b37
--- /dev/null
+++ b/.github/workflows/ci.yml
@@ -0,0 +1,32 @@
+name: Integration Tests
+
+on:
+    pull_request:
+        branches:
+            - main
+        types:
+            - opened
+            - synchronize
+            - reopened
+
+jobs:
+    integration-tests:
+        runs-on: ubuntu-latest
+        steps:
+            - name: Checkout code
+              uses: actions/checkout@v4
+
+            - name: Set up Python
+              uses: actions/setup-python@v5
+              with:
+                  python-version: '3.10'
+
+            - name: Install dependencies
+              run: |
+                  python -m pip install --upgrade pip
+                  pip install pipenv
+                  pipenv install --dev
+
+            - name: Run integration tests
+              run: |
+                  pipenv run python -m unittest discover -s tests/integration
\ No newline at end of file
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 5d2bc28c2b..242328f4f0 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -9,6 +9,6 @@ repos:
   hooks:
     - id: run-unittests
       name: Run unittests
-      entry: pipenv run python -m unittest discover tests
+      entry: pipenv run python -m unittest discover tests/unit
       language: system
       pass_filenames: false
diff --git a/recaptcha_classifier/data/pipeline.py b/recaptcha_classifier/data/pipeline.py
index 0f113a09ef..46a489f795 100644
--- a/recaptcha_classifier/data/pipeline.py
+++ b/recaptcha_classifier/data/pipeline.py
@@ -35,7 +35,7 @@ def __init__(self,
                  seed: int = 23,  # our group number
                  batch_size: int = 32,
                  num_workers: int = 4,
-                 balance: bool = False,
+                 balance: bool = True,
                  show_plots: bool = False) -> None:
         """
         Initializes the DataPreprocessingPipeline with the given parameters.
diff --git a/tests/integration/test_checkpoint.py b/tests/integration/test_checkpoint.py
new file mode 100644
index 0000000000..91c7294d09
--- /dev/null
+++ b/tests/integration/test_checkpoint.py
@@ -0,0 +1,48 @@
+import unittest
+import torch
+import os
+from recaptcha_classifier.models.main_model import MainCNN
+from recaptcha_classifier.train.training import Trainer
+from recaptcha_classifier.data.pipeline import DataPreprocessingPipeline
+from recaptcha_classifier.detection_labels import DetectionLabels
+
+
+class TestCheckpointIntegration(unittest.TestCase):
+    def test_checkpoint_save_load(self):
+        pipeline = DataPreprocessingPipeline(
+            DetectionLabels,
+            batch_size=2,
+            num_workers=0
+            )
+        
+        loaders = pipeline.run()
+        
+        model = MainCNN(n_layers=1, kernel_size=3, num_classes=len(DetectionLabels))
+        optimizer = torch.optim.Adam(model.parameters())
+        scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=1)
+        device = torch.device("cpu")
+        
+        trainer = Trainer(
+            train_loader=loaders["train"],
+            val_loader=loaders["val"],
+            optimizer=optimizer,
+            model=model,
+            scheduler=scheduler,
+            device=device,
+            epochs=1,
+        )
+        
+        trainer.train(model)
+        
+        model_state = {k: v.clone() for k, v in model.state_dict().items()}
+        
+        model_new = MainCNN(n_layers=1, kernel_size=3, num_classes=len(DetectionLabels))
+        
+        trainer.load_checkpoint_states(model_new)
+        
+        for key in model_state:
+            self.assertTrue(
+                torch.equal(model_state[key], model_new.state_dict()[key])
+            )
+            
+        trainer.delete_checkpoints()
\ No newline at end of file
diff --git a/tests/integration/test_data_pipeline.py b/tests/integration/test_data_pipeline.py
new file mode 100644
index 0000000000..4322638e2f
--- /dev/null
+++ b/tests/integration/test_data_pipeline.py
@@ -0,0 +1,13 @@
+import unittest
+
+from recaptcha_classifier.data.pipeline import DataPreprocessingPipeline
+from recaptcha_classifier.detection_labels import DetectionLabels
+
+
+class TestDataPipeline(unittest.TestCase):
+    def test_pipeline_runs(self):
+        pipeline = DataPreprocessingPipeline(DetectionLabels)
+        loaders = pipeline.run()
+        
+        self.assertIn("train", loaders)
+        self.assertGreater(len(loaders["train"]), 0)
\ No newline at end of file
diff --git a/tests/data/__init__.py b/tests/unit/data/__init__.py
similarity index 100%
rename from tests/data/__init__.py
rename to tests/unit/data/__init__.py
diff --git a/tests/data/test_augment.py b/tests/unit/data/test_augment.py
similarity index 100%
rename from tests/data/test_augment.py
rename to tests/unit/data/test_augment.py
diff --git a/tests/data/test_dataset.py b/tests/unit/data/test_dataset.py
similarity index 100%
rename from tests/data/test_dataset.py
rename to tests/unit/data/test_dataset.py
diff --git a/tests/data/test_loader_factory.py b/tests/unit/data/test_loader_factory.py
similarity index 100%
rename from tests/data/test_loader_factory.py
rename to tests/unit/data/test_loader_factory.py
diff --git a/tests/data/test_paths_loader.py b/tests/unit/data/test_paths_loader.py
similarity index 100%
rename from tests/data/test_paths_loader.py
rename to tests/unit/data/test_paths_loader.py
diff --git a/tests/data/test_preprocessor.py b/tests/unit/data/test_preprocessor.py
similarity index 100%
rename from tests/data/test_preprocessor.py
rename to tests/unit/data/test_preprocessor.py
diff --git a/tests/data/test_splitter.py b/tests/unit/data/test_splitter.py
similarity index 100%
rename from tests/data/test_splitter.py
rename to tests/unit/data/test_splitter.py
diff --git a/tests/data/test_visualizer.py b/tests/unit/data/test_visualizer.py
similarity index 100%
rename from tests/data/test_visualizer.py
rename to tests/unit/data/test_visualizer.py
diff --git a/tests/features/__init__.py b/tests/unit/features/__init__.py
similarity index 100%
rename from tests/features/__init__.py
rename to tests/unit/features/__init__.py
diff --git a/tests/features/test_classification_metrics.py b/tests/unit/features/test_classification_metrics.py
similarity index 100%
rename from tests/features/test_classification_metrics.py
rename to tests/unit/features/test_classification_metrics.py
diff --git a/tests/features/test_evaluate.py b/tests/unit/features/test_evaluate.py
similarity index 100%
rename from tests/features/test_evaluate.py
rename to tests/unit/features/test_evaluate.py
diff --git a/tests/models/__init__.py b/tests/unit/models/__init__.py
similarity index 100%
rename from tests/models/__init__.py
rename to tests/unit/models/__init__.py
diff --git a/tests/models/test_HPoptimizer.py b/tests/unit/models/test_HPoptimizer.py
similarity index 100%
rename from tests/models/test_HPoptimizer.py
rename to tests/unit/models/test_HPoptimizer.py
diff --git a/tests/test_classifier.py b/tests/unit/models/test_classifier.py
similarity index 100%
rename from tests/test_classifier.py
rename to tests/unit/models/test_classifier.py
diff --git a/tests/models/test_training.py b/tests/unit/models/test_training.py
similarity index 100%
rename from tests/models/test_training.py
rename to tests/unit/models/test_training.py
diff --git a/tests/models/utils_training_hpo.py b/tests/unit/models/utils_training_hpo.py
similarity index 100%
rename from tests/models/utils_training_hpo.py
rename to tests/unit/models/utils_training_hpo.py

From 4c0f0bca27b7e0ef0209dc21798548781d339afa Mon Sep 17 00:00:00 2001
From: Sinan <sinan_ceviker@yahoo.com>
Date: Sat, 24 May 2025 22:53:32 +0200
Subject: [PATCH 11/32] started repolishing namings and structure of data
 pipeline for final structure

---
 recaptcha_classifier/data/__init__.py       | 15 ++++-----
 recaptcha_classifier/data/augment.py        | 14 ++++-----
 recaptcha_classifier/data/collate_batch.py  |  7 +----
 recaptcha_classifier/data/dataset.py        |  4 +--
 recaptcha_classifier/data/downloader.py     | 19 +++++++-----
 recaptcha_classifier/data/loader_factory.py |  6 ++--
 recaptcha_classifier/data/pair_loader.py    |  6 ++--
 recaptcha_classifier/data/splitter.py       | 14 ++++-----
 recaptcha_classifier/data/types.py          | 34 +++++++--------------
 recaptcha_classifier/data/visualizer.py     | 10 +++---
 recaptcha_classifier/detection_labels.py    |  7 -----
 11 files changed, 56 insertions(+), 80 deletions(-)

diff --git a/recaptcha_classifier/data/__init__.py b/recaptcha_classifier/data/__init__.py
index c341ff9f94..c7d918d6f9 100644
--- a/recaptcha_classifier/data/__init__.py
+++ b/recaptcha_classifier/data/__init__.py
@@ -16,11 +16,9 @@
 from .collate_batch import collate_batch
 
 from .types import (
-    FilePair,
-    FilePairList,
-    DatasetSplitDict,
-    BBoxList,
-    DataPair,
+    ImagePathList,
+    DatasetSplitMap,
+    LoadedImg,
     DataItem,
     DataBatch
 )
@@ -43,10 +41,9 @@
     "collate_batch",
     # Types
     "FilePair",
-    "FilePairList",
-    "DatasetSplitDict",
-    "BBoxList",
-    "DataPair",
+    "ImagePathList",
+    "DatasetSplitMap",
+    "LoadedImg",
     "DataItem",
     "DataBatch",
 ]
diff --git a/recaptcha_classifier/data/augment.py b/recaptcha_classifier/data/augment.py
index ffbf127646..8b622db68f 100644
--- a/recaptcha_classifier/data/augment.py
+++ b/recaptcha_classifier/data/augment.py
@@ -3,7 +3,7 @@
 from PIL import Image
 from typing import List
 from .scaler import YOLOScaler
-from .types import DataPair, BBoxList
+from .types import LoadedImg, BBoxList
 
 
 class Augmentation(ABC):
@@ -11,7 +11,7 @@ class Augmentation(ABC):
     @abstractmethod
     def augment(self,
                 image: Image.Image,
-                annotations: List) -> DataPair:
+                annotations: List) -> LoadedImg:
         """
         Apply the transformation of the image and updates the bounding boxes
         if necessary.
@@ -21,7 +21,7 @@ def augment(self,
             annotations (List): List of annotations associated with the image.
 
         Returns:
-            DataPair: The augmented image and the updated
+            LoadedImg: The augmented image and the updated
             annotations.
         """
         pass
@@ -34,7 +34,7 @@ def __init__(self, transforms=[]) -> None:
 
     def apply_transforms(self,
                          image: Image.Image,
-                         annotations: BBoxList) -> DataPair:
+                         annotations: BBoxList) -> LoadedImg:
         """
         Apply all transformations in the pipeline to the image and
         annotations.
@@ -45,7 +45,7 @@ def apply_transforms(self,
             associated with the image.
 
         Returns:
-            DataPair: The augmented image and the updated
+            LoadedImg: The augmented image and the updated
             annotations.
         """
         for transform in self._transforms:
@@ -62,7 +62,7 @@ def __init__(self, p: float = 0.5) -> None:
 
     def augment(self,
                 image: Image.Image,
-                annotations: BBoxList) -> DataPair:
+                annotations: BBoxList) -> LoadedImg:
         flipped = image.transpose(Image.FLIP_LEFT_RIGHT)
         new_annotations = YOLOScaler.scale_for_flip(annotations)
 
@@ -80,7 +80,7 @@ def __init__(self, degrees: float = 30.0, p: float = 0.5) -> None:
 
     def augment(self,
                 image: Image.Image,
-                annotations: BBoxList) -> DataPair:
+                annotations: BBoxList) -> LoadedImg:
         angle = random.uniform(-self._degrees, self._degrees)
 
         rotated = image.rotate(angle)
diff --git a/recaptcha_classifier/data/collate_batch.py b/recaptcha_classifier/data/collate_batch.py
index 55cc67c688..a1f12afab6 100644
--- a/recaptcha_classifier/data/collate_batch.py
+++ b/recaptcha_classifier/data/collate_batch.py
@@ -10,25 +10,20 @@ def collate_batch(batch: List[DataItem]) -> DataBatch:
 
     Args:
         batch (List[DataItem]): A batch of training items; each item is
-        a tuple of format (image tensor, bounding boxes, class index).
+        a tuple of format (image tensor, class index).
 
     Returns:
         Batch: A single tuple containing:
             - images_tensor (Tensor): Batched images of
             shape (batch_size, 3, H, W)
-            - bboxes (List[BBoxList]): A list of
-            bounding boxes for each image
             - labels_tensor (Tensor): A tensor of shape
             containing the class indices.
     """
     images = [item[0] for item in batch]
-    # bboxes = [item[1] for item in batch]
-    # labels = [item[2] for item in batch]
     labels = [item[1] for item in batch]
 
     # Stack them as (3, H, W) tensors
     images_tensor = torch.stack(images)
     labels_tensor = torch.stack(labels)
 
-    # return images_tensor, bboxes, labels_tensor
     return images_tensor, labels_tensor
diff --git a/recaptcha_classifier/data/dataset.py b/recaptcha_classifier/data/dataset.py
index 803b4b6027..f14ad87f0b 100644
--- a/recaptcha_classifier/data/dataset.py
+++ b/recaptcha_classifier/data/dataset.py
@@ -2,7 +2,7 @@
 from torch.utils.data import Dataset
 from .preprocessor import ImagePrep
 from .augment import AugmentationPipeline
-from .types import FilePairList, DataItem
+from .types import ImagePathList, DataItem
 
 
 class ImageDataset(Dataset):
@@ -17,7 +17,7 @@ class ImageDataset(Dataset):
     https://docs.pytorch.org/tutorials/beginner/basics/data_tutorial.html
     """
     def __init__(self,
-                 pairs: FilePairList,
+                 pairs: ImagePathList,
                  preprocessor: ImagePrep,
                  augmentator: Optional[AugmentationPipeline] = None,
                  class_map: dict = {}
diff --git a/recaptcha_classifier/data/downloader.py b/recaptcha_classifier/data/downloader.py
index 0ba4c6a336..e03c87561c 100644
--- a/recaptcha_classifier/data/downloader.py
+++ b/recaptcha_classifier/data/downloader.py
@@ -1,11 +1,9 @@
 import requests
 import zipfile
-import logging
+import shutil
 from pathlib import Path
 from alive_progress import alive_bar
 
-logger = logging.getLogger(__name__)
-
 
 class DatasetDownloader:
     """
@@ -37,11 +35,11 @@ def download(self) -> None:
         If not, downloads and then unzips it.
         """
         if self._is_downloaded():
-            logger.info("Dataset already exists, skipping download.")
+            print("Dataset already exists, skipping download.")
             return
 
         self._prepare_dest()
-        logger.info(f"Downloading {self._url} to {self._dest}...")
+        print(f"Downloading {self._url} to {self._dest}...")
         self._download_zip()
         self._unzip_and_cleanup()
 
@@ -82,7 +80,7 @@ def _download_zip(self) -> None:
             for chunk in resp.iter_content(chunk_size=8192):
                 f.write(chunk)
                 bar(len(chunk))
-        logger.info("Download completed successfully.")
+        print("Download completed successfully.")
 
     def _unzip_and_cleanup(self) -> None:
         """
@@ -92,7 +90,7 @@ def _unzip_and_cleanup(self) -> None:
         Note: this assumes that the downloaded dataset has exactly
         the structure of our selected Kaggle dataset for simplicity.
         """
-        logger.info("Extracting...")
+        print("Extracting...")
         with zipfile.ZipFile(self._zip_path) as z:
             z.extractall(self._dest)
 
@@ -101,5 +99,10 @@ def _unzip_and_cleanup(self) -> None:
             (root / sub).rename(self._dest / sub)
 
         root.rmdir()
+        
+        label_dir = self._dest / "labels"
+        if label_dir.exists() and label_dir.is_dir():
+            shutil.rmtree(label_dir)
+        
         self._zip_path.unlink()
-        print("Extraction and cleanup completed successfully.")
+        print("Extraction and cleanup completed successfully.")
\ No newline at end of file
diff --git a/recaptcha_classifier/data/loader_factory.py b/recaptcha_classifier/data/loader_factory.py
index 26e807c834..d104205b98 100644
--- a/recaptcha_classifier/data/loader_factory.py
+++ b/recaptcha_classifier/data/loader_factory.py
@@ -4,7 +4,7 @@
 from .dataset import ImageDataset
 from .preprocessor import ImagePrep
 from .augment import AugmentationPipeline
-from .types import DatasetSplitDict, FilePairList
+from .types import DatasetSplitMap, ImagePathList
 from .collate_batch import collate_batch
 
 
@@ -39,12 +39,12 @@ def __init__(self,
         self._class_map = class_map
 
     def create_loaders(self,
-                       splits: DatasetSplitDict) -> Dict[str, DataLoader]:
+                       splits: DatasetSplitMap) -> Dict[str, DataLoader]:
         loaders: Dict[str, DataLoader] = {}
 
         for split_name, cls_dict in splits.items():
             # flatten nested dict of pairs
-            flat_pairs: FilePairList = [pair
+            flat_pairs: ImagePathList = [pair
                                         # traversing over classes
                                         for pairs in cls_dict.values()
                                         # traversing over pairs
diff --git a/recaptcha_classifier/data/pair_loader.py b/recaptcha_classifier/data/pair_loader.py
index c8885cbf2f..828727c5d0 100644
--- a/recaptcha_classifier/data/pair_loader.py
+++ b/recaptcha_classifier/data/pair_loader.py
@@ -1,7 +1,7 @@
 import logging
 from pathlib import Path
 from typing import List, Dict
-from .types import ClassFileDict
+from .types import ClassToImgPaths
 
 logger = logging.getLogger(__name__)
 
@@ -33,9 +33,9 @@ def __init__(self,
         self._classes = classes
         self._images_dir = Path(images_dir)
         # self._labels_dir = Path(labels_dir)
-        self._pairs: ClassFileDict = dict()
+        self._pairs: ClassToImgPaths = dict()
 
-    def find_pairs(self) -> ClassFileDict:
+    def find_pairs(self) -> ClassToImgPaths:
         """
         Returns all pairs loaded for the given classes.
         It caches the response after first run.
diff --git a/recaptcha_classifier/data/splitter.py b/recaptcha_classifier/data/splitter.py
index 4e36bbdb76..31416a9274 100644
--- a/recaptcha_classifier/data/splitter.py
+++ b/recaptcha_classifier/data/splitter.py
@@ -1,6 +1,6 @@
 import random
 from typing import Tuple
-from .types import ClassFileDict, DatasetSplitDict, FilePairList
+from .types import ClassToImgPaths, DatasetSplitMap, ImagePathList
 
 
 class DataSplitter:
@@ -29,8 +29,8 @@ def __init__(self,
         self._validate_ratios()
 
     def split(self,
-              pairs_by_class: ClassFileDict
-              ) -> DatasetSplitDict:
+              pairs_by_class: ClassToImgPaths
+              ) -> DatasetSplitMap:
         """
         Splits each class into train, validation, and test sets.
         It shuffles the data if specified and returns a dictionary
@@ -40,7 +40,7 @@ def split(self,
             items (List): List of items to be split.
 
         Returns:
-            DatasetSplitDict: Nested dictionary containing
+            DatasetSplitMap: Nested dictionary containing
             splits for each class.
         """
         splits = {'train': {}, 'val': {}, 'test': {}}
@@ -69,16 +69,16 @@ def _validate_ratios(self) -> None:
         if any(ratio < 0 for ratio in self._ratios):
             raise ValueError("Ratios must be positive.")
 
-    def _shuffle_items(self, items: FilePairList) -> FilePairList:
+    def _shuffle_items(self, items: ImagePathList) -> ImagePathList:
         """
         Returns a shuffled copied version of the items list,
         using seed if provided.
 
         Args:
-            items (FilePairList): List of items to be shuffled.
+            items (ImagePathList): List of items to be shuffled.
 
         Returns:
-            FilePairList: Shuffled list of items.
+            ImagePathList: Shuffled list of items.
         """
         new_items = items.copy()
         rand = random.Random(self._seed)
diff --git a/recaptcha_classifier/data/types.py b/recaptcha_classifier/data/types.py
index f5ad6b42d7..f5537406a1 100644
--- a/recaptcha_classifier/data/types.py
+++ b/recaptcha_classifier/data/types.py
@@ -3,34 +3,22 @@
 from pathlib import Path
 from torch import Tensor
 
-# OBJECT DETECTION TASK CLASSES
-# A (image, label) pair containing their system paths/ locations
-FilePair = Path  # Tuple[Path, Path]
-
-# A list of (image, label) pairs, for more items in the dataset
-FilePairList = List[FilePair]
+# A list of image paths, for more items in the dataset
+ImagePathList = List[Path]
 
 # A dictionary where the keys are class names and
-# the values are lists of (image, label) pairs, elements of that class
-ClassFileDict = Dict[str, FilePairList]
+# the values are lists of all image paths for that class in the dataset
+ClassToImgPaths = Dict[str, ImagePathList]
 
 # A nested dictionary where main keys are train/val/test
-# and the subkeys are class names
-DatasetSplitDict = Dict[str, ClassFileDict]
-
-# YOLO bounding box format (x_center, y_center, width, height)
-BBox = Tuple[float, float, float, float]
-
-# List of bounding boxes for one image from the dataset
-BBoxList = List[BBox]
+# and the subkeys are class names, containing all image paths of that class
+DatasetSplitMap = Dict[str, ClassToImgPaths]
 
-# A dataset item, of (image, annotations), where both features
-# now loaded in memory, instead of Paths like in FilePair
-DataPair = Tuple[Image.Image, BBoxList]
+# A dataset item, where the image is now loaded from disk
+LoadedImg = Image.Image
 
-# A final dataset item, that now contains the image tensor,
-# the bounding boxes and the class id; ready for model training
-DataItem = Tuple[Tensor, BBoxList, int]
+# A final dataset item, that now contains the image tensor, and the class id; ready for model training
+DataItem = Tuple[Tensor, int]
 
 # Output of the dataloader, a batch of data items
-DataBatch = Tuple[Tensor, List[BBoxList], Tensor]
+DataBatch = Tuple[Tensor, Tensor]
diff --git a/recaptcha_classifier/data/visualizer.py b/recaptcha_classifier/data/visualizer.py
index cca973d9ef..b0315c9dea 100644
--- a/recaptcha_classifier/data/visualizer.py
+++ b/recaptcha_classifier/data/visualizer.py
@@ -1,5 +1,5 @@
 import matplotlib.pyplot as plt
-from .types import DatasetSplitDict
+from .types import DatasetSplitMap
 import numpy as np
 
 
@@ -10,12 +10,12 @@ class Visualizer:
     visualization of classes across the splits.
     """
     @classmethod
-    def print_counts(cls, splits: DatasetSplitDict) -> None:
+    def print_counts(cls, splits: DatasetSplitMap) -> None:
         """
         Prints the counts of samples in each class, for each split.
 
         Args:
-            splits (DatasetSplitDict): the dataset splits
+            splits (DatasetSplitMap): the dataset splits
                 containing the pairs for each class.
         """
         for split, cls_dict in splits.items():
@@ -26,14 +26,14 @@ def print_counts(cls, splits: DatasetSplitDict) -> None:
 
     @classmethod
     def plot_splits(cls,
-                    splits: DatasetSplitDict,
+                    splits: DatasetSplitMap,
                     title: str = "Class Distribution in Splits") -> None:
         """
         Plots a bar chart showing the amount and percentage of samples
         present in each class, for each of the splits.
 
         Args:
-            splits (DatasetSplitDict): the dataset splits
+            splits (DatasetSplitMap): the dataset splits
                 containing the pairs for each class.
             title (str): the title of the plot.
         """
diff --git a/recaptcha_classifier/detection_labels.py b/recaptcha_classifier/detection_labels.py
index 5c9fe8872c..2ca79204d7 100644
--- a/recaptcha_classifier/detection_labels.py
+++ b/recaptcha_classifier/detection_labels.py
@@ -5,13 +5,6 @@ class DetectionLabels(Enum):
     """
     Enum for improving readability of the object classes.
     """
-
-    """
-    OBJECT DETECTION TASK CLASSES
-    CROSSWALK = 0
-    CHIMNEY = 1
-    STAIR = 2
-    """
     BICYCLE = 0
     BRIDGE = 1
     BUS = 2

From e799210474afa4abe4bc8f66bcd22306e7bff6f7 Mon Sep 17 00:00:00 2001
From: Sinan <sinan_ceviker@yahoo.com>
Date: Sat, 24 May 2025 23:35:18 +0200
Subject: [PATCH 12/32] revamped download process to cleanup structure of
 dataset

---
 recaptcha_classifier/data/__init__.py     |  2 -
 recaptcha_classifier/data/augment.py      | 31 +++-----
 recaptcha_classifier/data/dataset.py      |  6 --
 recaptcha_classifier/data/downloader.py   | 70 +++++++++++++-----
 recaptcha_classifier/data/pipeline.py     |  2 +-
 recaptcha_classifier/data/preprocessor.py | 23 ------
 recaptcha_classifier/data/scaler.py       | 90 -----------------------
 7 files changed, 62 insertions(+), 162 deletions(-)
 delete mode 100644 recaptcha_classifier/data/scaler.py

diff --git a/recaptcha_classifier/data/__init__.py b/recaptcha_classifier/data/__init__.py
index c7d918d6f9..7fdb9a5551 100644
--- a/recaptcha_classifier/data/__init__.py
+++ b/recaptcha_classifier/data/__init__.py
@@ -11,7 +11,6 @@
     HorizontalFlip,
     RandomRotation
 )
-from .scaler import YOLOScaler
 
 from .collate_batch import collate_batch
 
@@ -36,7 +35,6 @@
     "AugmentationPipeline",
     "HorizontalFlip",
     "RandomRotation",
-    "YOLOScaler",
     # Methods
     "collate_batch",
     # Types
diff --git a/recaptcha_classifier/data/augment.py b/recaptcha_classifier/data/augment.py
index 8b622db68f..ca51e07e3a 100644
--- a/recaptcha_classifier/data/augment.py
+++ b/recaptcha_classifier/data/augment.py
@@ -2,8 +2,7 @@
 from abc import ABC, abstractmethod
 from PIL import Image
 from typing import List
-from .scaler import YOLOScaler
-from .types import LoadedImg, BBoxList
+from .types import LoadedImg
 
 
 class Augmentation(ABC):
@@ -33,15 +32,13 @@ def __init__(self, transforms=[]) -> None:
         self._transforms: List[Augmentation] = transforms
 
     def apply_transforms(self,
-                         image: Image.Image,
-                         annotations: BBoxList) -> LoadedImg:
+                         image: Image.Image) -> LoadedImg:
         """
         Apply all transformations in the pipeline to the image and
         annotations.
 
         Args:
             image (Image.Image): The image to be augmented.
-            annotations (BBoxList): List of annotations
             associated with the image.
 
         Returns:
@@ -51,43 +48,33 @@ def apply_transforms(self,
         for transform in self._transforms:
             if hasattr(transform, 'prob') and random.random() > transform.prob:
                 continue
-            image, annotations = transform.augment(image, annotations)
+            image, annotations = transform.augment(image)
         return image, annotations
 
 
 class HorizontalFlip(Augmentation):
-    """Flips the image horizontally, with probability p and updates bboxes."""
+    """Flips the image horizontally, with probability p."""
     def __init__(self, p: float = 0.5) -> None:
         self.prob = p
 
     def augment(self,
-                image: Image.Image,
-                annotations: BBoxList) -> LoadedImg:
+                image: Image.Image) -> LoadedImg:
         flipped = image.transpose(Image.FLIP_LEFT_RIGHT)
-        new_annotations = YOLOScaler.scale_for_flip(annotations)
 
-        return flipped, new_annotations
+        return flipped
 
 
 class RandomRotation(Augmentation):
     """
-    Rotates the image by a random angle,
-    also updates bboxes to reflect the rotation.
+    Rotates the image by a random angle.
     """
     def __init__(self, degrees: float = 30.0, p: float = 0.5) -> None:
         self._degrees = degrees
         self.prob = p
 
     def augment(self,
-                image: Image.Image,
-                annotations: BBoxList) -> LoadedImg:
+                image: Image.Image) -> LoadedImg:
         angle = random.uniform(-self._degrees, self._degrees)
 
         rotated = image.rotate(angle)
-        new_annotations = (YOLOScaler
-                           .scale_for_rotation(annotations,
-                                               angle,
-                                               image.size)
-                           )
-
-        return rotated, new_annotations
+        return rotated
diff --git a/recaptcha_classifier/data/dataset.py b/recaptcha_classifier/data/dataset.py
index f14ad87f0b..8adda5a9a8 100644
--- a/recaptcha_classifier/data/dataset.py
+++ b/recaptcha_classifier/data/dataset.py
@@ -52,10 +52,6 @@ def __getitem__(self,
 
         # Load image and label
         img = self._prep.load_image(img_path)
-        # bb = self._prep.load_labels(lbl_path)
-
-        # if not bb:
-        #    raise ValueError(f"Bounding box list is empty for {lbl_path}")
 
         # Apply augmentation if passed
         if self._aug:
@@ -71,6 +67,4 @@ def __getitem__(self,
             raise KeyError(f"Class name '{c_name}' not found in classes.")
         c_id = self._class_map[c_name]
 
-        # Return image tensor, bounding box and class index
         return tensor, self._prep.class_id_to_tensor(c_id)
-        # return tensor, bb, c_id
diff --git a/recaptcha_classifier/data/downloader.py b/recaptcha_classifier/data/downloader.py
index e03c87561c..1e597b6b7e 100644
--- a/recaptcha_classifier/data/downloader.py
+++ b/recaptcha_classifier/data/downloader.py
@@ -3,6 +3,7 @@
 import shutil
 from pathlib import Path
 from alive_progress import alive_bar
+from recaptcha_classifier.detection_labels import DetectionLabels
 
 
 class DatasetDownloader:
@@ -28,6 +29,7 @@ def __init__(self,
         self._dest: Path = Path(dest)
         self._zip_path: Path = self._dest / "dataset.zip"
         self._progress = alive_bar
+        self._expected_folder_names = DetectionLabels.dataset_classnames()
 
     def download(self) -> None:
         """
@@ -41,16 +43,25 @@ def download(self) -> None:
         self._prepare_dest()
         print(f"Downloading {self._url} to {self._dest}...")
         self._download_zip()
-        self._unzip_and_cleanup()
+        self._extract_zip()
+        self._move_subfolders()
+        self._delete_labels()
+        self._flatten_images_folder()
+        self._zip_path.unlink()
+        print("Download and extraction completed successfully.")
 
     def _is_downloaded(self) -> bool:
         """
-        Checks if the dataset is already downloaded.
+        Checks if the dataset is already downloaded and in the expected format.
 
         Returns:
             bool: True if the dataset is already downloaded, False otherwise.
         """
-        return self._dest.exists() and any(self._dest.iterdir())
+        if not self._dest.exists():
+            return False
+        folders = {p.name for p in self._dest.iterdir() if p.is_dir()}
+        expected = set(self._expected_folder_names)
+        return expected.issubset(folders)
 
     def _prepare_dest(self) -> None:
         """
@@ -80,29 +91,52 @@ def _download_zip(self) -> None:
             for chunk in resp.iter_content(chunk_size=8192):
                 f.write(chunk)
                 bar(len(chunk))
-        print("Download completed successfully.")
-
-    def _unzip_and_cleanup(self) -> None:
+    
+    def _extract_zip(self) -> None:
         """
-        Unzips the downloaded dataset.
-        Then it cleans up the zip file and its main extracted directory.
-
-        Note: this assumes that the downloaded dataset has exactly
-        the structure of our selected Kaggle dataset for simplicity.
+        Extracts the downloaded zip file to the destination directory.
         """
-        print("Extracting...")
         with zipfile.ZipFile(self._zip_path) as z:
             z.extractall(self._dest)
 
-        root = next(p for p in self._dest.iterdir() if p.is_dir())
+    def _move_subfolders(self) -> None:
+        """
+        Moves the main images and labels subfolders
+        from the extracted directory to the destination directory.
+        Finally, it removes the main extracted directory that is now empty.
+        """
+        root = next(p for p in self._dest.iterdir() if p.is_dir() and p.name not in self._expected_folder_names)
+        
         for sub in ("images", "labels"):
-            (root / sub).rename(self._dest / sub)
-
-        root.rmdir()
+            source = root / sub
+            if source.exists():
+                target = self._dest / sub
+                if target.exists():
+                    shutil.rmtree(target)
+                source.rename(target)
         
+        if root.exists() and root.is_dir():
+            root.rmdir()
+    
+    def _delete_labels(self) -> None:
+        """
+        Deletes the labels directory if it exists.
+        """
         label_dir = self._dest / "labels"
         if label_dir.exists() and label_dir.is_dir():
             shutil.rmtree(label_dir)
+    
+    def _flatten_images_folder(self) -> None:
+        images_dir = self._dest / "images"
+        if not images_dir.exists():
+            return
+
+        for subfolder in images_dir.iterdir():
+            if subfolder.is_dir():
+                target_path = self._dest / subfolder.name
+                if target_path.exists():
+                    shutil.rmtree(target_path)
+                subfolder.rename(target_path)
         
-        self._zip_path.unlink()
-        print("Extraction and cleanup completed successfully.")
\ No newline at end of file
+        if not any(images_dir.iterdir()):
+            images_dir.rmdir()
diff --git a/recaptcha_classifier/data/pipeline.py b/recaptcha_classifier/data/pipeline.py
index a2949c67c6..961e44a513 100644
--- a/recaptcha_classifier/data/pipeline.py
+++ b/recaptcha_classifier/data/pipeline.py
@@ -114,4 +114,4 @@ def run(self) -> Dict[str, DataLoader]:
         # 4. Create DataLoaders for each split
         print("e. Creating DataLoaders for each split...")
         loaders = self._creator.create_loaders(splits)
-        return loaders
+        return loaders
\ No newline at end of file
diff --git a/recaptcha_classifier/data/preprocessor.py b/recaptcha_classifier/data/preprocessor.py
index c171503a2e..51cd4dd0ea 100644
--- a/recaptcha_classifier/data/preprocessor.py
+++ b/recaptcha_classifier/data/preprocessor.py
@@ -3,7 +3,6 @@
 from PIL import Image
 import numpy as np
 import torch
-from .types import BBoxList
 
 
 class ImagePrep:
@@ -39,28 +38,6 @@ def load_image(self, img_path: Path) -> Image.Image:
         loaded = Image.open(img_path).convert("RGB")
         return self._resize(loaded)
 
-    def load_labels(self, lbl_path: Path) -> BBoxList:
-        """
-        Parses the list of YOLO format labels from the file at the given path.
-
-        Args:
-            lbl_path (Path): Path to the label file.
-
-        Returns:
-            BBoxList: List of bounding boxes in YOLO format
-            (x_center, y_center, width, height).
-        """
-        bounding_boxes = []
-        with open(lbl_path, "r") as f:
-            for line in f:
-                parts = line.strip().split()
-                if len(parts) < 5:
-                    continue  # invalid line skipped
-                _, x_center, y_center, width, height = map(float, parts)
-                bounding_boxes.append((x_center, y_center, width, height))
-
-        return bounding_boxes
-
     def _resize(self, img: Image.Image) -> Image.Image:
         """
         Resizes the image to the target size.
diff --git a/recaptcha_classifier/data/scaler.py b/recaptcha_classifier/data/scaler.py
deleted file mode 100644
index f35a303125..0000000000
--- a/recaptcha_classifier/data/scaler.py
+++ /dev/null
@@ -1,90 +0,0 @@
-from typing import Tuple
-from .types import BBoxList
-
-
-class YOLOScaler:
-    """
-    This class is responsible for scaling the YOLO format bounding boxes
-    based on the transform applied to the image. It is only
-    used inside the AugmentationPipeline class, to adjust
-    the coordinates of the bounding boxes after applying
-    transformations to the image.
-    """
-    @staticmethod
-    def scale_for_flip(bboxes: BBoxList) -> BBoxList:
-        """
-        Adjusts the bounding boxes for horizontal flip.
-
-        Args:
-            bboxes (BBoxList): List of bounding boxes in YOLO format
-            (x_center, y_center, width, height).
-
-        Returns:
-            BBoxList: List of scaled bounding boxes.
-        """
-        return [(1 - x, y, w, h) for (x, y, w, h) in bboxes]
-
-    @staticmethod
-    def scale_for_rotation(bboxes: BBoxList,
-                           angle: float,
-                           size: Tuple[int, int]) -> BBoxList:
-        """
-        Adjusts the bounding boxes for rotation.
-
-        Args:
-            bboxes (BBoxList): List of bounding boxes in YOLO format
-            (x_center, y_center, width, height).
-            angle (float): Angle of rotation.
-            size (Tuple[int, int]): Size of the image. (width, height)
-
-        Returns:
-            BBoxList: List of scaled bounding boxes.
-        """
-        import math
-        width, height = size
-        angle_rad = math.radians(angle)
-        c_x, c_y = width / 2, height / 2  # center coordinates of the image
-
-        n_ann = []  # the new bounding boxes, that we will return
-
-        for x, y, w, h in bboxes:
-            # calculate pixel coordinates from bb
-            x0, y0 = x * width, y * height
-            bw, bh = w * width, h * height
-
-            # calculate corner coordinates
-            corners = [
-                (x0 - bw / 2, y0 - bh / 2),
-                (x0 + bw / 2, y0 - bh / 2),
-                (x0 + bw / 2, y0 + bh / 2),
-                (x0 - bw / 2, y0 + bh / 2)
-            ]
-
-            # rotate the corners
-            new_corners = []
-            for cx, cy in corners:
-                # rotate the corners around the center of the image
-                # formula at https://en.wikipedia.org/wiki/Rotation_matrix
-                x_rot = (math.cos(angle_rad) * (cx - c_x) -
-                         math.sin(angle_rad) * (cy - c_y) +
-                         c_x)
-                y_rot = (math.sin(angle_rad) * (cx - c_x) +
-                         math.cos(angle_rad) * (cy - c_y) +
-                         c_y)
-
-                new_corners.append((x_rot, y_rot))
-
-            # calculate new bounding box
-            x_min = min(c[0] for c in new_corners)
-            x_max = max(c[0] for c in new_corners)
-            y_min = min(c[1] for c in new_corners)
-            y_max = max(c[1] for c in new_corners)
-
-            new_x = max(0, min(1, (x_min + x_max) / (2 * width)))
-            new_y = max(0, min(1, (y_min + y_max) / (2 * height)))
-            new_w = max(0, min(1, (x_max - x_min) / width))
-            new_h = max(0, min(1, (y_max - y_min) / height))
-
-            n_ann.append((new_x, new_y, new_w, new_h))
-
-        return n_ann

From 799d3142387050ded9ab368a1536d888481254b3 Mon Sep 17 00:00:00 2001
From: Sinan <sinan_ceviker@yahoo.com>
Date: Sun, 25 May 2025 00:01:21 +0200
Subject: [PATCH 13/32] polished some more types and docs, given the uodates

---
 main.py                                   |   1 -
 recaptcha_classifier/data/__init__.py     |   4 +-
 recaptcha_classifier/data/downloader.py   |   5 +-
 recaptcha_classifier/data/pair_loader.py  | 121 ----------------------
 recaptcha_classifier/data/paths_loader.py |  93 +++++++++++++++++
 recaptcha_classifier/data/pipeline.py     |  23 ++--
 recaptcha_classifier/data/splitter.py     |   3 +-
 recaptcha_classifier/data/visualizer.py   |  44 ++------
 recaptcha_classifier/detection_labels.py  |   2 +-
 tests/data/notyet-test_pair_loader.py     |  16 +--
 10 files changed, 128 insertions(+), 184 deletions(-)
 delete mode 100644 recaptcha_classifier/data/pair_loader.py
 create mode 100644 recaptcha_classifier/data/paths_loader.py

diff --git a/main.py b/main.py
index 5c508c1aa1..d9061c3a73 100644
--- a/main.py
+++ b/main.py
@@ -71,6 +71,5 @@ def open_api():
     # opens endpoint at http://localhost:8000/
     uvicorn.run("recaptcha_classifier.server.api:app", reload=True)
 
-
 if __name__ == '__main__':
     main()
\ No newline at end of file
diff --git a/recaptcha_classifier/data/__init__.py b/recaptcha_classifier/data/__init__.py
index 7fdb9a5551..b4dde44d2b 100644
--- a/recaptcha_classifier/data/__init__.py
+++ b/recaptcha_classifier/data/__init__.py
@@ -1,6 +1,6 @@
 from .pipeline import DataPreprocessingPipeline
 from .downloader import DatasetDownloader
-from .pair_loader import ImageLabelLoader
+from .paths_loader import ImagePathsLoader
 from .splitter import DataSplitter
 from .visualizer import Visualizer
 from .loader_factory import LoaderFactory
@@ -26,7 +26,7 @@
     # Classes
     "DataPreprocessingPipeline",
     "DatasetDownloader",
-    "ImageLabelLoader",
+    "ImagePathsLoader",
     "DataSplitter",
     "Visualizer",
     "LoaderFactory",
diff --git a/recaptcha_classifier/data/downloader.py b/recaptcha_classifier/data/downloader.py
index 1e597b6b7e..219c7f25f0 100644
--- a/recaptcha_classifier/data/downloader.py
+++ b/recaptcha_classifier/data/downloader.py
@@ -2,8 +2,8 @@
 import zipfile
 import shutil
 from pathlib import Path
+from typing import List
 from alive_progress import alive_bar
-from recaptcha_classifier.detection_labels import DetectionLabels
 
 
 class DatasetDownloader:
@@ -15,6 +15,7 @@ class DatasetDownloader:
     dataset downloading operation, with no other responsibilities.
     """
     def __init__(self,
+                 class_names: List[str],
                  url: str = ("https://www.kaggle.com/api/v1/datasets/"
                              "download/mikhailma/test-dataset"),
                  dest: str = "data") -> None:
@@ -29,7 +30,7 @@ def __init__(self,
         self._dest: Path = Path(dest)
         self._zip_path: Path = self._dest / "dataset.zip"
         self._progress = alive_bar
-        self._expected_folder_names = DetectionLabels.dataset_classnames()
+        self._expected_folder_names = class_names
 
     def download(self) -> None:
         """
diff --git a/recaptcha_classifier/data/pair_loader.py b/recaptcha_classifier/data/pair_loader.py
deleted file mode 100644
index 828727c5d0..0000000000
--- a/recaptcha_classifier/data/pair_loader.py
+++ /dev/null
@@ -1,121 +0,0 @@
-import logging
-from pathlib import Path
-from typing import List, Dict
-from .types import ClassToImgPaths
-
-logger = logging.getLogger(__name__)
-
-
-class ImageLabelLoader:
-    """
-    This class loads all image-label pairs for given classes.
-
-    It scans for all matching images and labels and caches the result.
-
-    It follows Single Responsibility Principle (SRP) as it only handles
-    the loading of the pairs. Also, it uses the Iterator pattern, as
-    it can be looped over to get the list pairs as tuples by class,
-    in format (class, [(img_path, lbl_path), ...].)
-    """
-    def __init__(self,
-                 classes: List[str],
-                 images_dir: str = "data/images",
-                 # labels_dir: str = "data/labels"
-                 ) -> None:
-        """
-        Initializes the PairsLoader instance.
-
-        Args:
-            classes (List[str]): List of class names to load.
-            images_dir (str): Path to the directory containing images.
-            labels_dir (str): Path to the directory containing labels.
-        """
-        self._classes = classes
-        self._images_dir = Path(images_dir)
-        # self._labels_dir = Path(labels_dir)
-        self._pairs: ClassToImgPaths = dict()
-
-    def find_pairs(self) -> ClassToImgPaths:
-        """
-        Returns all pairs loaded for the given classes.
-        It caches the response after first run.
-
-        Returns:
-            List[Tuple[Path, Path]]: List of tuples
-            containing image and label paths. (img_path, lbl_path)
-        """
-        if not self._pairs:
-            self._load_pairs()
-        return self._pairs
-
-    def __iter__(self):
-        """
-        Iterates over classes and their respective list of pairs.
-
-        Yields:
-            Tuple[str, List[Tuple[Path, Path]]]: Class name and list of
-            tuples containing image and label paths.
-        """
-        for cls, pairs in self.find_pairs().items():
-            yield cls, pairs
-
-    def __len__(self) -> int:
-        """
-        Returns total number of matched pairs.
-
-        Returns:
-            int: Number of matched pairs.
-        """
-        return sum(len(pairs) for pairs in self.find_pairs().values())
-
-    def class_count(self) -> Dict[str, int]:
-        """
-        Returns a dictionary with the count of pairs for each class.
-        The keys are class names and the values are the counts.
-
-        Returns:
-            Dict[str, int]: Dictionary with class names as keys and
-            counts as values.
-        """
-        return {cls: len(pairs) for cls, pairs in self.find_pairs().items()}
-
-    def _load_pairs(self) -> None:
-        """
-        Private method to scan directories of given classes and match all
-        available image-label pairs.
-        It ignores images with missing labels.
-        """
-        total_count = 0
-        for cls in self._classes:
-            img_dir = self._images_dir / cls
-            # lbl_dir = self._labels_dir / cls
-            # skipped, cls_count = 0, 0
-
-            if not Path.is_dir(img_dir):  # or not Path.exists(lbl_dir)
-                logger.info(f"Warning: Missing folder for {cls}. Skipping.")
-                continue
-
-            self._pairs[cls] = list(img_dir.glob("*.png"))  # []
-            N = len(self._pairs[cls])
-            logger.info(f"Found {N} images in {cls}.")
-            total_count += N
-            """
-            for img_path in img_dir.glob("*.png"):
-                lbl_path = lbl_dir / img_path.name.replace(".png", ".txt")
-                cls_count += 1
-
-                if not Path.exists(lbl_path):
-                    skipped += 1
-                    continue
-
-                self._pairs[cls].append((img_path, lbl_path))
-
-
-            print(f"Loaded {cls_count - skipped} image-label pairs for {cls}.")
-            if skipped > 0:
-                print(f"Warning: {skipped} missing labels in {cls}. Skipped.")
-
-            total_count += cls_count - skipped
-            """
-
-        print(f"Total pairs loaded: {total_count}")
diff --git a/recaptcha_classifier/data/paths_loader.py b/recaptcha_classifier/data/paths_loader.py
new file mode 100644
index 0000000000..712e09ad90
--- /dev/null
+++ b/recaptcha_classifier/data/paths_loader.py
@@ -0,0 +1,93 @@
+from pathlib import Path
+from typing import List, Dict
+from .types import ClassToImgPaths
+
+
+class ImagePathsLoader:
+    """
+    This class loads all image paths for given classes.
+
+    It scans for all matching images and caches the result.
+
+    It follows Single Responsibility Principle (SRP) as it only handles
+    the loading of the image paths. Also, it uses the Iterator pattern, as
+    it can be looped over to get the list paths as tuples by class,
+    in format (class, [img_path1, ...]).
+    """
+    def __init__(self,
+                 classes: List[str],
+                 images_dir: str = "data") -> None:
+        """
+        Initializes the ImagePathsLoader instance.
+
+        Args:
+            classes (List[str]): List of class names to load.
+            images_dir (str): Path to the directory containing images.
+        """
+        self._classes = classes
+        self._images_dir = Path(images_dir)
+        self._paths: ClassToImgPaths = dict()
+
+    def find_image_paths(self) -> ClassToImgPaths:
+        """
+        Returns all image paths loaded for the given classes.
+        It caches the response after first run.
+
+        Returns:
+            ClassToImgPaths: Dictionary mapping class names to lists
+            of image paths.
+        """
+        if not self._paths:
+            self._load_pairs()
+        return self._paths
+
+    def __iter__(self):
+        """
+        Iterates over classes and their respective list of pairs.
+
+        Yields:
+            Tuple[str, List[Path]]: Class name and list of
+            image paths.
+        """
+        for cls, pairs in self.find_image_paths().items():
+            yield cls, pairs
+
+    def __len__(self) -> int:
+        """
+        Returns total number of matched pairs.
+
+        Returns:
+            int: Number of matched pairs.
+        """
+        return sum(len(pairs) for pairs in self.find_image_paths().values())
+
+    def class_count(self) -> Dict[str, int]:
+        """
+        Returns a dictionary with the count of pairs for each class.
+        The keys are class names and the values are the counts.
+
+        Returns:
+            Dict[str, int]: Dictionary with class names as keys and
+            counts as values.
+        """
+        return {cls: len(pairs) for cls, pairs in self.find_image_paths().items()}
+
+    def _load_pairs(self) -> None:
+        """
+        Private method to scan directories of given classes and match all
+        available image paths.
+        """
+        total_count = 0
+        for cls in self._classes:
+            img_dir = self._images_dir / cls
+
+            if not Path.is_dir(img_dir):
+                print(f"Warning: Missing folder for {cls}. Skipping.")
+                continue
+
+            self._paths[cls] = sorted(img_dir.glob("*.png"))
+            N = len(self._paths[cls])
+            print(f"Found {N} images in {cls}.")
+            total_count += N
+
+        print(f"Total image paths found: {total_count}")
diff --git a/recaptcha_classifier/data/pipeline.py b/recaptcha_classifier/data/pipeline.py
index 961e44a513..b9082fec72 100644
--- a/recaptcha_classifier/data/pipeline.py
+++ b/recaptcha_classifier/data/pipeline.py
@@ -1,8 +1,8 @@
 from typing import Dict, Tuple
 from torch.utils.data import DataLoader
-
+from enum import EnumMeta
 from .downloader import DatasetDownloader
-from .pair_loader import ImageLabelLoader
+from .paths_loader import ImagePathsLoader
 from .splitter import DataSplitter
 from .visualizer import Visualizer
 from .preprocessor import ImagePrep
@@ -33,7 +33,7 @@ class DataPreprocessingPipeline:
     pipeline and it includes all its components.
     """
     def __init__(self,
-                 class_map: Dict[str, int],
+                 class_enum: EnumMeta,
                  ratios: Tuple[float, float, float] = (0.7, 0.2, 0.1),
                  seed: int = 23,  # our group number
                  batch_size: int = 32,
@@ -44,7 +44,7 @@ def __init__(self,
         Initializes the DataPreprocessingPipeline with the given parameters.
 
         Args:
-            class_map (Dict[str, int]): Mappng of class names to indices.
+            class_enum (EnumMeta): Enum class containing dataset classes.
             ratios (Tuple[float, float, float]): Ratios for train,
             val, and test splits.
             seed (int): Random seed for reproducibility.
@@ -53,14 +53,16 @@ def __init__(self,
             balance (bool): Whether to balance the dataset.
             show_plots (bool): Whether to show plots.
         """
-        self._downloader = DatasetDownloader()
-        self._loader = ImageLabelLoader(list(class_map.keys()))
+        self._class_enum = class_enum
+        self._downloader = DatasetDownloader(self._class_enum
+                                             .dataset_classnames())
+        self._loader = ImagePathsLoader(self._class_enum.dataset_classnames())
         self._splitter = DataSplitter(ratios, seed=seed)
         self._show_plots = show_plots
         self._preproc = ImagePrep()
         self._augment = self._build_augmentator()
         self._creator = LoaderFactory(
-            class_map=class_map,
+            class_map=self._class_enum.to_class_map(),
             preprocessor=self._preproc,
             augmentator=self._augment,
             batch_size=batch_size,
@@ -95,7 +97,7 @@ def run(self) -> Dict[str, DataLoader]:
 
         # 2. Finds all pairs of images and YOLO annotations from the dataset
         print("b. Searching for all the data...")
-        pairs_by_class = self._loader.find_pairs()
+        pairs_by_class = self._loader.find_image_paths()
 
         # 3a. Splits the data into train, val, and test sets
         print("c. Splitting the data...")
@@ -113,5 +115,6 @@ def run(self) -> Dict[str, DataLoader]:
 
         # 4. Create DataLoaders for each split
         print("e. Creating DataLoaders for each split...")
-        loaders = self._creator.create_loaders(splits)
-        return loaders
\ No newline at end of file
+        #loaders = self._creator.create_loaders(splits)
+        #return loaders
+        return []
\ No newline at end of file
diff --git a/recaptcha_classifier/data/splitter.py b/recaptcha_classifier/data/splitter.py
index 31416a9274..dcba9fd3b3 100644
--- a/recaptcha_classifier/data/splitter.py
+++ b/recaptcha_classifier/data/splitter.py
@@ -15,7 +15,8 @@ class DataSplitter:
     def __init__(self,
                  ratios: Tuple[float, float, float] = (0.7, 0.2, 0.1),
                  shuffle: bool = True,
-                 seed: int = None) -> None:
+                 seed: int = 23 # our group number
+                 ) -> None:
         """
         Args:
             ratios (Tuple[float, float, float]): Ratios for
diff --git a/recaptcha_classifier/data/visualizer.py b/recaptcha_classifier/data/visualizer.py
index b0315c9dea..6a95de7c33 100644
--- a/recaptcha_classifier/data/visualizer.py
+++ b/recaptcha_classifier/data/visualizer.py
@@ -16,12 +16,12 @@ def print_counts(cls, splits: DatasetSplitMap) -> None:
 
         Args:
             splits (DatasetSplitMap): the dataset splits
-                containing the pairs for each class.
+                containing the items for each class.
         """
         for split, cls_dict in splits.items():
             print(f"{split.upper()}:")
-            for cls, pairs in cls_dict.items():
-                print(f"  {cls:5s}: {len(pairs)}")
+            for cls, items in cls_dict.items():
+                print(f"  {cls:5s}: {len(items)}")
             print()
 
     @classmethod
@@ -34,7 +34,7 @@ def plot_splits(cls,
 
         Args:
             splits (DatasetSplitMap): the dataset splits
-                containing the pairs for each class.
+                containing the items for each class.
             title (str): the title of the plot.
         """
         classes = list(splits['train'].keys())
@@ -51,40 +51,8 @@ def plot_splits(cls,
         # total for each class, simply adding counts of each split
         totals = [t+v+te for t, v, te in
                   zip(counts_train, counts_val, counts_test)]
-
-        """
-        # drawing bars
-        train_bars = plt.bar([i - width for i in x],
-                             counts_train,
-                             width,
-                             label='Train')
-        val_bars = plt.bar(x, counts_val, width, label='Val')
-        test_bars = plt.bar([i + width for i in x],
-                            counts_test,
-                            width,
-                            label='Test')
-
-        # adding perc labels on top of each of the bar
-        for bars, counts in [(train_bars, counts_train),
-                             (val_bars, counts_val),
-                             (test_bars, counts_test)]:
-            for bar, count, total in zip(bars, counts, totals):
-                perc = count / total * 100
-                plt.text(
-                    bar.get_x() + bar.get_width() / 2,  # middle of the bar,
-                    bar.get_height(),  # on top of the bar,
-                    f'{perc:.1f}%',  # percentage, formatted to 1 decimal
-                    ha='center', va='bottom'
-                )
-
-        plt.xticks(x, classes)
-        plt.ylabel('No. of Samples')
-        plt.title(title)
-        plt.legend()
-        plt.tight_layout()
-        plt.show()
-        """
-        fig, ax = plt.subplots(figsize=(num_classes, 6))
+        
+        _, ax = plt.subplots(figsize=(num_classes, 6))
         bar1 = ax.bar(x - width, counts_train, width, label='Train')
         bar2 = ax.bar(x, counts_val, width, label='Val')
         bar3 = ax.bar(x + width, counts_test, width, label='Test')
diff --git a/recaptcha_classifier/detection_labels.py b/recaptcha_classifier/detection_labels.py
index 2ca79204d7..1cbb610f0a 100644
--- a/recaptcha_classifier/detection_labels.py
+++ b/recaptcha_classifier/detection_labels.py
@@ -83,5 +83,5 @@ def dataset_classnames(cls) -> list:
         Returns:
             list: List of class names.
         """
-        return [name.capitalize().replace("_", " ")
+        return [name.replace("_", " ").title()
                 for name in cls.__members__.keys()]
diff --git a/tests/data/notyet-test_pair_loader.py b/tests/data/notyet-test_pair_loader.py
index 987cdf3af3..e7ae89a983 100644
--- a/tests/data/notyet-test_pair_loader.py
+++ b/tests/data/notyet-test_pair_loader.py
@@ -2,10 +2,10 @@
 from pathlib import Path
 from unittest.mock import patch
 
-from recaptcha_classifier.data.pair_loader import ImageLabelLoader
+from recaptcha_classifier.data.pair_loader import ImagePathsLoader
 
 
-class TestImageLabelLoader(unittest.TestCase):
+class TestImagePathsLoader(unittest.TestCase):
     @patch("recaptcha_classifier.data.pair_loader.Path.glob")
     @patch("recaptcha_classifier.data.pair_loader.Path.exists",
            return_value=True)
@@ -20,8 +20,8 @@ def test_load_pairs_with_all_labels(self,
             Path("data/images/class1/img2.png"),
         ]  # 2 images found in the png glob
 
-        loader = ImageLabelLoader(["class1"])
-        pairs = loader.find_pairs()
+        loader = ImagePathsLoader(["class1"])
+        pairs = loader.find_image_paths()
 
         expected_pairs = {
             (Path("data/images/class1/img1.png"),
@@ -49,8 +49,8 @@ def test_load_pairs_with_missing_labels(self,
         # label folder exists, img1.txt exists but img2.txt does not
         exists_mock.side_effect = [True, True, False]
 
-        loader = ImageLabelLoader(["class1"])
-        pairs = loader.find_pairs()
+        loader = ImagePathsLoader(["class1"])
+        pairs = loader.find_image_paths()
 
         self.assertIn("class1", pairs)
         expected_pair = {
@@ -65,14 +65,14 @@ def test_load_pairs_with_missing_labels(self,
     @patch("recaptcha_classifier.data.pair_loader.Path.exists")
     @patch("recaptcha_classifier.data.pair_loader.Path.is_dir")
     def test_caching(self, is_dir_mock, exists_mock, glob_mock):
-        loader = ImageLabelLoader(["class1"])
+        loader = ImagePathsLoader(["class1"])
         loader._pairs = {"test": [(Path("test.png"), Path("test.txt"))]}
 
         # if any mocked method is called, then the cache is not used
         for method in (is_dir_mock, exists_mock, glob_mock):
             method.side_effect = AssertionError(f"{method} called, error!")
 
-        pairs = loader.find_pairs()
+        pairs = loader.find_image_paths()
 
         self.assertIs(pairs, loader._pairs)
         self.assertEqual(pairs, {"test": [(Path("test.png"),

From ff9428933b40627f9eb718a10d2d7fa6aae15cc6 Mon Sep 17 00:00:00 2001
From: Sinan <sinan_ceviker@yahoo.com>
Date: Sun, 25 May 2025 00:34:27 +0200
Subject: [PATCH 14/32] started redoing tests

---
 recaptcha_classifier/data/augment.py          | 20 ++++++++----------
 recaptcha_classifier/data/dataset.py          | 16 +++++++-------
 recaptcha_classifier/data/loader_factory.py   | 21 +++++++++----------
 recaptcha_classifier/data/pipeline.py         |  7 +++----
 recaptcha_classifier/detection_labels.py      |  2 +-
 ...notyet-test_augment.py => test_augment.py} | 16 +++-----------
 ...notyet-test_dataset.py => test_dataset.py} | 18 +++++-----------
 ...ader_factory.py => test_loader_factory.py} |  0
 ...est_pair_loader.py => test_pair_loader.py} |  0
 ...t_preprocessor.py => test_preprocessor.py} |  0
 .../{notyet-test_scaler.py => test_scaler.py} |  0
 ...tyet-test_splitter.py => test_splitter.py} |  0
 ...-test_visualizer.py => test_visualizer.py} |  0
 13 files changed, 38 insertions(+), 62 deletions(-)
 rename tests/data/{notyet-test_augment.py => test_augment.py} (66%)
 rename tests/data/{notyet-test_dataset.py => test_dataset.py} (71%)
 rename tests/data/{notyet-test_loader_factory.py => test_loader_factory.py} (100%)
 rename tests/data/{notyet-test_pair_loader.py => test_pair_loader.py} (100%)
 rename tests/data/{notyet-test_preprocessor.py => test_preprocessor.py} (100%)
 rename tests/data/{notyet-test_scaler.py => test_scaler.py} (100%)
 rename tests/data/{notyet-test_splitter.py => test_splitter.py} (100%)
 rename tests/data/{notyet-test_visualizer.py => test_visualizer.py} (100%)

diff --git a/recaptcha_classifier/data/augment.py b/recaptcha_classifier/data/augment.py
index ca51e07e3a..1dcef38bbf 100644
--- a/recaptcha_classifier/data/augment.py
+++ b/recaptcha_classifier/data/augment.py
@@ -9,14 +9,14 @@ class Augmentation(ABC):
     """Abstract class for data augmentation."""
     @abstractmethod
     def augment(self,
-                image: Image.Image,
+                image: LoadedImg,
                 annotations: List) -> LoadedImg:
         """
         Apply the transformation of the image and updates the bounding boxes
         if necessary.
 
         Args:
-            image (Image.Image): The image to be augmented.
+            image (LoadedImg): The image to be augmented.
             annotations (List): List of annotations associated with the image.
 
         Returns:
@@ -32,24 +32,22 @@ def __init__(self, transforms=[]) -> None:
         self._transforms: List[Augmentation] = transforms
 
     def apply_transforms(self,
-                         image: Image.Image) -> LoadedImg:
+                         image: LoadedImg) -> LoadedImg:
         """
         Apply all transformations in the pipeline to the image and
         annotations.
 
         Args:
-            image (Image.Image): The image to be augmented.
-            associated with the image.
+            image (LoadedImg): The image to be augmented.
 
         Returns:
-            LoadedImg: The augmented image and the updated
-            annotations.
+            LoadedImg: The augmented image.
         """
         for transform in self._transforms:
             if hasattr(transform, 'prob') and random.random() > transform.prob:
                 continue
-            image, annotations = transform.augment(image)
-        return image, annotations
+            image = transform.augment(image)
+        return image
 
 
 class HorizontalFlip(Augmentation):
@@ -58,7 +56,7 @@ def __init__(self, p: float = 0.5) -> None:
         self.prob = p
 
     def augment(self,
-                image: Image.Image) -> LoadedImg:
+                image: LoadedImg) -> LoadedImg:
         flipped = image.transpose(Image.FLIP_LEFT_RIGHT)
 
         return flipped
@@ -73,7 +71,7 @@ def __init__(self, degrees: float = 30.0, p: float = 0.5) -> None:
         self.prob = p
 
     def augment(self,
-                image: Image.Image) -> LoadedImg:
+                image: LoadedImg) -> LoadedImg:
         angle = random.uniform(-self._degrees, self._degrees)
 
         rotated = image.rotate(angle)
diff --git a/recaptcha_classifier/data/dataset.py b/recaptcha_classifier/data/dataset.py
index 8adda5a9a8..4d64cccfb7 100644
--- a/recaptcha_classifier/data/dataset.py
+++ b/recaptcha_classifier/data/dataset.py
@@ -7,7 +7,7 @@
 
 class ImageDataset(Dataset):
     """
-    A class to handle the pairs of (image, label) for the dataset.
+    A class to handle the images for the dataset.
     It makes them ready for training, by applying augmentation
     for the training set, preprocessing and makes sure that the
     output format is in PyTorch Tensor format.
@@ -17,7 +17,7 @@ class ImageDataset(Dataset):
     https://docs.pytorch.org/tutorials/beginner/basics/data_tutorial.html
     """
     def __init__(self,
-                 pairs: ImagePathList,
+                 items: ImagePathList,
                  preprocessor: ImagePrep,
                  augmentator: Optional[AugmentationPipeline] = None,
                  class_map: dict = {}
@@ -25,13 +25,13 @@ def __init__(self,
         """
         Initializes the ImageDataset with the given parameters.
         """
-        self._pairs = pairs
+        self._items = items
         self._prep = preprocessor
         self._aug = augmentator
         self._class_map = class_map
 
     def __len__(self) -> int:
-        return len(self._pairs)
+        return len(self._items)
 
     def __getitem__(self,
                     idx: int
@@ -47,16 +47,14 @@ def __getitem__(self,
             preprocessed image in tensor format, the YOLO bound
             box annotations and the label.
         """
-        # img_path, lbl_path = self._pairs[idx]
-        img_path = self._pairs[idx]
+        img_path = self._items[idx]
 
-        # Load image and label
+        # Load image
         img = self._prep.load_image(img_path)
 
         # Apply augmentation if passed
         if self._aug:
-            # img, bb = self._aug.apply_transforms(img, bb)
-            img, _ = self._aug.apply_transforms(img, [])
+            img = self._aug.apply_transforms(img)
 
         # Convert image to tensor
         tensor = self._prep.to_tensor(img)
diff --git a/recaptcha_classifier/data/loader_factory.py b/recaptcha_classifier/data/loader_factory.py
index d104205b98..0aeded526f 100644
--- a/recaptcha_classifier/data/loader_factory.py
+++ b/recaptcha_classifier/data/loader_factory.py
@@ -25,7 +25,7 @@ def __init__(self,
 
         Args:
             class_map (dict): A dictionary mapping class names to indices.
-            preprocessor (ImagePrep): The preprocessor to use.
+            preprocessor (ImagePrep): The preprocessor to use for data.
             augmentator (Optional[AugmentationPipeline]): The augmentator used
             batch_size (int): Batch size for DataLoader.
             num_workers (int): Number of workers for DataLoader.
@@ -43,25 +43,25 @@ def create_loaders(self,
         loaders: Dict[str, DataLoader] = {}
 
         for split_name, cls_dict in splits.items():
-            # flatten nested dict of pairs
-            flat_pairs: ImagePathList = [pair
+            # flatten nested dict of image_paths
+            flat_image_paths: ImagePathList = [image_path
                                         # traversing over classes
-                                        for pairs in cls_dict.values()
-                                        # traversing over pairs
-                                        for pair in pairs
+                                        for image_paths in cls_dict.values()
+                                        # traversing over image_paths
+                                        for image_path in image_paths
                                         ]
 
             # augmentatr only for training set
             augmentator = self._aug if split_name == 'train' else None
 
             dataset = ImageDataset(
-                pairs=flat_pairs,
+                items=flat_image_paths,
                 preprocessor=self._preprocessor,
                 augmentator=augmentator,
                 class_map=self._class_map
             )
 
-            sampler = (self._build_sampler(flat_pairs) if self._balance
+            sampler = (self._build_sampler(flat_image_paths) if self._balance
                        and split_name == "train" else None)
 
             loader = DataLoader(
@@ -77,14 +77,13 @@ def create_loaders(self,
 
         return loaders
 
-    def _build_sampler(self, pairs):
+    def _build_sampler(self, image_paths):
         """
         Builds a sampler for the dataset to balance the classes.
         """
         class_counts = Counter()
         targets = []
-        # for img_path, _ in pairs:
-        for img_path in pairs:
+        for img_path in image_paths:
             cls = img_path.parent.name
             targets.append(self._class_map[cls])
             class_counts[self._class_map[cls]] += 1
diff --git a/recaptcha_classifier/data/pipeline.py b/recaptcha_classifier/data/pipeline.py
index b9082fec72..612328a8f2 100644
--- a/recaptcha_classifier/data/pipeline.py
+++ b/recaptcha_classifier/data/pipeline.py
@@ -39,7 +39,7 @@ def __init__(self,
                  batch_size: int = 32,
                  num_workers: int = 4,
                  balance: bool = False,
-                 show_plots: bool = True) -> None:
+                 show_plots: bool = False) -> None:
         """
         Initializes the DataPreprocessingPipeline with the given parameters.
 
@@ -115,6 +115,5 @@ def run(self) -> Dict[str, DataLoader]:
 
         # 4. Create DataLoaders for each split
         print("e. Creating DataLoaders for each split...")
-        #loaders = self._creator.create_loaders(splits)
-        #return loaders
-        return []
\ No newline at end of file
+        loaders = self._creator.create_loaders(splits)
+        return loaders
\ No newline at end of file
diff --git a/recaptcha_classifier/detection_labels.py b/recaptcha_classifier/detection_labels.py
index 1cbb610f0a..e4ae0311c4 100644
--- a/recaptcha_classifier/detection_labels.py
+++ b/recaptcha_classifier/detection_labels.py
@@ -53,7 +53,7 @@ def to_class_map(cls) -> dict:
         Returns:
             dict: Dictionary representation of the enum.
         """
-        return {cl.name.capitalize().replace("_", " "):
+        return {cl.name.replace("_", " ").title():
                 cl.value for cl in cls}
 
     @classmethod
diff --git a/tests/data/notyet-test_augment.py b/tests/data/test_augment.py
similarity index 66%
rename from tests/data/notyet-test_augment.py
rename to tests/data/test_augment.py
index b153bf66cd..667a2681c8 100644
--- a/tests/data/notyet-test_augment.py
+++ b/tests/data/test_augment.py
@@ -7,7 +7,6 @@
     HorizontalFlip,
     RandomRotation
 )
-from recaptcha_classifier.data.scaler import YOLOScaler
 
 
 class TestAugmentation(unittest.TestCase):
@@ -17,39 +16,30 @@ def setUp(self):
                     (1, 100, 3))
         )
         self.img = self.img.convert("RGB")
-        self.bb = [(0.5, 0.5, 0.2, 0.2)]
 
     def test_horizontal_flip(self):
         aug = HorizontalFlip(p=1.0)
-        flipped_img, flipped_bb = aug.augment(self.img, self.bb)
+        flipped_img = aug.augment(self.img)
 
         self.assertFalse(np.array_equal(np.array(flipped_img),
                                         np.array(self.img)))
 
-        result = YOLOScaler.scale_for_flip(self.bb)
-        self.assertEqual(flipped_bb, result)
-
     @patch('random.uniform', return_value=30)
     def test_random_rotation(self, _):
         augmenter = RandomRotation(degrees=30)
-        rotated_img, rotated_bb = augmenter.augment(self.img, self.bb)
+        rotated_img = augmenter.augment(self.img)
 
         self.assertFalse(np.array_equal(np.array(rotated_img),
                                         np.array(self.img)))
 
-        result = YOLOScaler.scale_for_rotation(self.bb, 30, self.img.size)
-        for i, j in zip(rotated_bb, result):
-            self.assertAlmostEqual(i, j, places=4)
-
     def test_pipeline(self):
         pipeline = AugmentationPipeline()
         pipeline.add_transform(HorizontalFlip(p=1.0))
         pipeline.add_transform(RandomRotation(degrees=30))
 
-        new_img, new_bb = pipeline.apply_transforms(self.img, self.bb)
+        new_img = pipeline.apply_transforms(self.img)
 
         self.assertIsInstance(new_img, Image.Image)
-        self.assertIsInstance(new_bb, list)
 
 
 if __name__ == "__main__":
diff --git a/tests/data/notyet-test_dataset.py b/tests/data/test_dataset.py
similarity index 71%
rename from tests/data/notyet-test_dataset.py
rename to tests/data/test_dataset.py
index 48ce2e0376..9b8bedc5b8 100644
--- a/tests/data/notyet-test_dataset.py
+++ b/tests/data/test_dataset.py
@@ -10,19 +10,16 @@
 
 class TestImageDataset(unittest.TestCase):
     def setUp(self):
-        self.pairs = [(Path("data/images/c1/i1.png"),
-                       Path("data/labels/c1/i1.txt"))]
+        self.images = [Path("data/images/c1/i1.png")]
         self.class_map = {"c1": 0}
         self.preprocessor = ImagePrep()
         self.augmentator = AugmentationPipeline()
 
     @patch.object(ImagePrep, 'load_image')
-    @patch.object(ImagePrep, 'load_labels')
     @patch.object(ImagePrep, 'to_tensor')
-    def test_loading(self, to_tensor_mock, load_labels_mock, load_image_mock):
+    def test_loading(self, to_tensor_mock, load_image_mock):
         # Mock the return values
         load_image_mock.return_value = MagicMock()
-        load_labels_mock.return_value = [(0.1, 0.2, 0.3, 0.4)]
         to_tensor_mock.return_value = torch.rand(3, 224, 224)  # expect shape
 
         dataset = ImageDataset(
@@ -32,18 +29,15 @@ def test_loading(self, to_tensor_mock, load_labels_mock, load_image_mock):
             class_map=self.class_map
         )
 
-        tensor, bboxes, cid = dataset[0]
+        tensor, cid = dataset[0]
 
         self.assertIsInstance(tensor, torch.Tensor)
         self.assertEqual(tensor.shape, (3, 224, 224))
-        self.assertIsInstance(bboxes, list)
-        self.assertEqual(bboxes, [(0.1, 0.2, 0.3, 0.4)])
         self.assertIsInstance(cid, int)
         self.assertEqual(cid, 0)
 
     @patch.object(ImagePrep, 'load_image')
-    @patch.object(ImagePrep, 'load_labels', return_value=[])
-    def test_empty_bb(self, _, load_image_mock):
+    def test_empty_bb(self, load_image_mock):
         load_image_mock.return_value = MagicMock()
         dataset = ImageDataset(
             pairs=self.pairs,
@@ -55,9 +49,7 @@ def test_empty_bb(self, _, load_image_mock):
             dataset[0]
 
     @patch.object(ImagePrep, 'load_image')
-    @patch.object(ImagePrep, 'load_labels',
-                  return_value=[(0.1, 0.2, 0.3, 0.4)])
-    def test_no_class(self, load_labels_mock, load_image_mock):
+    def test_no_class(self, load_image_mock):
         load_image_mock.return_value = np.ones((224, 224, 3))
         dataset = ImageDataset(
             pairs=self.pairs,
diff --git a/tests/data/notyet-test_loader_factory.py b/tests/data/test_loader_factory.py
similarity index 100%
rename from tests/data/notyet-test_loader_factory.py
rename to tests/data/test_loader_factory.py
diff --git a/tests/data/notyet-test_pair_loader.py b/tests/data/test_pair_loader.py
similarity index 100%
rename from tests/data/notyet-test_pair_loader.py
rename to tests/data/test_pair_loader.py
diff --git a/tests/data/notyet-test_preprocessor.py b/tests/data/test_preprocessor.py
similarity index 100%
rename from tests/data/notyet-test_preprocessor.py
rename to tests/data/test_preprocessor.py
diff --git a/tests/data/notyet-test_scaler.py b/tests/data/test_scaler.py
similarity index 100%
rename from tests/data/notyet-test_scaler.py
rename to tests/data/test_scaler.py
diff --git a/tests/data/notyet-test_splitter.py b/tests/data/test_splitter.py
similarity index 100%
rename from tests/data/notyet-test_splitter.py
rename to tests/data/test_splitter.py
diff --git a/tests/data/notyet-test_visualizer.py b/tests/data/test_visualizer.py
similarity index 100%
rename from tests/data/notyet-test_visualizer.py
rename to tests/data/test_visualizer.py

From 379dcbd75323e6c9847d0625debe1bc0c6f97d53 Mon Sep 17 00:00:00 2001
From: Sinan <sinan_ceviker@yahoo.com>
Date: Thu, 29 May 2025 18:57:30 +0200
Subject: [PATCH 15/32] pulled from main

---
 .gitignore                                            | 7 ++-----
 Pipfile                                               | 1 +
 recaptcha_classifier/__init__.py                      | 8 +++++++-
 recaptcha_classifier/detection_labels.py              | 4 +---
 recaptcha_classifier/features/__init__.py             | 5 +++++
 recaptcha_classifier/features/evaluation/__init__.py  | 5 +++++
 recaptcha_classifier/features/evaluation/evaluate.py  | 4 ++--
 recaptcha_classifier/models/main_model/__init__.py    | 5 +++++
 recaptcha_classifier/models/main_model/model_class.py | 9 ++++-----
 recaptcha_classifier/train/__init__.py                | 3 +++
 recaptcha_classifier/train/training.py                | 8 +++++---
 11 files changed, 40 insertions(+), 19 deletions(-)

diff --git a/.gitignore b/.gitignore
index adf0e2c2e9..4a3312fbf8 100644
--- a/.gitignore
+++ b/.gitignore
@@ -5,9 +5,6 @@ __pycache__/
 venv/
 .env
 
-private/
-/data/
-
 /models/
-/simple_classifier_checkpoints/
-/main_classifier_checkpoints/
\ No newline at end of file
+private/
+/data/
\ No newline at end of file
diff --git a/Pipfile b/Pipfile
index b646d315d9..d6ff5dc0ce 100644
--- a/Pipfile
+++ b/Pipfile
@@ -10,6 +10,7 @@ verify_ssl = true
 
 [packages]
 numpy = "*"
+pillow= "*"
 opencv-python = "*"
 pre-commit = "*"
 torch = {index = "pytorch-cu128", version = "2.7.0+cu128", markers = "platform_system == 'Windows'"}
diff --git a/recaptcha_classifier/__init__.py b/recaptcha_classifier/__init__.py
index c1a3c4d72c..61c583b299 100644
--- a/recaptcha_classifier/__init__.py
+++ b/recaptcha_classifier/__init__.py
@@ -1,9 +1,15 @@
 from .models import SimpleCNN
+from .models.main_model import MainCNN
 from .detection_labels import DetectionLabels
 from .data import DataPreprocessingPipeline
+from .train import Trainer
+from .features import evaluate_model
 
 __all__ = [
     "DetectionLabels",
     "DataPreprocessingPipeline",
-    'SimpleCNN'
+    'SimpleCNN',
+    'MainCNN',
+    'Trainer',
+    'evaluate_model'
 ]
\ No newline at end of file
diff --git a/recaptcha_classifier/detection_labels.py b/recaptcha_classifier/detection_labels.py
index e4ae0311c4..f99c72e3ee 100644
--- a/recaptcha_classifier/detection_labels.py
+++ b/recaptcha_classifier/detection_labels.py
@@ -76,9 +76,7 @@ def from_id(cls, id: int) -> str:
     @classmethod
     def dataset_classnames(cls) -> list:
         """
-        Returns a list of class names, only with first letter capitalized.
-        We use it for the pair loader, as that is the format of the folders
-        downloaded from the dataset.
+        Returns a list of class names.
 
         Returns:
             list: List of class names.
diff --git a/recaptcha_classifier/features/__init__.py b/recaptcha_classifier/features/__init__.py
index e69de29bb2..915c534014 100644
--- a/recaptcha_classifier/features/__init__.py
+++ b/recaptcha_classifier/features/__init__.py
@@ -0,0 +1,5 @@
+from .evaluation import evaluate_model
+
+__all__ = [
+    "evaluate_model"
+]
\ No newline at end of file
diff --git a/recaptcha_classifier/features/evaluation/__init__.py b/recaptcha_classifier/features/evaluation/__init__.py
index e69de29bb2..95daef105c 100644
--- a/recaptcha_classifier/features/evaluation/__init__.py
+++ b/recaptcha_classifier/features/evaluation/__init__.py
@@ -0,0 +1,5 @@
+from .evaluate import evaluate_model
+
+__all__ = [
+    "evaluate_model"
+]
\ No newline at end of file
diff --git a/recaptcha_classifier/features/evaluation/evaluate.py b/recaptcha_classifier/features/evaluation/evaluate.py
index 1224a8922a..db998a1c39 100644
--- a/recaptcha_classifier/features/evaluation/evaluate.py
+++ b/recaptcha_classifier/features/evaluation/evaluate.py
@@ -1,4 +1,4 @@
-import recaptcha_classifier.features.evaluation.classification_metrics as cm
+from .classification_metrics import evaluate_classification
 import torch
 from tqdm import tqdm
 from recaptcha_classifier.detection_labels import DetectionLabels
@@ -43,7 +43,7 @@ def evaluate_model(model: torch.nn.Module,
     y_pred = torch.cat(all_preds)
     y_true = torch.cat(all_targets)
 
-    class_results = cm.evaluate_classification(
+    class_results = evaluate_classification(
         y_pred=y_pred,
         y_true=y_true,
         class_names=class_names,
diff --git a/recaptcha_classifier/models/main_model/__init__.py b/recaptcha_classifier/models/main_model/__init__.py
index e69de29bb2..5417bd46f3 100644
--- a/recaptcha_classifier/models/main_model/__init__.py
+++ b/recaptcha_classifier/models/main_model/__init__.py
@@ -0,0 +1,5 @@
+from .model_class import MainCNN
+
+__all__ = [
+    "MainCNN"
+]
\ No newline at end of file
diff --git a/recaptcha_classifier/models/main_model/model_class.py b/recaptcha_classifier/models/main_model/model_class.py
index 96d2b74b01..b24d810212 100644
--- a/recaptcha_classifier/models/main_model/model_class.py
+++ b/recaptcha_classifier/models/main_model/model_class.py
@@ -1,8 +1,10 @@
 import torch
 import torch.nn as nn
 from recaptcha_classifier.constants import INPUT_SHAPE
+from recaptcha_classifier.models.base_model import BaseModel
 
-class MainCNN(nn.Module): # should inherit BaseModel(nn.Module)
+
+class MainCNN(BaseModel):
 
     def __init__(self,
                  n_layers: int,
@@ -64,8 +66,5 @@ def forward(self, x) -> torch.Tensor:
 
         x = x.view(x.size(0), -1)
         x = self.classifier(x)
-        return x
-
-
-
+        return x  # ! return logits, torch.softmax(x, dim=1) if needed for uncertainty
 
diff --git a/recaptcha_classifier/train/__init__.py b/recaptcha_classifier/train/__init__.py
index e69de29bb2..3d6a085185 100644
--- a/recaptcha_classifier/train/__init__.py
+++ b/recaptcha_classifier/train/__init__.py
@@ -0,0 +1,3 @@
+from .training import Trainer
+
+__all__ = ['Trainer']
\ No newline at end of file
diff --git a/recaptcha_classifier/train/training.py b/recaptcha_classifier/train/training.py
index c663c04ecf..9b8053c799 100644
--- a/recaptcha_classifier/train/training.py
+++ b/recaptcha_classifier/train/training.py
@@ -15,10 +15,12 @@ def __init__(self,
                  train_loader: DataLoader,
                  val_loader: DataLoader,
                  epochs: int,
+                 optimizer: torch.optim.Optimizer,
+                 scheduler: torch.optim.lr_scheduler,
                  save_folder: str,
-                 model_file_name='model.pt',
-                 optimizer_file_name='optimizer.pt',
-                 scheduler_file_name='scheduler.pt',
+                 model_file_name: str ='model.pt',
+                 optimizer_file_name: str ='optimizer.pt',
+                 scheduler_file_name: str ='scheduler.pt',
                  device: torch.device |None = None,
                  early_stop_threshold: int = 5
                  ):

From 9d4f273e5f518ea35abde4e1abb0afaef0b6074b Mon Sep 17 00:00:00 2001
From: Sinan <sinan_ceviker@yahoo.com>
Date: Sun, 25 May 2025 01:38:48 +0200
Subject: [PATCH 16/32] first complete training!!!

---
 evaluation_results.json                       |   0
 recaptcha_classifier/data/__init__.py         |   8 +-
 recaptcha_classifier/data/augment.py          |  69 ++----------------
 recaptcha_classifier/data/pipeline.py         |  14 ++--
 .../evaluation/classification_metrics.py      |   5 +-
 reports/figures/first_training.png            | Bin 0 -> 77959 bytes
 6 files changed, 20 insertions(+), 76 deletions(-)
 create mode 100644 evaluation_results.json
 create mode 100644 reports/figures/first_training.png

diff --git a/evaluation_results.json b/evaluation_results.json
new file mode 100644
index 0000000000..e69de29bb2
diff --git a/recaptcha_classifier/data/__init__.py b/recaptcha_classifier/data/__init__.py
index b4dde44d2b..ab71a0435a 100644
--- a/recaptcha_classifier/data/__init__.py
+++ b/recaptcha_classifier/data/__init__.py
@@ -6,11 +6,7 @@
 from .loader_factory import LoaderFactory
 from .dataset import ImageDataset
 from .preprocessor import ImagePrep
-from .augment import (
-    AugmentationPipeline,
-    HorizontalFlip,
-    RandomRotation
-)
+from .augment import AugmentationPipeline
 
 from .collate_batch import collate_batch
 
@@ -33,8 +29,6 @@
     "ImageDataset",
     "ImagePrep",
     "AugmentationPipeline",
-    "HorizontalFlip",
-    "RandomRotation",
     # Methods
     "collate_batch",
     # Types
diff --git a/recaptcha_classifier/data/augment.py b/recaptcha_classifier/data/augment.py
index 1dcef38bbf..2ee09cfc16 100644
--- a/recaptcha_classifier/data/augment.py
+++ b/recaptcha_classifier/data/augment.py
@@ -1,41 +1,20 @@
-import random
-from abc import ABC, abstractmethod
-from PIL import Image
 from typing import List
 from .types import LoadedImg
-
-
-class Augmentation(ABC):
-    """Abstract class for data augmentation."""
-    @abstractmethod
-    def augment(self,
-                image: LoadedImg,
-                annotations: List) -> LoadedImg:
-        """
-        Apply the transformation of the image and updates the bounding boxes
-        if necessary.
-
-        Args:
-            image (LoadedImg): The image to be augmented.
-            annotations (List): List of annotations associated with the image.
-
-        Returns:
-            LoadedImg: The augmented image and the updated
-            annotations.
-        """
-        pass
+from torchvision import transforms
 
 
 class AugmentationPipeline:
     """Class to manage a series of augmentations in sequence."""
-    def __init__(self, transforms=[]) -> None:
-        self._transforms: List[Augmentation] = transforms
+    def __init__(self, transforms_list: List) -> None:
+        """
+        Initializes the augmentation pipeline with a list of transformations.
+        """
+        self._pipeline = transforms.Compose(transforms_list)
 
     def apply_transforms(self,
                          image: LoadedImg) -> LoadedImg:
         """
-        Apply all transformations in the pipeline to the image and
-        annotations.
+        Apply all transformations in the pipeline to the image.
 
         Args:
             image (LoadedImg): The image to be augmented.
@@ -43,36 +22,4 @@ def apply_transforms(self,
         Returns:
             LoadedImg: The augmented image.
         """
-        for transform in self._transforms:
-            if hasattr(transform, 'prob') and random.random() > transform.prob:
-                continue
-            image = transform.augment(image)
-        return image
-
-
-class HorizontalFlip(Augmentation):
-    """Flips the image horizontally, with probability p."""
-    def __init__(self, p: float = 0.5) -> None:
-        self.prob = p
-
-    def augment(self,
-                image: LoadedImg) -> LoadedImg:
-        flipped = image.transpose(Image.FLIP_LEFT_RIGHT)
-
-        return flipped
-
-
-class RandomRotation(Augmentation):
-    """
-    Rotates the image by a random angle.
-    """
-    def __init__(self, degrees: float = 30.0, p: float = 0.5) -> None:
-        self._degrees = degrees
-        self.prob = p
-
-    def augment(self,
-                image: LoadedImg) -> LoadedImg:
-        angle = random.uniform(-self._degrees, self._degrees)
-
-        rotated = image.rotate(angle)
-        return rotated
+        return self._pipeline(image)
\ No newline at end of file
diff --git a/recaptcha_classifier/data/pipeline.py b/recaptcha_classifier/data/pipeline.py
index 612328a8f2..0f113a09ef 100644
--- a/recaptcha_classifier/data/pipeline.py
+++ b/recaptcha_classifier/data/pipeline.py
@@ -6,11 +6,8 @@
 from .splitter import DataSplitter
 from .visualizer import Visualizer
 from .preprocessor import ImagePrep
-from .augment import (
-    AugmentationPipeline,
-    HorizontalFlip,
-    RandomRotation
-)
+from .augment import AugmentationPipeline
+from torchvision import transforms
 from .loader_factory import LoaderFactory
 
 
@@ -78,8 +75,11 @@ def _build_augmentator(self) -> AugmentationPipeline:
             AugmentationPipeline: The augmentation pipeline.
         """
         return AugmentationPipeline([
-            HorizontalFlip(p=0.5),
-            RandomRotation(degrees=30, p=0.5)
+            transforms.RandomHorizontalFlip(p=0.5),
+            transforms.RandomRotation(degrees=15),
+            transforms.ColorJitter(brightness=0.3, contrast=0.3, saturation=0.3),
+            transforms.RandomResizedCrop(size=(224, 224), scale=(0.8, 1.0)),
+            transforms.GaussianBlur(kernel_size=(5, 9), sigma=(0.1, 2.0)),
         ])
 
     def run(self) -> Dict[str, DataLoader]:
diff --git a/recaptcha_classifier/features/evaluation/classification_metrics.py b/recaptcha_classifier/features/evaluation/classification_metrics.py
index 0fc17a2e0d..85ac0f5cab 100644
--- a/recaptcha_classifier/features/evaluation/classification_metrics.py
+++ b/recaptcha_classifier/features/evaluation/classification_metrics.py
@@ -13,7 +13,8 @@ def evaluate_classification(y_pred: Tensor,
                             device: torch.device = torch.device("cuda" if torch.cuda.is_available() else "cpu"),
                             average: str = 'weighted',
                             cm_plot: bool = True,
-                            class_names: Optional[list[str]] = None) -> dict:
+                            class_names: Optional[list[str]] = None,
+                            ) -> dict:
     """
     Evaluate classification model using torchmetrics
 
@@ -40,6 +41,8 @@ def evaluate_classification(y_pred: Tensor,
     # Convert logits to predicted labels
     y_pred = torch.argmax(y_pred, dim=1)
 
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
     # Initialize Metrics
     acc = Accuracy(task="multiclass", num_classes=num_classes).to(device=device)
     f1 = F1Score(task="multiclass", num_classes=num_classes, average=average).to(device=device)
diff --git a/reports/figures/first_training.png b/reports/figures/first_training.png
new file mode 100644
index 0000000000000000000000000000000000000000..dedddc444b5be1d1faac7279c43325cced13e538
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literal 0
HcmV?d00001


From c9dd0b177ea4edb730bb6e600f878eaaa5313043 Mon Sep 17 00:00:00 2001
From: Sinan <sinan_ceviker@yahoo.com>
Date: Mon, 26 May 2025 15:59:14 +0200
Subject: [PATCH 17/32] pulled from main

---
 recaptcha_classifier/__init__.py                   | 3 ++-
 recaptcha_classifier/models/__init__.py            | 3 ++-
 recaptcha_classifier/models/main_model/__init__.py | 4 +++-
 3 files changed, 7 insertions(+), 3 deletions(-)

diff --git a/recaptcha_classifier/__init__.py b/recaptcha_classifier/__init__.py
index 61c583b299..792c58996c 100644
--- a/recaptcha_classifier/__init__.py
+++ b/recaptcha_classifier/__init__.py
@@ -1,5 +1,5 @@
 from .models import SimpleCNN
-from .models.main_model import MainCNN
+from .models.main_model import MainCNN, HPOptimizer
 from .detection_labels import DetectionLabels
 from .data import DataPreprocessingPipeline
 from .train import Trainer
@@ -10,6 +10,7 @@
     "DataPreprocessingPipeline",
     'SimpleCNN',
     'MainCNN',
+    'HPOptimizer',
     'Trainer',
     'evaluate_model'
 ]
\ No newline at end of file
diff --git a/recaptcha_classifier/models/__init__.py b/recaptcha_classifier/models/__init__.py
index 5f6908ea92..735c490da1 100644
--- a/recaptcha_classifier/models/__init__.py
+++ b/recaptcha_classifier/models/__init__.py
@@ -1,3 +1,4 @@
 from .simple_classifier_model import SimpleCNN
+from .main_model import MainCNN, HPOptimizer
 
-__all__ = ['SimpleCNN']
+__all__ = ['SimpleCNN', 'MainCNN', 'HPOptimizer']
diff --git a/recaptcha_classifier/models/main_model/__init__.py b/recaptcha_classifier/models/main_model/__init__.py
index 5417bd46f3..405ca63fb8 100644
--- a/recaptcha_classifier/models/main_model/__init__.py
+++ b/recaptcha_classifier/models/main_model/__init__.py
@@ -1,5 +1,7 @@
 from .model_class import MainCNN
+from .HPoptimizer import HPOptimizer
 
 __all__ = [
-    "MainCNN"
+    "MainCNN",
+    "HPOptimizer"
 ]
\ No newline at end of file

From 58d93ada0cb5b1210ffc2a5befbed8b059826797 Mon Sep 17 00:00:00 2001
From: Sinan <sinan_ceviker@yahoo.com>
Date: Mon, 26 May 2025 16:19:20 +0200
Subject: [PATCH 18/32] solved unit tests for other parts of code

---
 Pipfile                                                    | 1 +
 .../features/evaluation/classification_metrics.py          | 6 +++++-
 recaptcha_classifier/models/main_model/HPoptimizer.py      | 7 +++++--
 tests/models/test_training.py                              | 1 +
 4 files changed, 12 insertions(+), 3 deletions(-)

diff --git a/Pipfile b/Pipfile
index d6ff5dc0ce..8457549909 100644
--- a/Pipfile
+++ b/Pipfile
@@ -10,6 +10,7 @@ verify_ssl = true
 
 [packages]
 numpy = "*"
+pandas = "*"
 pillow= "*"
 opencv-python = "*"
 pre-commit = "*"
diff --git a/recaptcha_classifier/features/evaluation/classification_metrics.py b/recaptcha_classifier/features/evaluation/classification_metrics.py
index 85ac0f5cab..f5c3162382 100644
--- a/recaptcha_classifier/features/evaluation/classification_metrics.py
+++ b/recaptcha_classifier/features/evaluation/classification_metrics.py
@@ -40,8 +40,12 @@ def evaluate_classification(y_pred: Tensor,
 
     # Convert logits to predicted labels
     y_pred = torch.argmax(y_pred, dim=1)
-
     device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    y_pred = y_pred.to(device)
+    y_true = y_true.to(device)
+    
+    if num_classes <= 0:
+        raise ValueError("num_classes must be a positive integer")
 
     # Initialize Metrics
     acc = Accuracy(task="multiclass", num_classes=num_classes).to(device=device)
diff --git a/recaptcha_classifier/models/main_model/HPoptimizer.py b/recaptcha_classifier/models/main_model/HPoptimizer.py
index 4bb6490f90..fe280cb67c 100644
--- a/recaptcha_classifier/models/main_model/HPoptimizer.py
+++ b/recaptcha_classifier/models/main_model/HPoptimizer.py
@@ -6,6 +6,8 @@
 from recaptcha_classifier.models.main_model.model_class import MainCNN
 from recaptcha_classifier.train.training import Trainer
 
+from recaptcha_classifier.detection_labels import DetectionLabels
+
 
 class HPOptimizer(object):
     """Class for optimizing hyperparameters."""
@@ -81,9 +83,10 @@ def optimize_hyperparameters(self,
         return df_opt_data.copy()[:n_models]
 
 
-    def _train_one_model(self, hp_combo, save_checkpoints) -> None:
+    def _train_one_model(self, hp_combo) -> None:
         model = MainCNN(n_layers=int(hp_combo[0]), kernel_size=int(hp_combo[1]))
-        self.trainer.train(model=model, lr=hp_combo[2], load_checkpoint=False, save_checkpoint=save_checkpoints)
+        self._trainer.optimizer = torch.optim.RAdam(model.parameters(), lr=hp_combo[2])
+        self._trainer.train(model=model, lr=hp_combo[2], load_checkpoint=False)
 
 
     def _generate_hp_combinations(self, hp) -> list:
diff --git a/tests/models/test_training.py b/tests/models/test_training.py
index 14b59f42f7..f63c389480 100644
--- a/tests/models/test_training.py
+++ b/tests/models/test_training.py
@@ -47,6 +47,7 @@ def test_train_process(self) -> None:
 
 
     def test_train_load_checkpoint(self):
+        self.trainer.train(model=self.model, load_checkpoint=False)
         self.trainer.train(model=self.model, load_checkpoint=True)
         assert_equal(os.path.exists(os.path.join(self.trainer.save_folder, self.trainer.model_file_name)),
                      True,

From 6a60723e747289b135d2931e2193de6a6bc8d431 Mon Sep 17 00:00:00 2001
From: Sinan <sinan_ceviker@yahoo.com>
Date: Mon, 26 May 2025 16:55:50 +0200
Subject: [PATCH 19/32] fixed data preproc tests

---
 tests/data/test_augment.py        | 29 +++--------
 tests/data/test_dataset.py        | 28 ++++-------
 tests/data/test_loader_factory.py | 19 +++----
 tests/data/test_pair_loader.py    | 83 -------------------------------
 tests/data/test_paths_loader.py   | 35 +++++++++++++
 tests/data/test_preprocessor.py   |  7 ---
 tests/data/test_scaler.py         | 20 --------
 tests/data/test_splitter.py       |  2 +-
 tests/data/test_visualizer.py     | 12 ++---
 9 files changed, 64 insertions(+), 171 deletions(-)
 delete mode 100644 tests/data/test_pair_loader.py
 create mode 100644 tests/data/test_paths_loader.py
 delete mode 100644 tests/data/test_scaler.py

diff --git a/tests/data/test_augment.py b/tests/data/test_augment.py
index 667a2681c8..de6eeef074 100644
--- a/tests/data/test_augment.py
+++ b/tests/data/test_augment.py
@@ -2,11 +2,8 @@
 import numpy as np
 from unittest.mock import patch
 from PIL import Image
-from recaptcha_classifier.data.augment import (
-    AugmentationPipeline,
-    HorizontalFlip,
-    RandomRotation
-)
+from recaptcha_classifier.data.augment import AugmentationPipeline
+from torchvision import transforms
 
 
 class TestAugmentation(unittest.TestCase):
@@ -17,25 +14,11 @@ def setUp(self):
         )
         self.img = self.img.convert("RGB")
 
-    def test_horizontal_flip(self):
-        aug = HorizontalFlip(p=1.0)
-        flipped_img = aug.augment(self.img)
-
-        self.assertFalse(np.array_equal(np.array(flipped_img),
-                                        np.array(self.img)))
-
-    @patch('random.uniform', return_value=30)
-    def test_random_rotation(self, _):
-        augmenter = RandomRotation(degrees=30)
-        rotated_img = augmenter.augment(self.img)
-
-        self.assertFalse(np.array_equal(np.array(rotated_img),
-                                        np.array(self.img)))
-
     def test_pipeline(self):
-        pipeline = AugmentationPipeline()
-        pipeline.add_transform(HorizontalFlip(p=1.0))
-        pipeline.add_transform(RandomRotation(degrees=30))
+        pipeline = AugmentationPipeline([
+            transforms.RandomHorizontalFlip(p=1.0),
+            transforms.RandomRotation(degrees=30)
+        ])
 
         new_img = pipeline.apply_transforms(self.img)
 
diff --git a/tests/data/test_dataset.py b/tests/data/test_dataset.py
index 9b8bedc5b8..1fd9118067 100644
--- a/tests/data/test_dataset.py
+++ b/tests/data/test_dataset.py
@@ -10,20 +10,22 @@
 
 class TestImageDataset(unittest.TestCase):
     def setUp(self):
-        self.images = [Path("data/images/c1/i1.png")]
+        self.items = [Path("data/c1/i1.png")]
         self.class_map = {"c1": 0}
         self.preprocessor = ImagePrep()
-        self.augmentator = AugmentationPipeline()
+        self.augmentator = AugmentationPipeline(transforms_list=[])
 
     @patch.object(ImagePrep, 'load_image')
     @patch.object(ImagePrep, 'to_tensor')
-    def test_loading(self, to_tensor_mock, load_image_mock):
+    @patch.object(ImagePrep, 'class_id_to_tensor')
+    def test_loading(self, class_id_mock, to_tensor_mock, load_image_mock):
         # Mock the return values
         load_image_mock.return_value = MagicMock()
         to_tensor_mock.return_value = torch.rand(3, 224, 224)  # expect shape
+        class_id_mock.return_value = torch.tensor(0)
 
         dataset = ImageDataset(
-            pairs=self.pairs,
+            items=self.items,
             preprocessor=self.preprocessor,
             augmentator=self.augmentator,
             class_map=self.class_map
@@ -33,26 +35,14 @@ def test_loading(self, to_tensor_mock, load_image_mock):
 
         self.assertIsInstance(tensor, torch.Tensor)
         self.assertEqual(tensor.shape, (3, 224, 224))
-        self.assertIsInstance(cid, int)
-        self.assertEqual(cid, 0)
-
-    @patch.object(ImagePrep, 'load_image')
-    def test_empty_bb(self, load_image_mock):
-        load_image_mock.return_value = MagicMock()
-        dataset = ImageDataset(
-            pairs=self.pairs,
-            preprocessor=self.preprocessor,
-            augmentator=self.augmentator,
-            class_map=self.class_map
-        )
-        with self.assertRaises(ValueError):
-            dataset[0]
+        self.assertIsInstance(cid, torch.Tensor)
+        self.assertEqual(cid.item(), 0)
 
     @patch.object(ImagePrep, 'load_image')
     def test_no_class(self, load_image_mock):
         load_image_mock.return_value = np.ones((224, 224, 3))
         dataset = ImageDataset(
-            pairs=self.pairs,
+            items=self.items,
             preprocessor=self.preprocessor,
             augmentator=self.augmentator,
         )
diff --git a/tests/data/test_loader_factory.py b/tests/data/test_loader_factory.py
index cab1187202..18f768b64e 100644
--- a/tests/data/test_loader_factory.py
+++ b/tests/data/test_loader_factory.py
@@ -12,7 +12,7 @@ class TestLoaderFactory(unittest.TestCase):
     def test_create_loaders(self, dataset_mock):
         class_map = {"class1": 0, "class2": 1}
         preprocessor = ImagePrep()
-        augmentator = AugmentationPipeline()
+        augmentator = AugmentationPipeline(transforms_list=[])
         factory = LoaderFactory(class_map, preprocessor, augmentator)
 
         dataset_mock.return_value = dataset_mock
@@ -20,20 +20,17 @@ def test_create_loaders(self, dataset_mock):
 
         splits = {
             "train": {
-                "class1": [(Path("img1.png"), Path("label1.txt")),
-                           (Path("img2.png"), Path("label2.txt"))],
-                "class2": [(Path("img3.png"), Path("label3.txt"))]
+                "class1": [Path("img1.png"), Path("img2.png")],
             },
             "val": {
-                "class1": [(Path("img4.png"), Path("label4.txt"))],
-                "class2": [(Path("img5.png"), Path("label5.txt"))]
+                "class1":
+                    [Path("img3.png")],
             },
             "test": {
-                "class1": [(Path("img6.png"), Path("label6.txt"))],
-                "class2": [(Path("img7.png"), Path("label7.txt"))]
+                "class1": [Path("img4.png"), Path("img5.png"), Path("img6.png")]
             }
         }
-
+        
         loaders = factory.create_loaders(splits)
 
         self.assertIn("train", loaders)
@@ -45,9 +42,7 @@ def test_create_loaders(self, dataset_mock):
 
         # We also need to check that the train set has the augmentator
         dataset_mock.assert_any_call(
-            pairs=[(Path("img1.png"), Path("label1.txt")),
-                   (Path("img2.png"), Path("label2.txt")),
-                   (Path("img3.png"), Path("label3.txt"))],
+            items=[Path("img1.png"),Path("img2.png")],
             preprocessor=preprocessor,
             augmentator=augmentator,
             class_map=class_map
diff --git a/tests/data/test_pair_loader.py b/tests/data/test_pair_loader.py
deleted file mode 100644
index e7ae89a983..0000000000
--- a/tests/data/test_pair_loader.py
+++ /dev/null
@@ -1,83 +0,0 @@
-import unittest
-from pathlib import Path
-from unittest.mock import patch
-
-from recaptcha_classifier.data.pair_loader import ImagePathsLoader
-
-
-class TestImagePathsLoader(unittest.TestCase):
-    @patch("recaptcha_classifier.data.pair_loader.Path.glob")
-    @patch("recaptcha_classifier.data.pair_loader.Path.exists",
-           return_value=True)
-    @patch("recaptcha_classifier.data.pair_loader.Path.is_dir",
-           return_value=True)
-    def test_load_pairs_with_all_labels(self,
-                                        is_dir_mock,
-                                        exists_mock,
-                                        glob_mock):
-        glob_mock.return_value = [
-            Path("data/images/class1/img1.png"),
-            Path("data/images/class1/img2.png"),
-        ]  # 2 images found in the png glob
-
-        loader = ImagePathsLoader(["class1"])
-        pairs = loader.find_image_paths()
-
-        expected_pairs = {
-            (Path("data/images/class1/img1.png"),
-             Path("data/labels/class1/img1.txt")),
-            (Path("data/images/class1/img2.png"),
-             Path("data/labels/class1/img2.txt")),
-        }
-
-        self.assertEqual(set(pairs["class1"]), expected_pairs)
-        self.assertEqual(len(pairs["class1"]), 2)
-
-    @patch("recaptcha_classifier.data.pair_loader.Path.glob")
-    @patch("recaptcha_classifier.data.pair_loader.Path.exists")
-    @patch("recaptcha_classifier.data.pair_loader.Path.is_dir",
-           return_value=True)
-    def test_load_pairs_with_missing_labels(self,
-                                            is_dir_mock,
-                                            exists_mock,
-                                            glob_mock):
-        glob_mock.return_value = [
-            Path("data/images/class1/img1.png"),
-            Path("data/images/class1/img2.png"),
-        ]  # 2 images found in the png glob
-
-        # label folder exists, img1.txt exists but img2.txt does not
-        exists_mock.side_effect = [True, True, False]
-
-        loader = ImagePathsLoader(["class1"])
-        pairs = loader.find_image_paths()
-
-        self.assertIn("class1", pairs)
-        expected_pair = {
-            (Path("data/images/class1/img1.png"),
-             Path("data/labels/class1/img1.txt")),
-        }
-
-        self.assertEqual(set(pairs["class1"]), expected_pair)
-        self.assertEqual(len(pairs["class1"]), 1)
-
-    @patch("recaptcha_classifier.data.pair_loader.Path.glob")
-    @patch("recaptcha_classifier.data.pair_loader.Path.exists")
-    @patch("recaptcha_classifier.data.pair_loader.Path.is_dir")
-    def test_caching(self, is_dir_mock, exists_mock, glob_mock):
-        loader = ImagePathsLoader(["class1"])
-        loader._pairs = {"test": [(Path("test.png"), Path("test.txt"))]}
-
-        # if any mocked method is called, then the cache is not used
-        for method in (is_dir_mock, exists_mock, glob_mock):
-            method.side_effect = AssertionError(f"{method} called, error!")
-
-        pairs = loader.find_image_paths()
-
-        self.assertIs(pairs, loader._pairs)
-        self.assertEqual(pairs, {"test": [(Path("test.png"),
-                                           Path("test.txt"))]})
-
-
-if __name__ == "__main__":
-    unittest.main()
diff --git a/tests/data/test_paths_loader.py b/tests/data/test_paths_loader.py
new file mode 100644
index 0000000000..3eebc66e82
--- /dev/null
+++ b/tests/data/test_paths_loader.py
@@ -0,0 +1,35 @@
+import unittest
+from pathlib import Path
+from unittest.mock import patch
+
+from recaptcha_classifier.data.paths_loader import ImagePathsLoader
+
+
+class TestImagePathsLoader(unittest.TestCase):
+    @patch("recaptcha_classifier.data.paths_loader.Path.glob")
+    @patch("recaptcha_classifier.data.paths_loader.Path.exists",
+           return_value=True)
+    @patch("recaptcha_classifier.data.paths_loader.Path.is_dir",
+           return_value=True)
+    def test_load_paths(self,
+                                        is_dir_mock,
+                                        exists_mock,
+                                        glob_mock):
+        glob_mock.return_value = [
+            Path("data/images/class1/img1.png"),
+            Path("data/images/class1/img2.png"),
+        ]  # 2 images found in the png glob
+
+        loader = ImagePathsLoader(["class1"])
+        pairs = loader.find_image_paths()
+
+        expected_pairs = {
+            Path("data/images/class1/img1.png"),
+            Path("data/images/class1/img2.png"),
+        }
+
+        self.assertEqual(set(pairs["class1"]), expected_pairs)
+        self.assertEqual(len(pairs["class1"]), 2)
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/tests/data/test_preprocessor.py b/tests/data/test_preprocessor.py
index daec7a0f05..9f533df829 100644
--- a/tests/data/test_preprocessor.py
+++ b/tests/data/test_preprocessor.py
@@ -24,13 +24,6 @@ def test_load_image(self, open_mock):
         img_mock.convert.assert_called_once_with("RGB")
         resized_mock.resize.assert_called_once_with((224, 224), Image.LANCZOS)
 
-    @patch("builtins.open", new_callable=unittest.mock.mock_open,
-           read_data="0 0.2 0.3 0.4 0.5\n")
-    def test_load_labels(self, file_mock):
-        result = self.prep.load_labels(Path("test"))
-
-        self.assertEqual(result, [(0.2, 0.3, 0.4, 0.5)])
-
     def test_to_tensor(self):
         img = Image.new("RGB", (224, 224))
         tensor = self.prep.to_tensor(img)
diff --git a/tests/data/test_scaler.py b/tests/data/test_scaler.py
deleted file mode 100644
index 9a54dd26d4..0000000000
--- a/tests/data/test_scaler.py
+++ /dev/null
@@ -1,20 +0,0 @@
-import unittest
-from recaptcha_classifier.data.scaler import YOLOScaler
-
-
-class TestYOLOScaler(unittest.TestCase):
-    def test_scale_for_flip(self):
-        bb = [(0.5, 0.5, 0.2, 0.2), (0.3, 0.4, 0.1, 0.1)]
-        flipped = YOLOScaler.scale_for_flip(bb)
-        self.assertEqual(flipped, [(0.5, 0.5, 0.2, 0.2),
-                                   (0.7, 0.4, 0.1, 0.1)])
-
-    def test_scale_for_rotation(self):
-        bb = [(0.5, 0.5, 0.4, 0.2)]
-        rot = YOLOScaler.scale_for_rotation(bb, 90, (224, 224))
-        self.assertEqual(len(rot), 1)
-        self.assertTrue(all(0 <= v <= 1 for bb in rot for v in bb))
-
-    def test_empty_list(self):
-        self.assertEqual(YOLOScaler.scale_for_flip([]), [])
-        self.assertEqual(YOLOScaler.scale_for_rotation([], 45, (224, 224)), [])
diff --git a/tests/data/test_splitter.py b/tests/data/test_splitter.py
index 406b04dc5f..10796cc6d0 100644
--- a/tests/data/test_splitter.py
+++ b/tests/data/test_splitter.py
@@ -4,7 +4,7 @@
 
 class TestDataSplitter(unittest.TestCase):
     def test_default_split(self):
-        data = {"test_class": [(f"img_{i}", f"label_{i}") for i in range(10)]}
+        data = {"test_class": [f"img_{i}" for i in range(10)]}
         splits = DataSplitter().split(data)
 
         self.assertEqual(len(splits['train']['test_class']), 7)
diff --git a/tests/data/test_visualizer.py b/tests/data/test_visualizer.py
index 43096a5805..d4f0d9e8e8 100644
--- a/tests/data/test_visualizer.py
+++ b/tests/data/test_visualizer.py
@@ -8,16 +8,16 @@ def setUp(self):
         # only written pairs as string, instead of paths, for simplicity
         self.sample_splits = {
             "train": {
-                "class1": [("img1", "label1"), ("img2", "label2")],
-                "class2": [("img3", "label3"), ("img4", "label4")],
+                "class1": ["img1", "img2"],
+                "class2": ["img3", "img4"],
             },
             "val": {
-                "class1": [("img5", "label5")],
-                "class2": [("img6", "label6")],
+                "class1": ["img5"],
+                "class2": ["img6"],
             },
             "test": {
-                "class1": [("img7", "label7")],
-                "class2": [("img8", "label8")],
+                "class1": ["img7"],
+                "class2": ["img8"],
             },
         }
 

From 7d660912c6557e513be3262ed6fb9d90d88388a0 Mon Sep 17 00:00:00 2001
From: Sinan <sinan_ceviker@yahoo.com>
Date: Mon, 26 May 2025 17:15:22 +0200
Subject: [PATCH 20/32] setup integration tests

---
 .github/workflows/ci.yml                      | 32 +++++++++++++
 .pre-commit-config.yaml                       |  2 +-
 recaptcha_classifier/data/pipeline.py         |  2 +-
 tests/integration/test_checkpoint.py          | 48 +++++++++++++++++++
 tests/integration/test_data_pipeline.py       | 13 +++++
 tests/{ => unit}/data/__init__.py             |  0
 tests/{ => unit}/data/test_augment.py         |  0
 tests/{ => unit}/data/test_dataset.py         |  0
 tests/{ => unit}/data/test_loader_factory.py  |  0
 tests/{ => unit}/data/test_paths_loader.py    |  0
 tests/{ => unit}/data/test_preprocessor.py    |  0
 tests/{ => unit}/data/test_splitter.py        |  0
 tests/{ => unit}/data/test_visualizer.py      |  0
 tests/{ => unit}/features/__init__.py         |  0
 .../features/test_classification_metrics.py   |  0
 tests/{ => unit}/features/test_evaluate.py    |  0
 tests/{ => unit}/models/__init__.py           |  0
 tests/{ => unit}/models/test_HPoptimizer.py   |  0
 tests/{ => unit/models}/test_classifier.py    |  0
 tests/{ => unit}/models/test_training.py      |  0
 tests/{ => unit}/models/utils_training_hpo.py |  0
 21 files changed, 95 insertions(+), 2 deletions(-)
 create mode 100644 .github/workflows/ci.yml
 create mode 100644 tests/integration/test_checkpoint.py
 create mode 100644 tests/integration/test_data_pipeline.py
 rename tests/{ => unit}/data/__init__.py (100%)
 rename tests/{ => unit}/data/test_augment.py (100%)
 rename tests/{ => unit}/data/test_dataset.py (100%)
 rename tests/{ => unit}/data/test_loader_factory.py (100%)
 rename tests/{ => unit}/data/test_paths_loader.py (100%)
 rename tests/{ => unit}/data/test_preprocessor.py (100%)
 rename tests/{ => unit}/data/test_splitter.py (100%)
 rename tests/{ => unit}/data/test_visualizer.py (100%)
 rename tests/{ => unit}/features/__init__.py (100%)
 rename tests/{ => unit}/features/test_classification_metrics.py (100%)
 rename tests/{ => unit}/features/test_evaluate.py (100%)
 rename tests/{ => unit}/models/__init__.py (100%)
 rename tests/{ => unit}/models/test_HPoptimizer.py (100%)
 rename tests/{ => unit/models}/test_classifier.py (100%)
 rename tests/{ => unit}/models/test_training.py (100%)
 rename tests/{ => unit}/models/utils_training_hpo.py (100%)

diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
new file mode 100644
index 0000000000..88812b3b37
--- /dev/null
+++ b/.github/workflows/ci.yml
@@ -0,0 +1,32 @@
+name: Integration Tests
+
+on:
+    pull_request:
+        branches:
+            - main
+        types:
+            - opened
+            - synchronize
+            - reopened
+
+jobs:
+    integration-tests:
+        runs-on: ubuntu-latest
+        steps:
+            - name: Checkout code
+              uses: actions/checkout@v4
+
+            - name: Set up Python
+              uses: actions/setup-python@v5
+              with:
+                  python-version: '3.10'
+
+            - name: Install dependencies
+              run: |
+                  python -m pip install --upgrade pip
+                  pip install pipenv
+                  pipenv install --dev
+
+            - name: Run integration tests
+              run: |
+                  pipenv run python -m unittest discover -s tests/integration
\ No newline at end of file
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 5d2bc28c2b..242328f4f0 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -9,6 +9,6 @@ repos:
   hooks:
     - id: run-unittests
       name: Run unittests
-      entry: pipenv run python -m unittest discover tests
+      entry: pipenv run python -m unittest discover tests/unit
       language: system
       pass_filenames: false
diff --git a/recaptcha_classifier/data/pipeline.py b/recaptcha_classifier/data/pipeline.py
index 0f113a09ef..46a489f795 100644
--- a/recaptcha_classifier/data/pipeline.py
+++ b/recaptcha_classifier/data/pipeline.py
@@ -35,7 +35,7 @@ def __init__(self,
                  seed: int = 23,  # our group number
                  batch_size: int = 32,
                  num_workers: int = 4,
-                 balance: bool = False,
+                 balance: bool = True,
                  show_plots: bool = False) -> None:
         """
         Initializes the DataPreprocessingPipeline with the given parameters.
diff --git a/tests/integration/test_checkpoint.py b/tests/integration/test_checkpoint.py
new file mode 100644
index 0000000000..91c7294d09
--- /dev/null
+++ b/tests/integration/test_checkpoint.py
@@ -0,0 +1,48 @@
+import unittest
+import torch
+import os
+from recaptcha_classifier.models.main_model import MainCNN
+from recaptcha_classifier.train.training import Trainer
+from recaptcha_classifier.data.pipeline import DataPreprocessingPipeline
+from recaptcha_classifier.detection_labels import DetectionLabels
+
+
+class TestCheckpointIntegration(unittest.TestCase):
+    def test_checkpoint_save_load(self):
+        pipeline = DataPreprocessingPipeline(
+            DetectionLabels,
+            batch_size=2,
+            num_workers=0
+            )
+        
+        loaders = pipeline.run()
+        
+        model = MainCNN(n_layers=1, kernel_size=3, num_classes=len(DetectionLabels))
+        optimizer = torch.optim.Adam(model.parameters())
+        scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=1)
+        device = torch.device("cpu")
+        
+        trainer = Trainer(
+            train_loader=loaders["train"],
+            val_loader=loaders["val"],
+            optimizer=optimizer,
+            model=model,
+            scheduler=scheduler,
+            device=device,
+            epochs=1,
+        )
+        
+        trainer.train(model)
+        
+        model_state = {k: v.clone() for k, v in model.state_dict().items()}
+        
+        model_new = MainCNN(n_layers=1, kernel_size=3, num_classes=len(DetectionLabels))
+        
+        trainer.load_checkpoint_states(model_new)
+        
+        for key in model_state:
+            self.assertTrue(
+                torch.equal(model_state[key], model_new.state_dict()[key])
+            )
+            
+        trainer.delete_checkpoints()
\ No newline at end of file
diff --git a/tests/integration/test_data_pipeline.py b/tests/integration/test_data_pipeline.py
new file mode 100644
index 0000000000..4322638e2f
--- /dev/null
+++ b/tests/integration/test_data_pipeline.py
@@ -0,0 +1,13 @@
+import unittest
+
+from recaptcha_classifier.data.pipeline import DataPreprocessingPipeline
+from recaptcha_classifier.detection_labels import DetectionLabels
+
+
+class TestDataPipeline(unittest.TestCase):
+    def test_pipeline_runs(self):
+        pipeline = DataPreprocessingPipeline(DetectionLabels)
+        loaders = pipeline.run()
+        
+        self.assertIn("train", loaders)
+        self.assertGreater(len(loaders["train"]), 0)
\ No newline at end of file
diff --git a/tests/data/__init__.py b/tests/unit/data/__init__.py
similarity index 100%
rename from tests/data/__init__.py
rename to tests/unit/data/__init__.py
diff --git a/tests/data/test_augment.py b/tests/unit/data/test_augment.py
similarity index 100%
rename from tests/data/test_augment.py
rename to tests/unit/data/test_augment.py
diff --git a/tests/data/test_dataset.py b/tests/unit/data/test_dataset.py
similarity index 100%
rename from tests/data/test_dataset.py
rename to tests/unit/data/test_dataset.py
diff --git a/tests/data/test_loader_factory.py b/tests/unit/data/test_loader_factory.py
similarity index 100%
rename from tests/data/test_loader_factory.py
rename to tests/unit/data/test_loader_factory.py
diff --git a/tests/data/test_paths_loader.py b/tests/unit/data/test_paths_loader.py
similarity index 100%
rename from tests/data/test_paths_loader.py
rename to tests/unit/data/test_paths_loader.py
diff --git a/tests/data/test_preprocessor.py b/tests/unit/data/test_preprocessor.py
similarity index 100%
rename from tests/data/test_preprocessor.py
rename to tests/unit/data/test_preprocessor.py
diff --git a/tests/data/test_splitter.py b/tests/unit/data/test_splitter.py
similarity index 100%
rename from tests/data/test_splitter.py
rename to tests/unit/data/test_splitter.py
diff --git a/tests/data/test_visualizer.py b/tests/unit/data/test_visualizer.py
similarity index 100%
rename from tests/data/test_visualizer.py
rename to tests/unit/data/test_visualizer.py
diff --git a/tests/features/__init__.py b/tests/unit/features/__init__.py
similarity index 100%
rename from tests/features/__init__.py
rename to tests/unit/features/__init__.py
diff --git a/tests/features/test_classification_metrics.py b/tests/unit/features/test_classification_metrics.py
similarity index 100%
rename from tests/features/test_classification_metrics.py
rename to tests/unit/features/test_classification_metrics.py
diff --git a/tests/features/test_evaluate.py b/tests/unit/features/test_evaluate.py
similarity index 100%
rename from tests/features/test_evaluate.py
rename to tests/unit/features/test_evaluate.py
diff --git a/tests/models/__init__.py b/tests/unit/models/__init__.py
similarity index 100%
rename from tests/models/__init__.py
rename to tests/unit/models/__init__.py
diff --git a/tests/models/test_HPoptimizer.py b/tests/unit/models/test_HPoptimizer.py
similarity index 100%
rename from tests/models/test_HPoptimizer.py
rename to tests/unit/models/test_HPoptimizer.py
diff --git a/tests/test_classifier.py b/tests/unit/models/test_classifier.py
similarity index 100%
rename from tests/test_classifier.py
rename to tests/unit/models/test_classifier.py
diff --git a/tests/models/test_training.py b/tests/unit/models/test_training.py
similarity index 100%
rename from tests/models/test_training.py
rename to tests/unit/models/test_training.py
diff --git a/tests/models/utils_training_hpo.py b/tests/unit/models/utils_training_hpo.py
similarity index 100%
rename from tests/models/utils_training_hpo.py
rename to tests/unit/models/utils_training_hpo.py

From 05eef4abd0e41a42fdd084c5d4b1043ffa688925 Mon Sep 17 00:00:00 2001
From: Sinan <sinan_ceviker@yahoo.com>
Date: Wed, 28 May 2025 21:37:55 +0200
Subject: [PATCH 21/32] preparing for final submission

---
 recaptcha_classifier/__init__.py       |  4 +++-
 recaptcha_classifier/api/load_model.py | 12 ++++++++++++
 2 files changed, 15 insertions(+), 1 deletion(-)
 create mode 100644 recaptcha_classifier/api/load_model.py

diff --git a/recaptcha_classifier/__init__.py b/recaptcha_classifier/__init__.py
index 792c58996c..5ef9d74f96 100644
--- a/recaptcha_classifier/__init__.py
+++ b/recaptcha_classifier/__init__.py
@@ -4,6 +4,7 @@
 from .data import DataPreprocessingPipeline
 from .train import Trainer
 from .features import evaluate_model
+from .api import load_model
 
 __all__ = [
     "DetectionLabels",
@@ -12,5 +13,6 @@
     'MainCNN',
     'HPOptimizer',
     'Trainer',
-    'evaluate_model'
+    'evaluate_model',
+    'load_model'
 ]
\ No newline at end of file
diff --git a/recaptcha_classifier/api/load_model.py b/recaptcha_classifier/api/load_model.py
new file mode 100644
index 0000000000..215a865066
--- /dev/null
+++ b/recaptcha_classifier/api/load_model.py
@@ -0,0 +1,12 @@
+import torch
+from recaptcha_classifier import MainCNN, DetectionLabels
+
+def load_model(path="models/model.pt"):
+    model = MainCNN(
+        n_layers=2,
+        kernel_size=3,
+        num_classes=len(DetectionLabels),
+    )
+    model.load_state_dict(torch.load(path, map_location=torch.device('cpu')))
+    model.eval()
+    return model
\ No newline at end of file

From b69c69ddc2ffb272864d34257b76522e9aea56cb Mon Sep 17 00:00:00 2001
From: Sinan <sinan_ceviker@yahoo.com>
Date: Thu, 29 May 2025 19:48:27 +0200
Subject: [PATCH 22/32] only one random augmentation applied per epoch

---
 recaptcha_classifier/api/load_model.py      | 12 ------------
 recaptcha_classifier/data/augment.py        |  8 +++++---
 recaptcha_classifier/data/loader_factory.py |  2 +-
 recaptcha_classifier/data/pipeline.py       | 14 ++++++++++++--
 4 files changed, 18 insertions(+), 18 deletions(-)
 delete mode 100644 recaptcha_classifier/api/load_model.py

diff --git a/recaptcha_classifier/api/load_model.py b/recaptcha_classifier/api/load_model.py
deleted file mode 100644
index 215a865066..0000000000
--- a/recaptcha_classifier/api/load_model.py
+++ /dev/null
@@ -1,12 +0,0 @@
-import torch
-from recaptcha_classifier import MainCNN, DetectionLabels
-
-def load_model(path="models/model.pt"):
-    model = MainCNN(
-        n_layers=2,
-        kernel_size=3,
-        num_classes=len(DetectionLabels),
-    )
-    model.load_state_dict(torch.load(path, map_location=torch.device('cpu')))
-    model.eval()
-    return model
\ No newline at end of file
diff --git a/recaptcha_classifier/data/augment.py b/recaptcha_classifier/data/augment.py
index 2ee09cfc16..9a84e1538b 100644
--- a/recaptcha_classifier/data/augment.py
+++ b/recaptcha_classifier/data/augment.py
@@ -1,3 +1,4 @@
+import random
 from typing import List
 from .types import LoadedImg
 from torchvision import transforms
@@ -9,12 +10,12 @@ def __init__(self, transforms_list: List) -> None:
         """
         Initializes the augmentation pipeline with a list of transformations.
         """
-        self._pipeline = transforms.Compose(transforms_list)
+        self._transforms_list = transforms_list
 
     def apply_transforms(self,
                          image: LoadedImg) -> LoadedImg:
         """
-        Apply all transformations in the pipeline to the image.
+        Apply a random transformation from the pipeline to the image.
 
         Args:
             image (LoadedImg): The image to be augmented.
@@ -22,4 +23,5 @@ def apply_transforms(self,
         Returns:
             LoadedImg: The augmented image.
         """
-        return self._pipeline(image)
\ No newline at end of file
+        transform = random.choice(self._transforms_list)
+        return transform(image)
\ No newline at end of file
diff --git a/recaptcha_classifier/data/loader_factory.py b/recaptcha_classifier/data/loader_factory.py
index 10e7a580ed..0aeded526f 100644
--- a/recaptcha_classifier/data/loader_factory.py
+++ b/recaptcha_classifier/data/loader_factory.py
@@ -4,7 +4,7 @@
 from .dataset import ImageDataset
 from .preprocessor import ImagePrep
 from .augment import AugmentationPipeline
-from .types import DatasetSplitDict, FilePairList
+from .types import DatasetSplitMap, ImagePathList
 from .collate_batch import collate_batch
 
 
diff --git a/recaptcha_classifier/data/pipeline.py b/recaptcha_classifier/data/pipeline.py
index 46a489f795..63c474c774 100644
--- a/recaptcha_classifier/data/pipeline.py
+++ b/recaptcha_classifier/data/pipeline.py
@@ -70,16 +70,26 @@ def __init__(self,
     def _build_augmentator(self) -> AugmentationPipeline:
         """
         Builds the augmentation pipeline.
+        One of these augmentations will be applied randomly
+        in the dataset, for any image in the training set.
 
         Returns:
             AugmentationPipeline: The augmentation pipeline.
         """
         return AugmentationPipeline([
+            # 50 % chance to flip the image horizontally
             transforms.RandomHorizontalFlip(p=0.5),
+            # rotates image randomly within +-15 degrees
             transforms.RandomRotation(degrees=15),
+            # randomly changes brightness, contrast and saturation
             transforms.ColorJitter(brightness=0.3, contrast=0.3, saturation=0.3),
-            transforms.RandomResizedCrop(size=(224, 224), scale=(0.8, 1.0)),
-            transforms.GaussianBlur(kernel_size=(5, 9), sigma=(0.1, 2.0)),
+            # Translates/ shifts image up to 10% of its size
+            # in both x and y directions
+            transforms.RandomAffine(degrees=0, translate=(0.1, 0.1)),
+            # Mimics a camera lens blur effect
+            transforms.GaussianBlur(kernel_size=3, sigma=(0.1, 1.5)),
+            # Crops image 90% then resizes it to 224x224 (zoom in effect)
+            transforms.RandomResizedCrop(size=(224, 224), scale=(0.9, 1.0)),
         ])
 
     def run(self) -> Dict[str, DataLoader]:

From 4ca3332ba13a37261ccc70e1433dbbc1d138657e Mon Sep 17 00:00:00 2001
From: Sinan <sinan_ceviker@yahoo.com>
Date: Thu, 29 May 2025 20:40:13 +0200
Subject: [PATCH 23/32] made kfold print summary of folds

---
 recaptcha_classifier/__init__.py              |  2 +-
 .../evaluation/classification_metrics.py      |  7 ++-
 .../models/main_model/HPoptimizer.py          | 12 ++---
 .../models/main_model/kfold_validation.py     | 46 +++++++++++++------
 .../pipeline/main_model_pipeline.py           |  3 --
 recaptcha_classifier/train/training.py        |  2 -
 6 files changed, 42 insertions(+), 30 deletions(-)

diff --git a/recaptcha_classifier/__init__.py b/recaptcha_classifier/__init__.py
index 5ef9d74f96..3e5b54a524 100644
--- a/recaptcha_classifier/__init__.py
+++ b/recaptcha_classifier/__init__.py
@@ -4,7 +4,7 @@
 from .data import DataPreprocessingPipeline
 from .train import Trainer
 from .features import evaluate_model
-from .api import load_model
+from .server import load_model
 
 __all__ = [
     "DetectionLabels",
diff --git a/recaptcha_classifier/features/evaluation/classification_metrics.py b/recaptcha_classifier/features/evaluation/classification_metrics.py
index f5c3162382..8581ac6b2a 100644
--- a/recaptcha_classifier/features/evaluation/classification_metrics.py
+++ b/recaptcha_classifier/features/evaluation/classification_metrics.py
@@ -1,7 +1,7 @@
 import torch
 from torch import Tensor
 from torchmetrics import Accuracy, F1Score
-from torchmetrics.classification import MulticlassConfusionMatrix
+from torchmetrics.classification import MulticlassConfusionMatrix, MulticlassAccuracy
 from typing import Optional
 from recaptcha_classifier.detection_labels import DetectionLabels
 import matplotlib.pyplot as plt
@@ -50,11 +50,13 @@ def evaluate_classification(y_pred: Tensor,
     # Initialize Metrics
     acc = Accuracy(task="multiclass", num_classes=num_classes).to(device=device)
     f1 = F1Score(task="multiclass", num_classes=num_classes, average=average).to(device=device)
+    topk_acc = MulticlassAccuracy(num_classes=num_classes, top_k=3).to(device=device)
     confmat = MulticlassConfusionMatrix(num_classes=num_classes).to(device=device)
 
     # Compute metrics
     acc_val = acc(y_pred, y_true)
     f1_val = f1(y_pred, y_true)
+    topk_acc_val = topk_acc(y_pred, y_true)
     cm = confmat(y_pred, y_true)
 
     if cm_plot:
@@ -64,5 +66,6 @@ def evaluate_classification(y_pred: Tensor,
     return {
         'Accuracy': acc_val.item(),
         'F1-score': f1_val.item(),
-        'Confusion Matrix': cm
+        'Confusion Matrix': cm,
+        'Top-3 Accuracy': topk_acc_val.item()
     }
diff --git a/recaptcha_classifier/models/main_model/HPoptimizer.py b/recaptcha_classifier/models/main_model/HPoptimizer.py
index fe280cb67c..f9517388ac 100644
--- a/recaptcha_classifier/models/main_model/HPoptimizer.py
+++ b/recaptcha_classifier/models/main_model/HPoptimizer.py
@@ -1,13 +1,10 @@
 import itertools
 import random
-
 import pandas as pd
 
 from recaptcha_classifier.models.main_model.model_class import MainCNN
 from recaptcha_classifier.train.training import Trainer
 
-from recaptcha_classifier.detection_labels import DetectionLabels
-
 
 class HPOptimizer(object):
     """Class for optimizing hyperparameters."""
@@ -30,9 +27,9 @@ def get_history(self)->pd.DataFrame:
 
 
     def optimize_hyperparameters(self,
-                                 n_layers: list = list(range(1,3)),
-                                 kernel_sizes: list = [3, 4, 5],
-                                 learning_rates: list = [1e-3, 1e-4],
+                                 n_layers: list = [1, 2, 3],
+                                 kernel_sizes: list = [3, 5],
+                                 learning_rates: list = [1e-2, 1e-3, 1e-4],
                                  save_checkpoints: bool = True,
                                  n_models: int = 1,
                                  n_combos: int = 8,  # Number of random samples
@@ -85,8 +82,7 @@ def optimize_hyperparameters(self,
 
     def _train_one_model(self, hp_combo) -> None:
         model = MainCNN(n_layers=int(hp_combo[0]), kernel_size=int(hp_combo[1]))
-        self._trainer.optimizer = torch.optim.RAdam(model.parameters(), lr=hp_combo[2])
-        self._trainer.train(model=model, lr=hp_combo[2], load_checkpoint=False)
+        self.trainer.train(model=model, lr=hp_combo[2], load_checkpoint=False)
 
 
     def _generate_hp_combinations(self, hp) -> list:
diff --git a/recaptcha_classifier/models/main_model/kfold_validation.py b/recaptcha_classifier/models/main_model/kfold_validation.py
index 2465030f75..542182eb9d 100644
--- a/recaptcha_classifier/models/main_model/kfold_validation.py
+++ b/recaptcha_classifier/models/main_model/kfold_validation.py
@@ -2,7 +2,7 @@
 from sklearn.model_selection import KFold
 from sympy.printing.pytorch import torch
 from torch.utils.data import DataLoader, Subset
-
+import matplotlib.pyplot as plt
 from recaptcha_classifier import DetectionLabels
 from recaptcha_classifier.features.evaluation.evaluate import evaluate_model
 from recaptcha_classifier.train.training import Trainer
@@ -36,7 +36,6 @@ def __init__(self,
         self.device = device
         if device is None:
             self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-        self.results = None
 
 
     def run_cross_validation(self,
@@ -89,23 +88,42 @@ def run_cross_validation(self,
         
         df_results = pd.DataFrame(results)
         
-        self.results = df_results
+        self.print_summary(df_results)
+        self.plot_results(df_results)
     
-    def print_summary(self) -> None:
+    @staticmethod
+    def print_summary(results: pd.DataFrame) -> None:
         """
         Prints a summary of the cross-validation results.
         """
-        if self.results is None:
-            print("No results to display. Run cross-validation first.")
-            return
-        
-        print("\n--- Cross-Validation Summary ---")
-        print(self.results)
+        print("\n~~ Cross-Validation Summary ~~")
+        print(results.round(3))
         
-        mean_results = self.results.mean()
+        res = results.drop(columns=["fold"])
+
+        means = res.mean()
         print("\nMean Results Across Folds:")
-        print(mean_results)
+        print(means.round(3))
         
-        std_results = self.results.std()
+        stds = res.std()
         print("\nStandard Deviation Across Folds:")
-        print(std_results)
\ No newline at end of file
+        print(stds.round(3))
+    
+    @staticmethod
+    def plot_results(results: pd.DataFrame) -> None:
+        """
+        Plots the results of the cross-validation.
+        """
+        metrics = [col for col in results.columns if col != 'fold']
+        mean_vals = results[metrics].mean()
+        std_vals = results[metrics].std()
+        
+        fig, ax = plt.subplots(figsize=(10, 6))
+        mean_vals.plot(kind="bar", yerr=std_vals, capsize=5, ax=ax)
+        
+        ax.set_title("Cross-Validation Metrics (mean +- std)")
+        ax.set_ylabel("Score")
+        ax.set_xticklabels(metrics, rotation=45, ha='right')
+        plt.tight_layout()
+        plt.grid(axis='y')
+        plt.show()
\ No newline at end of file
diff --git a/recaptcha_classifier/pipeline/main_model_pipeline.py b/recaptcha_classifier/pipeline/main_model_pipeline.py
index 65f7aceaa3..223d659897 100644
--- a/recaptcha_classifier/pipeline/main_model_pipeline.py
+++ b/recaptcha_classifier/pipeline/main_model_pipeline.py
@@ -61,9 +61,6 @@ def _run_kfold_cross_validation(self) -> None:
         
         best_hp = self._hp_optimizer.get_best_hp()
         self._kfold.run_cross_validation(hp=best_hp)
-        
-        print("\n~~ Cross-Validation Summary ~~")
-        self._kfold.print_summary()
 
         self.lr = best_hp[2]
         self._model = self._initialize_model(
diff --git a/recaptcha_classifier/train/training.py b/recaptcha_classifier/train/training.py
index 9b8053c799..661bf3a34e 100644
--- a/recaptcha_classifier/train/training.py
+++ b/recaptcha_classifier/train/training.py
@@ -15,8 +15,6 @@ def __init__(self,
                  train_loader: DataLoader,
                  val_loader: DataLoader,
                  epochs: int,
-                 optimizer: torch.optim.Optimizer,
-                 scheduler: torch.optim.lr_scheduler,
                  save_folder: str,
                  model_file_name: str ='model.pt',
                  optimizer_file_name: str ='optimizer.pt',

From 587d719da0399f169d000c80ed0746a54f54a49d Mon Sep 17 00:00:00 2001
From: Sinan <sinan_ceviker@yahoo.com>
Date: Thu, 29 May 2025 21:03:13 +0200
Subject: [PATCH 24/32] now will start training both models

---
 recaptcha_classifier/data/collate_batch.py    | 10 ++--
 recaptcha_classifier/data/types.py            |  2 +-
 .../models/main_model/kfold_validation.py     | 59 +++++++++++--------
 3 files changed, 40 insertions(+), 31 deletions(-)

diff --git a/recaptcha_classifier/data/collate_batch.py b/recaptcha_classifier/data/collate_batch.py
index a1f12afab6..05b0050130 100644
--- a/recaptcha_classifier/data/collate_batch.py
+++ b/recaptcha_classifier/data/collate_batch.py
@@ -1,16 +1,16 @@
 from typing import List
-from .types import DataBatch, DataItem
+from .types import DataItem, DataBatch
 import torch
 
 
 def collate_batch(batch: List[DataItem]) -> DataBatch:
     """
-    Custom collate function used in the PyTorch DataLoader to handle
-    the non-uniform length of bounding box lists.
+    Custom collate function used in the PyTorch DataLoader to combine
+    the list of data items into a stacked batch for model training.
 
     Args:
-        batch (List[DataItem]): A batch of training items; each item is
-        a tuple of format (image tensor, class index).
+        batch (List[DataItem]): A list of dataset items, tensors of
+        image and label pair.
 
     Returns:
         Batch: A single tuple containing:
diff --git a/recaptcha_classifier/data/types.py b/recaptcha_classifier/data/types.py
index f5537406a1..ade77d8f66 100644
--- a/recaptcha_classifier/data/types.py
+++ b/recaptcha_classifier/data/types.py
@@ -18,7 +18,7 @@
 LoadedImg = Image.Image
 
 # A final dataset item, that now contains the image tensor, and the class id; ready for model training
-DataItem = Tuple[Tensor, int]
+DataItem = Tuple[Tensor, Tensor]
 
 # Output of the dataloader, a batch of data items
 DataBatch = Tuple[Tensor, Tensor]
diff --git a/recaptcha_classifier/models/main_model/kfold_validation.py b/recaptcha_classifier/models/main_model/kfold_validation.py
index 542182eb9d..7513e6166f 100644
--- a/recaptcha_classifier/models/main_model/kfold_validation.py
+++ b/recaptcha_classifier/models/main_model/kfold_validation.py
@@ -3,7 +3,6 @@
 from sympy.printing.pytorch import torch
 from torch.utils.data import DataLoader, Subset
 import matplotlib.pyplot as plt
-from recaptcha_classifier import DetectionLabels
 from recaptcha_classifier.features.evaluation.evaluate import evaluate_model
 from recaptcha_classifier.train.training import Trainer
 from recaptcha_classifier.models.main_model.model_class import MainCNN
@@ -29,14 +28,13 @@ def __init__(self,
         :param hp_optimizer: Instance of HPOptimizer
         :param device: Optional torch device
         """
-        self._class_map = DetectionLabels.all() # class labels
         self.train_loader = train_loader
         self.val_loader = val_loader
         self.k_folds = k_folds
         self.device = device
         if device is None:
-            self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-
+            self.device = torch.device("cuda" if torch.cuda.is_available()
+                                       else "cpu")
 
     def run_cross_validation(self,
                              hp: list,
@@ -59,38 +57,49 @@ def run_cross_validation(self,
         dataset = self.train_loader.dataset
 
         kf = KFold(n_splits=self.k_folds, shuffle=True, random_state=42)
-        
+
         results = []
-        
+
         n_layers, kernel_sizes, learning_rates = hp
 
-        for fold_index, (train_idx, val_idx) in enumerate(kf.split(all_indices)):
+        for fold_index, (t_idx, val_idx) in enumerate(kf.split(all_indices)):
+
             print(f"\n--- Fold {fold_index + 1}/{self.k_folds} ---")
 
-            train_subset = Subset(dataset, [all_indices[i] for i in train_idx])
+            train_subset = Subset(dataset, [all_indices[i]
+                                            for i in t_idx])
             val_subset = Subset(dataset, [all_indices[i] for i in val_idx])
 
-            fold_train_loader = DataLoader(train_subset, batch_size=batch_size, shuffle=False)
-            fold_val_loader = DataLoader(val_subset, batch_size=batch_size, shuffle=False)
-            
+            fold_train_loader = DataLoader(train_subset,
+                                           batch_size=batch_size,
+                                           shuffle=False)
+            fold_val_loader = DataLoader(val_subset,
+                                         batch_size=batch_size,
+                                         shuffle=False)
+
             model = MainCNN(n_layers=n_layers, kernel_size=kernel_sizes)
-            
+
             trainer = Trainer(fold_train_loader, fold_val_loader,
                               epochs=20, save_folder=MODELS_FOLDER,
                               device=self.device)
-            trainer.train(model, lr=learning_rates, save_checkpoint=save_checkpoints,
+
+            trainer.train(model,
+                          lr=learning_rates,
+                          save_checkpoint=save_checkpoints,
                           load_checkpoint=load_checkpoints)
-            
-            metrics = evaluate_model(model, fold_val_loader, device=self.device)
-            metrics.pop('Confusion Matrix') 
+
+            metrics = evaluate_model(model, fold_val_loader,
+                                     device=self.device)
+            metrics.pop('Confusion Matrix')
             metrics["fold"] = fold_index + 1
             results.append(metrics)
-        
+
         df_results = pd.DataFrame(results)
-        
+
+        df_results.to_csv(f"{MODELS_FOLDER}/kfold_results.csv", index=False)
         self.print_summary(df_results)
         self.plot_results(df_results)
-    
+
     @staticmethod
     def print_summary(results: pd.DataFrame) -> None:
         """
@@ -98,17 +107,17 @@ def print_summary(results: pd.DataFrame) -> None:
         """
         print("\n~~ Cross-Validation Summary ~~")
         print(results.round(3))
-        
+
         res = results.drop(columns=["fold"])
 
         means = res.mean()
         print("\nMean Results Across Folds:")
         print(means.round(3))
-        
+
         stds = res.std()
         print("\nStandard Deviation Across Folds:")
         print(stds.round(3))
-    
+
     @staticmethod
     def plot_results(results: pd.DataFrame) -> None:
         """
@@ -117,13 +126,13 @@ def plot_results(results: pd.DataFrame) -> None:
         metrics = [col for col in results.columns if col != 'fold']
         mean_vals = results[metrics].mean()
         std_vals = results[metrics].std()
-        
+
         fig, ax = plt.subplots(figsize=(10, 6))
         mean_vals.plot(kind="bar", yerr=std_vals, capsize=5, ax=ax)
-        
+
         ax.set_title("Cross-Validation Metrics (mean +- std)")
         ax.set_ylabel("Score")
         ax.set_xticklabels(metrics, rotation=45, ha='right')
         plt.tight_layout()
         plt.grid(axis='y')
-        plt.show()
\ No newline at end of file
+        plt.show()

From 57bd014d62a11a6bde16834298b9051cef1e0679 Mon Sep 17 00:00:00 2001
From: Sinan <sinan_ceviker@yahoo.com>
Date: Thu, 29 May 2025 21:24:54 +0200
Subject: [PATCH 25/32] starting training now

---
 .gitignore                                      |   3 +--
 .../evaluation/classification_metrics.py        |   3 ++-
 recaptcha_classifier/pipeline/base_pipeline.py  |   6 +++---
 .../pipeline/simple_cnn_pipeline.py             |   1 -
 reports/figures/first_training.png              | Bin 77959 -> 0 bytes
 reports/{ => figures}/main-model.png            | Bin
 reports/{ => figures}/simple-model.png          | Bin
 7 files changed, 6 insertions(+), 7 deletions(-)
 delete mode 100644 reports/figures/first_training.png
 rename reports/{ => figures}/main-model.png (100%)
 rename reports/{ => figures}/simple-model.png (100%)

diff --git a/.gitignore b/.gitignore
index 8799711320..1a48f990df 100644
--- a/.gitignore
+++ b/.gitignore
@@ -6,6 +6,5 @@ venv/
 .env
 
 /models/
-private/
 /data/
-/models/
\ No newline at end of file
+/private/
\ No newline at end of file
diff --git a/recaptcha_classifier/features/evaluation/classification_metrics.py b/recaptcha_classifier/features/evaluation/classification_metrics.py
index 8581ac6b2a..3ad76ba0c9 100644
--- a/recaptcha_classifier/features/evaluation/classification_metrics.py
+++ b/recaptcha_classifier/features/evaluation/classification_metrics.py
@@ -37,6 +37,7 @@ def evaluate_classification(y_pred: Tensor,
         dict: accuracy, f1, confusion_matrix
 
     """
+    logits = y_pred
 
     # Convert logits to predicted labels
     y_pred = torch.argmax(y_pred, dim=1)
@@ -56,7 +57,7 @@ def evaluate_classification(y_pred: Tensor,
     # Compute metrics
     acc_val = acc(y_pred, y_true)
     f1_val = f1(y_pred, y_true)
-    topk_acc_val = topk_acc(y_pred, y_true)
+    topk_acc_val = topk_acc(logits, y_true)
     cm = confmat(y_pred, y_true)
 
     if cm_plot:
diff --git a/recaptcha_classifier/pipeline/base_pipeline.py b/recaptcha_classifier/pipeline/base_pipeline.py
index 7ec64c0fab..674422b396 100644
--- a/recaptcha_classifier/pipeline/base_pipeline.py
+++ b/recaptcha_classifier/pipeline/base_pipeline.py
@@ -25,7 +25,7 @@ def __init__(self,
         self.optimizer_file_name = optimizer_file_name
         self.scheduler_file_name = scheduler_file_name
 
-        self._class_map = DetectionLabels.to_class_map()
+        self._class_map = DetectionLabels
         self._loaders = None
         self._data = None
         self._model = None
@@ -59,14 +59,14 @@ def _initialize_trainer(self) -> Trainer:
 
     @property
     def class_map_length(self):
-        return len(self._class_map)
+        return len(self._class_map.all())
 
     def evaluate(self, plot_cm: bool = False) -> dict:
         eval_results = evaluate_model(
             model=self._model,
             test_loader=self._loaders['test'],
             device=self._trainer.device,
-            class_names=list(self._class_map.keys()),
+            class_names=self._class_map.dataset_classnames,
             plot_cm=plot_cm
         )
         return eval_results
diff --git a/recaptcha_classifier/pipeline/simple_cnn_pipeline.py b/recaptcha_classifier/pipeline/simple_cnn_pipeline.py
index efd3a792f6..0b8cbd7816 100644
--- a/recaptcha_classifier/pipeline/simple_cnn_pipeline.py
+++ b/recaptcha_classifier/pipeline/simple_cnn_pipeline.py
@@ -1,7 +1,6 @@
 import torch
 from recaptcha_classifier.models.simple_classifier_model import SimpleCNN
 from recaptcha_classifier.pipeline.base_pipeline import BasePipeline
-from recaptcha_classifier.train.training import Trainer
 import os
 from recaptcha_classifier.constants import (
     MODELS_FOLDER, SIMPLE_MODEL_FILE_NAME,
diff --git a/reports/figures/first_training.png b/reports/figures/first_training.png
deleted file mode 100644
index dedddc444b5be1d1faac7279c43325cced13e538..0000000000000000000000000000000000000000
GIT binary patch
literal 0
HcmV?d00001

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diff --git a/reports/main-model.png b/reports/figures/main-model.png
similarity index 100%
rename from reports/main-model.png
rename to reports/figures/main-model.png
diff --git a/reports/simple-model.png b/reports/figures/simple-model.png
similarity index 100%
rename from reports/simple-model.png
rename to reports/figures/simple-model.png

From 6c3cd1b80ff9cffbd76670bf7a935afdf5ad1415 Mon Sep 17 00:00:00 2001
From: Sinan <sinan_ceviker@yahoo.com>
Date: Thu, 29 May 2025 21:26:35 +0200
Subject: [PATCH 26/32] now

---
 recaptcha_classifier/constants.py                    | 2 +-
 recaptcha_classifier/pipeline/simple_cnn_pipeline.py | 1 +
 2 files changed, 2 insertions(+), 1 deletion(-)

diff --git a/recaptcha_classifier/constants.py b/recaptcha_classifier/constants.py
index 943487486f..a09a783239 100644
--- a/recaptcha_classifier/constants.py
+++ b/recaptcha_classifier/constants.py
@@ -5,7 +5,7 @@
 IMAGE_SIZE = (IMAGE_WIDTH, IMAGE_HEIGHT)
 INPUT_SHAPE = (IMAGE_CHANNELS, IMAGE_HEIGHT, IMAGE_WIDTH)
 
-MODELS_FOLDER="models" # Folder where models are saved
+MODELS_FOLDER="models/final" # Folder where models are saved
 MAIN_MODEL_FILE_NAME = "main_model.pt"
 SIMPLE_MODEL_FILE_NAME = "simple_model.pt"
 OPTIMIZER_FILE_NAME = "optimizer.pt"
diff --git a/recaptcha_classifier/pipeline/simple_cnn_pipeline.py b/recaptcha_classifier/pipeline/simple_cnn_pipeline.py
index 0b8cbd7816..4f9b3496d6 100644
--- a/recaptcha_classifier/pipeline/simple_cnn_pipeline.py
+++ b/recaptcha_classifier/pipeline/simple_cnn_pipeline.py
@@ -40,4 +40,5 @@ def _initialize_model(self) -> SimpleCNN:
         return SimpleCNN(num_classes=self.class_map_length)
     
     def save_model(self):
+        os.makedirs(self.save_folder, exist_ok=True)
         torch.save(self._model.state_dict(), os.path.join(self.save_folder, self.model_file_name))
\ No newline at end of file

From 692ee5f81c6b65587473f9dc853df6b2efa12c8b Mon Sep 17 00:00:00 2001
From: Sinan <sinan_ceviker@yahoo.com>
Date: Thu, 29 May 2025 22:16:33 +0200
Subject: [PATCH 27/32] api working

---
 main.py                                       |  10 +++++--
 .../pipeline/base_pipeline.py                 |   2 +-
 .../pipeline/main_model_pipeline.py           |   1 +
 recaptcha_classifier/server/api.py            |  13 ++++-----
 recaptcha_classifier/server/app.py            |  26 ++++++++++++++----
 recaptcha_classifier/server/load_model.py     |   9 ++----
 reports/figures/simple-model.png              | Bin 78418 -> 80765 bytes
 7 files changed, 39 insertions(+), 22 deletions(-)

diff --git a/main.py b/main.py
index d9061c3a73..e34ca1c52b 100644
--- a/main.py
+++ b/main.py
@@ -49,10 +49,14 @@ def handle_action(choice: str):
         handle_action(choice)
 
 def ui():
-    from recaptcha_classifier.server.app import StreamlitApp
+    import subprocess
+    import os
+    root_dir = os.path.dirname(os.path.abspath(__file__))
+    streamlit_file = os.path.join(root_dir, "recaptcha_classifier", "server", "app.py")
 
-    app = StreamlitApp()
-    app.render()
+    subprocess.Popen(["uvicorn", "recaptcha_classifier.server.api:app", "--reload"])
+    
+    subprocess.run(["streamlit", "run", streamlit_file])
 
 def train_simple_cnn():
     from recaptcha_classifier.pipeline.simple_cnn_pipeline import SimpleClassifierPipeline
diff --git a/recaptcha_classifier/pipeline/base_pipeline.py b/recaptcha_classifier/pipeline/base_pipeline.py
index 674422b396..fec7ac4df6 100644
--- a/recaptcha_classifier/pipeline/base_pipeline.py
+++ b/recaptcha_classifier/pipeline/base_pipeline.py
@@ -66,7 +66,7 @@ def evaluate(self, plot_cm: bool = False) -> dict:
             model=self._model,
             test_loader=self._loaders['test'],
             device=self._trainer.device,
-            class_names=self._class_map.dataset_classnames,
+            class_names=self._class_map.dataset_classnames(),
             plot_cm=plot_cm
         )
         return eval_results
diff --git a/recaptcha_classifier/pipeline/main_model_pipeline.py b/recaptcha_classifier/pipeline/main_model_pipeline.py
index 223d659897..31538aed0a 100644
--- a/recaptcha_classifier/pipeline/main_model_pipeline.py
+++ b/recaptcha_classifier/pipeline/main_model_pipeline.py
@@ -73,6 +73,7 @@ def _initialize_model(self, n_layers: int, kernel_size: int) -> MainCNN:
             num_classes=self.class_map_length)
     
     def save_model(self):
+        os.makedirs(self.save_folder, exist_ok=True)
         torch.save({
             "model_state_dict": self._model.state_dict(),
             "config": {
diff --git a/recaptcha_classifier/server/api.py b/recaptcha_classifier/server/api.py
index e952408e36..33ed2668a4 100644
--- a/recaptcha_classifier/server/api.py
+++ b/recaptcha_classifier/server/api.py
@@ -4,7 +4,7 @@
 from fastapi import FastAPI, File, UploadFile, Response
 from fastapi.responses import JSONResponse
 from PIL import Image
-from .load_model import load_main_model
+from .load_model import load_main_model, load_simple_model
 from recaptcha_classifier.detection_labels import DetectionLabels
 from recaptcha_classifier.constants import IMAGE_SIZE
 from pydantic import BaseModel
@@ -24,15 +24,14 @@ class PredictionResponse(BaseModel):
 def load_models():
     """Load the models into memory at startup."""
     global model
-    model = load_main_model(device)
-    model.to(device)
+    model = load_simple_model(device)
 
 @app.post("/predict", response_model=PredictionResponse)
 async def predict(response: Response, file: UploadFile = File(...)) -> PredictionResponse:
     try:
         data = await file.read()
         img = Image.open(io.BytesIO(data)).convert("RGB")
-        result = predict(model, device, img)
+        result = inference(model, device, img)
         
         response.headers["Cache-Control"] = "max-age=3600"
         
@@ -43,7 +42,7 @@ async def predict(response: Response, file: UploadFile = File(...)) -> Predictio
             content={"error": str(e)}
         )
     
-def predict(model: torch.nn.Module, device: torch.device, image: Image.Image) -> dict:
+def inference(model: torch.nn.Module, device: torch.device, image: Image.Image) -> dict:
     """
     Handles the prediction logic for the uploaded image.
     """
@@ -58,7 +57,7 @@ def predict(model: torch.nn.Module, device: torch.device, image: Image.Image) ->
         label = DetectionLabels.from_id(id)
         
     return {
-        "label": label.name,
-        "confidence": float(output.max().item()),
+        "label": label,
+        "confidence": f"{float(output.max().item()) * 100:.2f}%",
         "class_id": id
     }
\ No newline at end of file
diff --git a/recaptcha_classifier/server/app.py b/recaptcha_classifier/server/app.py
index 2a2c770da7..732cc5daf3 100644
--- a/recaptcha_classifier/server/app.py
+++ b/recaptcha_classifier/server/app.py
@@ -1,6 +1,7 @@
 import streamlit as st
 import torch
 from typing import Literal
+import requests
 
 
 class StreamlitApp:
@@ -54,10 +55,25 @@ def render_inference_tab(self) -> None:
         st.title("Inference")
         st.write("Please upload an image for inference.")
 
-        uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
+        file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
         
-        if uploaded_file is not None:
-            # process and call api
-            st.image(uploaded_file, caption='Uploaded Image.', use_column_width=True)
+        if file is not None:
+            st.image(file, caption='Uploaded Image.', use_column_width=True)
             if st.button("Run Inference"):
-                pass
\ No newline at end of file
+                files = {"file": {file.getvalue()}}
+                
+                try:
+                    resp = requests.post("http://localhost:8000/predict", files=files)
+                    resp.raise_for_status()
+                    result = resp.json()
+                    
+                    st.success("Prediction successful!")
+                    st.write(f"Label: {result['label']}")
+                    st.write(f"Confidence: {result['confidence']:.2f}")
+                    st.write(f"Class ID: {result['class_id']}")
+                except Exception as e:
+                    st.error(f"Error during inference: {str(e)}")
+
+if __name__ == "__main__":
+    app = StreamlitApp()
+    app.render()
\ No newline at end of file
diff --git a/recaptcha_classifier/server/load_model.py b/recaptcha_classifier/server/load_model.py
index c8d62eb58f..0020ce0982 100644
--- a/recaptcha_classifier/server/load_model.py
+++ b/recaptcha_classifier/server/load_model.py
@@ -1,9 +1,6 @@
 import torch
-from torchvision import transforms
-from PIL import Image
-
+import os
 from ..models.main_model.model_class import MainCNN
-from recaptcha_classifier.detection_labels import DetectionLabels
 
 from recaptcha_classifier.constants import (
     MODELS_FOLDER,
@@ -18,7 +15,7 @@ def load_simple_model(device: torch.device = torch.device("cpu")):
     """
     from ..models.simple_classifier_model import SimpleCNN
     model = SimpleCNN()
-    path = MODELS_FOLDER + "/" + SIMPLE_MODEL_FILE_NAME
+    path = os.path.join(MODELS_FOLDER, SIMPLE_MODEL_FILE_NAME)
     model.load_state_dict(torch.load(path, map_location=device))
     model.to(device)
     model.eval()
@@ -28,7 +25,7 @@ def load_main_model(device: torch.device = torch.device("cpu")):
     """
     Load the main CNN model for image classification.
     """
-    path = MODELS_FOLDER + "/" + MAIN_MODEL_FILE_NAME
+    path = os.path.join(MODELS_FOLDER, MAIN_MODEL_FILE_NAME)
     checkpoint = torch.load(path, map_location=device)
     config = checkpoint['config']
     model = MainCNN(
diff --git a/reports/figures/simple-model.png b/reports/figures/simple-model.png
index 8004263166a5aa75c4ae12de17b94427cdfb128e..55b6774af41e3ff063d12e844f3d64037bfe0cc9 100644
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zA@E%@Gr{xo-4s4`S;jngzQ`>lk8)=uial6YWT`P;O>X8Jb%RX%5<>u~ZV5sb+rWLx
zc&GbcHl-XE*`OX|&n?=rzXa!pu)6cs)8I`h*0cYul4hsr;c^;U)I7Cwc={n|v}yQK
z_=ZV4-v+3QuwjxK>?e2AuEQI310>#I?cmgQ2k!2TXrOKG!ey_DyjCk(6IiVYL0F7-
zI2O3;_aOS_H$bN2e?306EY)C=TH{)#6>QE70+`44DR_Hdz5}<i%IWgnJJA4V>qL)N
zh6|3d><Jg4@jDp*b;>s$yq3|MCD0DIF84wz(jC9nm^*5KU)P9|KTiJq-MCMqe{<a2
zsdg40A_oykI~#PhXW_6)JSCmL^0tjjIv&8h#3g{*ik3vDq@=v)U{DADu`ln0{+hnw
z7W{;WEty*<q%@h+F`b=Yp>?;Xrzh;<cDIf-pLEh2A*sDXqo;7o_7VFWvO+roq1($9
z3XNe?$^09rYj4PX^StSL<-o_;SqzN>HwIVK8-lKKe;TTwK{ObF^=3(m_)X;CEC=$<
z<M=F7rBb19@!F~4VL94jgN@SC((+lEZnu4Bs-$u<0D7UO3IuN5C1yY0A_iQM_7@u-
z3-SSzPupPv*@d?nlGgfFIQIx({(u@t4N9@jDe*Y^OebiorPJ5>EgK<&$>+so{mh!a
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cCzUv6$`d8B49Bd`@g4Bb(bf%DZyl2MU-Q)gpa1{>


From 3cb29f0bc81c9106f150fedef93f665d6bd626dd Mon Sep 17 00:00:00 2001
From: Sinan <sinan_ceviker@yahoo.com>
Date: Thu, 29 May 2025 22:25:57 +0200
Subject: [PATCH 28/32] streamlit working

---
 recaptcha_classifier/server/api.py | 7 +++++--
 recaptcha_classifier/server/app.py | 6 +++---
 2 files changed, 8 insertions(+), 5 deletions(-)

diff --git a/recaptcha_classifier/server/api.py b/recaptcha_classifier/server/api.py
index 33ed2668a4..48e84d623e 100644
--- a/recaptcha_classifier/server/api.py
+++ b/recaptcha_classifier/server/api.py
@@ -3,6 +3,7 @@
 import torch
 from fastapi import FastAPI, File, UploadFile, Response
 from fastapi.responses import JSONResponse
+import torch.nn.functional as F
 from PIL import Image
 from .load_model import load_main_model, load_simple_model
 from recaptcha_classifier.detection_labels import DetectionLabels
@@ -13,7 +14,7 @@
 
 class PredictionResponse(BaseModel):
     label: str
-    confidence: float
+    confidence: str
     class_id: int
 
 
@@ -53,11 +54,13 @@ def inference(model: torch.nn.Module, device: torch.device, image: Image.Image)
     
     with torch.no_grad():
         output = model(tensor)
+        prob = F.softmax(output, dim=1)
+        conf = prob.argmax(dim=1).item()
         id = output.argmax(dim=1).item()
         label = DetectionLabels.from_id(id)
         
     return {
         "label": label,
-        "confidence": f"{float(output.max().item()) * 100:.2f}%",
+        "confidence": f"{conf * 100:.2f}%",
         "class_id": id
     }
\ No newline at end of file
diff --git a/recaptcha_classifier/server/app.py b/recaptcha_classifier/server/app.py
index 732cc5daf3..18e917c83b 100644
--- a/recaptcha_classifier/server/app.py
+++ b/recaptcha_classifier/server/app.py
@@ -60,16 +60,16 @@ def render_inference_tab(self) -> None:
         if file is not None:
             st.image(file, caption='Uploaded Image.', use_column_width=True)
             if st.button("Run Inference"):
-                files = {"file": {file.getvalue()}}
+                files = {"file": (file.name, file.getvalue(), file.type)}
                 
                 try:
-                    resp = requests.post("http://localhost:8000/predict", files=files)
+                    resp = requests.post("http://127.0.0.1:8000/predict", files=files)
                     resp.raise_for_status()
                     result = resp.json()
                     
                     st.success("Prediction successful!")
                     st.write(f"Label: {result['label']}")
-                    st.write(f"Confidence: {result['confidence']:.2f}")
+                    st.write(f"Confidence: {result['confidence']}")
                     st.write(f"Class ID: {result['class_id']}")
                 except Exception as e:
                     st.error(f"Error during inference: {str(e)}")

From eef2c1391df946823b66947f1553ac39541d7949 Mon Sep 17 00:00:00 2001
From: Sinan <sinan_ceviker@yahoo.com>
Date: Thu, 29 May 2025 22:32:00 +0200
Subject: [PATCH 29/32] training real model

---
 recaptcha_classifier/models/main_model/HPoptimizer.py | 7 ++++---
 1 file changed, 4 insertions(+), 3 deletions(-)

diff --git a/recaptcha_classifier/models/main_model/HPoptimizer.py b/recaptcha_classifier/models/main_model/HPoptimizer.py
index f9517388ac..7f669ce0ad 100644
--- a/recaptcha_classifier/models/main_model/HPoptimizer.py
+++ b/recaptcha_classifier/models/main_model/HPoptimizer.py
@@ -1,7 +1,7 @@
 import itertools
 import random
 import pandas as pd
-
+from typing import List
 from recaptcha_classifier.models.main_model.model_class import MainCNN
 from recaptcha_classifier.train.training import Trainer
 
@@ -80,9 +80,10 @@ def optimize_hyperparameters(self,
         return df_opt_data.copy()[:n_models]
 
 
-    def _train_one_model(self, hp_combo) -> None:
+    def _train_one_model(self, hp_combo: List, save_checkpoints: bool) -> None:
         model = MainCNN(n_layers=int(hp_combo[0]), kernel_size=int(hp_combo[1]))
-        self.trainer.train(model=model, lr=hp_combo[2], load_checkpoint=False)
+        self.trainer.train(model=model, lr=hp_combo[2], load_checkpoint=False,
+                           save_checkpoint=save_checkpoints)
 
 
     def _generate_hp_combinations(self, hp) -> list:

From a44af7ab08cee9ddfe04b00bbadf66e107c84b20 Mon Sep 17 00:00:00 2001
From: Sinan <sinan_ceviker@yahoo.com>
Date: Thu, 29 May 2025 22:37:45 +0200
Subject: [PATCH 30/32] trying to train on vm

---
 recaptcha_classifier/data/visualizer.py                       | 4 +++-
 .../features/evaluation/classification_metrics.py             | 4 +++-
 recaptcha_classifier/models/main_model/kfold_validation.py    | 4 +++-
 3 files changed, 9 insertions(+), 3 deletions(-)

diff --git a/recaptcha_classifier/data/visualizer.py b/recaptcha_classifier/data/visualizer.py
index 6a95de7c33..650f7e41d5 100644
--- a/recaptcha_classifier/data/visualizer.py
+++ b/recaptcha_classifier/data/visualizer.py
@@ -77,4 +77,6 @@ def annotate(bars, counts):
         ax.grid(axis='y', linestyle='--', alpha=0.7)
 
         plt.tight_layout()
-        plt.show()
+        #plt.show()
+        plt.savefig("outputs/plot1.png", dpi=300, bbox_inches='tight')
+        plt.close()
\ No newline at end of file
diff --git a/recaptcha_classifier/features/evaluation/classification_metrics.py b/recaptcha_classifier/features/evaluation/classification_metrics.py
index 3ad76ba0c9..4464c230c1 100644
--- a/recaptcha_classifier/features/evaluation/classification_metrics.py
+++ b/recaptcha_classifier/features/evaluation/classification_metrics.py
@@ -62,7 +62,9 @@ def evaluate_classification(y_pred: Tensor,
 
     if cm_plot:
         fig_, ax_ = confmat.plot(labels=class_names if class_names else None)
-        plt.show()
+        #plt.show()
+        plt.savefig("outputs/plot3.png", dpi=300, bbox_inches='tight')
+        plt.close()
 
     return {
         'Accuracy': acc_val.item(),
diff --git a/recaptcha_classifier/models/main_model/kfold_validation.py b/recaptcha_classifier/models/main_model/kfold_validation.py
index 7513e6166f..f55a4e4e06 100644
--- a/recaptcha_classifier/models/main_model/kfold_validation.py
+++ b/recaptcha_classifier/models/main_model/kfold_validation.py
@@ -135,4 +135,6 @@ def plot_results(results: pd.DataFrame) -> None:
         ax.set_xticklabels(metrics, rotation=45, ha='right')
         plt.tight_layout()
         plt.grid(axis='y')
-        plt.show()
+        #plt.show()
+        plt.savefig("outputs/plot5.png", dpi=300, bbox_inches='tight')
+        plt.close()

From 06ed77316ac098d497ef1177d0262fb5a0d5ea24 Mon Sep 17 00:00:00 2001
From: Sinan <sinan_ceviker@yahoo.com>
Date: Thu, 29 May 2025 23:44:32 +0200
Subject: [PATCH 31/32] last changes

---
 README.md                                     | 150 +++++++++++-------
 main.py                                       |   2 +-
 recaptcha_classifier/data/visualizer.py       |   4 +-
 .../evaluation/classification_metrics.py      |   4 +-
 .../models/main_model/kfold_validation.py     |   4 +-
 .../pipeline/base_pipeline.py                 |   8 +-
 .../pipeline/main_model_pipeline.py           |   2 +-
 recaptcha_classifier/server/api.py            |  21 ++-
 recaptcha_classifier/server/app.py            |  15 +-
 recaptcha_classifier/server/load_model.py     |  16 +-
 recaptcha_classifier/train/training.py        |   8 +-
 11 files changed, 155 insertions(+), 79 deletions(-)

diff --git a/README.md b/README.md
index 3d251d83b5..268f6a09d8 100644
--- a/README.md
+++ b/README.md
@@ -1,6 +1,6 @@
-# reCAPTCHA Solver with Multi-Task Learning 🛠️
+# reCAPTCHA Solver 🛠️
 
-This project is part of the **[Applied Machine Learning](https://ocasys.rug.nl/current/catalog/course/WBAI065-05#WBAI065-05.2024-2025.1)** course at the **University of Groningen**, developed by **Group 23**. Our project goal is to build an AI system that is able to automatically solve reCAPTCHA by combining **image classification** and **object detection** using **Multi-Task Learning**.
+This project is part of the **[Applied Machine Learning](https://ocasys.rug.nl/current/catalog/course/WBAI065-05#WBAI065-05.2024-2025.1)** course at the **University of Groningen**, developed by **Group 23**. Our project goal is to build an AI system that is able to automatically solve reCAPTCHA tests through **image classification techniques**.
 
 ---
 
@@ -13,94 +13,136 @@ This project is part of the **[Applied Machine Learning](https://ocasys.rug.nl/c
 
 Before getting started with our project, we encourage you to carefully read the sections below.
 
-
-
 ## Prerequisites
 Make sure you have the following installed:
 
-- **Pipenv**: Pipenv is used for dependency management. This tools enables users to easily create and manage virtual environments. To install Pipenv, use the following command:
+- **Pipenv**: Pipenv is used for dependency management. This tool enables users to easily create and manage virtual environments. To install Pipenv, use the following command:
     ```bash
     $ pip install --user pipenv
     ```
     For detailed installation instructions, [click here](https://pipenv.pypa.io/en/latest/installation.html).
 
 ## Getting Started
-### Setting up your own repository
-1. Fork this repository.
-2. Clone your fork locally.
-3. Configure a remote pointing to the upstream repository to sync changes between your fork and the original repository.
-   ```bash
-   git remote add upstream https://github.com/ivopascal/Applied-ML-Template
-   ```
-   **Don't skip this step.** We might update the original repository, so you should be able to easily pull our changes.
-   
-   To update your forked repo follow these steps:
-   1. `git fetch upstream`
-   2. `git rebase upstream/main`
-   3. `git push origin main`
-      
-      Sometimes you may need to use `git push --force origin main`. Only use this flag the first time you push after you rebased, and be careful as you might overwrite your teammates' changes.
-
-### Pipenv
-This tool is incredibly easy to use. Let's **install** our first package, which you will all need in your projects.
+## Installing Dependencies
+To install the project dependencies run:
 
 ```bash
-pipenv install <package-name>
+pipenv install
 ```
 
-After running this command, you will notice that two files were modified, namely, _Pipfile_ and _Pipfile.lock_. _Pipfile_ is the configuration file that specifies all the dependencies in your virtual environment.
+This will automatically create a virtual environment.
+
+To **activate** the virtual environment, run:
 
-To **uninstall** a package, you can run the command:
 ```bash
-pipenv uninstall <package-name>
+pipenv shell
 ```
 
-To **activate** the virtual environment, run `pipenv shell`. You can now use the environment as you wish. To **deactivate** the environment run the command `exit`.
-
-If you **already have access to a Pipfile**, you can install the dependencies using `pipenv install`.
-
-For a comprehensive list of commands, consult the [official documentation](https://pipenv.pypa.io/en/latest/cli.html).
+To **deactivate** the virtual environment, run:
 
-### Unit testing
-You are expected to test your code using unit testing, which is a technique where small individual components of your code are tested in isolation.
+```bash
+exit
+```
 
-An **example** is given in _tests/test_main.py_, which uses the standard _unittest_ Python module to test whether the function _hello_world_ from _main.py_ works as expected.
+## Testing
+You can run all the unit and integration tests which use the standard _unittest_ Python module with the following command:
 
-To run all the tests developed using _unittest_, simply use:
 ```bash
 python -m unittest discover tests
 ```
 If you wish to see additional details, run it in verbose mode:
+
 ```bash
 python -m unittest discover -v tests
 ```
 
-## Get Coding
-You are now ready to start working on your projects.
+## Repository Structure
+
+To make navigating through the repository easier, you can find its structure below, with additional comments.
 
-We recommend following the same folder structure as in the original repository. This will make it easier for you to have cleaner and consistent code, and easier for us to follow your progress and help you.
 
-Your repository should look something like this:
 ```bash
-├───data  # Stores .csv
-├───models  # Stores .pkl
-├───notebooks  # Contains experimental .ipynbs
-├───project_name
-│   ├───data  # For data processing, not storing .csv
-│   ├───features
-│   └───models  # For model creation, not storing .pkl
+├───data  # Stores the .csv dataset
+├───models  # Stores the .pkl models
+├───notebooks  # Empty
+├───recaptcha_classifier
+│   ├───data  # Data processing
+│   ├───features # Evaluation class
+│   └───models  # Model classes
 ├───reports
 ├───tests
-│   ├───data
-│   ├───features
-│   └───models
+│   ├───data  # Unit tests for data processing
+│   ├───features  # Unit tests for evaluation
+│   ├───integration  # Integration tests
+│   └───models  # Unit tests for models
 ├───.gitignore
 ├───.pre-commit-config.yaml
-├───main.py
+├───main.py 
 ├───train_model.py
-├───Pipfile
+├───Pipfile  # Dependencies
 ├───Pipfile.lock
-├───README.md
+├───README.md  # Instructions
+```
+
+## **🚀 Usage Guide**
+
+### Running the App
+
+1. Activate pipenv environment (if not already activated)
+
+```bash
+pipenv shell
+```
+
+2. To launch any component of our project, run:
+```bash
+python main.py [OPTION]
 ```
 
-**Good luck and happy coding! 🚀**
\ No newline at end of file
+Available list of options:
+--streamlit - Launches Streamlit UI
+--api - starts the FastAPI backend
+--train-simple-cnn - Trains the simple baseline model
+--train-main-cnn - Trains our main model
+
+If no argument has been passed, an interactive menu will appear to let you choose the action.
+
+## API Documentation
+
+### Example API call and response format
+
+You can make a call to the api using curl, by running the command below. Make sure to include a valid file path. The path can either be absolute (full) or relative to your
+current location from command terminal.
+
+```bash
+curl -X POST "http://localhost:8000/predict" -H "accept: application/json" -H "Content-Type: multipart/form-data" -F "file=@<path_to_file>"
+```
+You will get a response in the following format:
+
+```json
+ {
+ "class_id":1,
+ "class_name":"Bridge",
+ "confidence": "99.9%"
+ }
+```
+
+The API is stateless, initializing the model on launching, and caches responses for 1 hour.
+
+
+After running the server, you can access the Documentation:
+
+Interactive API docs (Swagger UI): http://localhost:8000/docs
+
+ReDoc documentation: http://localhost:8000/redoc
+
+These interfaces allow you to test predictions and inspect the request/response formats.
+
+
+
+### Possible error Responses
+
+| Status code  |    Description
+|--------------|----------------------------------------------------------------|
+|    200       |  Succesful Prediction                                          |
+|    422       |  Validation error (eg. file not provided or malformed request) |
\ No newline at end of file
diff --git a/main.py b/main.py
index e34ca1c52b..bcfe6274a5 100644
--- a/main.py
+++ b/main.py
@@ -67,7 +67,7 @@ def train_simple_cnn():
 def train_main_classifier():
     from recaptcha_classifier.pipeline.main_model_pipeline import MainClassifierPipeline
 
-    pipeline = MainClassifierPipeline(epochs=1, k_folds=2)
+    pipeline = MainClassifierPipeline()
     pipeline.run(save_train_checkpoints=False)
     
 def open_api():
diff --git a/recaptcha_classifier/data/visualizer.py b/recaptcha_classifier/data/visualizer.py
index 650f7e41d5..cd3801a83f 100644
--- a/recaptcha_classifier/data/visualizer.py
+++ b/recaptcha_classifier/data/visualizer.py
@@ -77,6 +77,4 @@ def annotate(bars, counts):
         ax.grid(axis='y', linestyle='--', alpha=0.7)
 
         plt.tight_layout()
-        #plt.show()
-        plt.savefig("outputs/plot1.png", dpi=300, bbox_inches='tight')
-        plt.close()
\ No newline at end of file
+        plt.show()
\ No newline at end of file
diff --git a/recaptcha_classifier/features/evaluation/classification_metrics.py b/recaptcha_classifier/features/evaluation/classification_metrics.py
index 4464c230c1..3ad76ba0c9 100644
--- a/recaptcha_classifier/features/evaluation/classification_metrics.py
+++ b/recaptcha_classifier/features/evaluation/classification_metrics.py
@@ -62,9 +62,7 @@ def evaluate_classification(y_pred: Tensor,
 
     if cm_plot:
         fig_, ax_ = confmat.plot(labels=class_names if class_names else None)
-        #plt.show()
-        plt.savefig("outputs/plot3.png", dpi=300, bbox_inches='tight')
-        plt.close()
+        plt.show()
 
     return {
         'Accuracy': acc_val.item(),
diff --git a/recaptcha_classifier/models/main_model/kfold_validation.py b/recaptcha_classifier/models/main_model/kfold_validation.py
index f55a4e4e06..7513e6166f 100644
--- a/recaptcha_classifier/models/main_model/kfold_validation.py
+++ b/recaptcha_classifier/models/main_model/kfold_validation.py
@@ -135,6 +135,4 @@ def plot_results(results: pd.DataFrame) -> None:
         ax.set_xticklabels(metrics, rotation=45, ha='right')
         plt.tight_layout()
         plt.grid(axis='y')
-        #plt.show()
-        plt.savefig("outputs/plot5.png", dpi=300, bbox_inches='tight')
-        plt.close()
+        plt.show()
diff --git a/recaptcha_classifier/pipeline/base_pipeline.py b/recaptcha_classifier/pipeline/base_pipeline.py
index fec7ac4df6..8355855936 100644
--- a/recaptcha_classifier/pipeline/base_pipeline.py
+++ b/recaptcha_classifier/pipeline/base_pipeline.py
@@ -14,7 +14,8 @@ def __init__(self,
                  save_folder: str = "checkpoints",
                  model_file_name: str = "model.pt",
                  optimizer_file_name: str = "optimizer.pt",
-                 scheduler_file_name: str = "scheduler.pt"
+                 scheduler_file_name: str = "scheduler.pt",
+                 early_stopping: bool = True
                  ):
 
         self.lr = lr
@@ -24,7 +25,7 @@ def __init__(self,
         self.model_file_name = model_file_name
         self.optimizer_file_name = optimizer_file_name
         self.scheduler_file_name = scheduler_file_name
-
+        self.early_stopping = early_stopping
         self._class_map = DetectionLabels
         self._loaders = None
         self._data = None
@@ -55,7 +56,8 @@ def _initialize_trainer(self) -> Trainer:
                        model_file_name=self.model_file_name,
                        optimizer_file_name=self.optimizer_file_name,
                        scheduler_file_name=self.scheduler_file_name,
-                       device=self.device)
+                       device=self.device,
+                       early_stopping=self.early_stopping)
 
     @property
     def class_map_length(self):
diff --git a/recaptcha_classifier/pipeline/main_model_pipeline.py b/recaptcha_classifier/pipeline/main_model_pipeline.py
index 31538aed0a..95f46efd5a 100644
--- a/recaptcha_classifier/pipeline/main_model_pipeline.py
+++ b/recaptcha_classifier/pipeline/main_model_pipeline.py
@@ -55,11 +55,11 @@ def _run_kfold_cross_validation(self) -> None:
             train_loader=self._loaders["train"],
             val_loader=self._loaders["val"],
             k_folds=self.k_folds,
-            hp_optimizer=self._hp_optimizer,
             device=self.device
         )
         
         best_hp = self._hp_optimizer.get_best_hp()
+        
         self._kfold.run_cross_validation(hp=best_hp)
 
         self.lr = best_hp[2]
diff --git a/recaptcha_classifier/server/api.py b/recaptcha_classifier/server/api.py
index 48e84d623e..8b7b30c2c5 100644
--- a/recaptcha_classifier/server/api.py
+++ b/recaptcha_classifier/server/api.py
@@ -5,11 +5,12 @@
 from fastapi.responses import JSONResponse
 import torch.nn.functional as F
 from PIL import Image
-from .load_model import load_main_model, load_simple_model
+from .load_model import load_main_model, get_model_path
 from recaptcha_classifier.detection_labels import DetectionLabels
 from recaptcha_classifier.constants import IMAGE_SIZE
 from pydantic import BaseModel
 from typing import Literal
+import os
 
 
 class PredictionResponse(BaseModel):
@@ -20,15 +21,29 @@ class PredictionResponse(BaseModel):
 
 app = FastAPI()
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model = None
 
 @app.on_event("startup")
 def load_models():
     """Load the models into memory at startup."""
     global model
-    model = load_simple_model(device)
+    model_path = get_model_path("main")
+    if os.path.exists(model_path):
+        model = load_main_model(device)
+    else:
+        print("Model file not found. API will return an error for predictions"
+            "until model is available.")
 
 @app.post("/predict", response_model=PredictionResponse)
-async def predict(response: Response, file: UploadFile = File(...)) -> PredictionResponse:
+async def predict(response: Response,
+                  file: UploadFile = File(...)
+                  ) -> PredictionResponse:
+    if model is None:
+        return JSONResponse(
+            status_code=503,
+            content={"error": "Model not loaded. Please train "
+                     "or download first."}
+        )
     try:
         data = await file.read()
         img = Image.open(io.BytesIO(data)).convert("RGB")
diff --git a/recaptcha_classifier/server/app.py b/recaptcha_classifier/server/app.py
index 18e917c83b..e75e889683 100644
--- a/recaptcha_classifier/server/app.py
+++ b/recaptcha_classifier/server/app.py
@@ -2,6 +2,8 @@
 import torch
 from typing import Literal
 import requests
+from recaptcha_classifier.pipeline.main_model_pipeline import MainClassifierPipeline
+from recaptcha_classifier.pipeline.simple_cnn_pipeline import SimpleClassifierPipeline
 
 
 class StreamlitApp:
@@ -46,7 +48,18 @@ def render_training_tab(self) -> None:
                      help="CUDA is not available on this machine.")
             
         if st.button("Start Training"):
-            # logic here
+            if self.model_type == "Simple":
+                pipeline = SimpleClassifierPipeline(lr=self.lr,
+                                                    epochs=self.epochs,
+                                                    early_stopping=self.early_stopping,
+                                                    device=self.device)
+                pipeline.run()
+            else:
+                pipeline = MainClassifierPipeline(lr=self.lr,
+                                                  epochs=self.epochs,
+                                                  early_stopping=self.early_stopping,
+                                                  device=self.device)
+                pipeline.run()
             st.success(f"Started training {self.model_type} model with "
                        f"learning rate {self.lr}, epochs {self.epochs}, "
                        f"early stopping: {self.early_stopping}, on {self.device}.")
diff --git a/recaptcha_classifier/server/load_model.py b/recaptcha_classifier/server/load_model.py
index 0020ce0982..e8fa22bf75 100644
--- a/recaptcha_classifier/server/load_model.py
+++ b/recaptcha_classifier/server/load_model.py
@@ -15,7 +15,7 @@ def load_simple_model(device: torch.device = torch.device("cpu")):
     """
     from ..models.simple_classifier_model import SimpleCNN
     model = SimpleCNN()
-    path = os.path.join(MODELS_FOLDER, SIMPLE_MODEL_FILE_NAME)
+    path = get_model_path("simple")
     model.load_state_dict(torch.load(path, map_location=device))
     model.to(device)
     model.eval()
@@ -25,7 +25,7 @@ def load_main_model(device: torch.device = torch.device("cpu")):
     """
     Load the main CNN model for image classification.
     """
-    path = os.path.join(MODELS_FOLDER, MAIN_MODEL_FILE_NAME)
+    path = get_model_path("main")
     checkpoint = torch.load(path, map_location=device)
     config = checkpoint['config']
     model = MainCNN(
@@ -35,4 +35,14 @@ def load_main_model(device: torch.device = torch.device("cpu")):
     model.load_state_dict(checkpoint['model_state_dict'])
     model.to(device)
     model.eval()
-    return model
\ No newline at end of file
+    return model
+
+def get_model_path(model_type: str) -> str:
+    """
+    Get the path to the model file based on the model type.
+    """
+    if model_type == "simple":
+        return os.path.join(MODELS_FOLDER, SIMPLE_MODEL_FILE_NAME)
+    elif model_type == "main":
+        return os.path.join(MODELS_FOLDER, MAIN_MODEL_FILE_NAME)
+    return None
\ No newline at end of file
diff --git a/recaptcha_classifier/train/training.py b/recaptcha_classifier/train/training.py
index 661bf3a34e..83ee75e507 100644
--- a/recaptcha_classifier/train/training.py
+++ b/recaptcha_classifier/train/training.py
@@ -20,7 +20,8 @@ def __init__(self,
                  optimizer_file_name: str ='optimizer.pt',
                  scheduler_file_name: str ='scheduler.pt',
                  device: torch.device |None = None,
-                 early_stop_threshold: int = 5
+                 early_stop_threshold: int = 5,
+                 early_stopping: bool = True
                  ):
         """
         Constructor for Trainer class.
@@ -47,7 +48,7 @@ def __init__(self,
         self._loss_acc_history = []
         self.optimizer = None
         self.scheduler = None
-        
+        self._does_early_stop = early_stopping
         self._early_stop_threshold = early_stop_threshold
         self._best_val_loss = float('inf')
         self._stagnation_counter = 0
@@ -126,12 +127,11 @@ def train(self,
             val_acc = val_accuracy_counter.compute().item()
             print(f"Epoch {epoch+1} - Val loss: {val_loss:.4f}, Val accuracy: {val_acc:.4f}")
 
-            if self._early_stop(val_loss):
+            if self._does_early_stop and self._early_stop(val_loss):
                 print(f"Early stopping at epoch {epoch + 1}.")
                 print(f"Best validation loss: {self._best_val_loss:.4f}")
                 break
 
-
     def _train_one_epoch(self, model, train_accuracy_counter, train_loss_counter, train_progress_bar):
         model.train()
         for data, targets in train_progress_bar:

From 0a47525f89e88c82de9325b7ec0ef6efb6ee4bdb Mon Sep 17 00:00:00 2001
From: Sinan <sinan_ceviker@yahoo.com>
Date: Fri, 30 May 2025 00:20:21 +0200
Subject: [PATCH 32/32] new img

---
 .github/workflows/ci.yml       |  32 --------------------------------
 reports/figures/main-model.png | Bin 75501 -> 75513 bytes
 2 files changed, 32 deletions(-)
 delete mode 100644 .github/workflows/ci.yml

diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
deleted file mode 100644
index 88812b3b37..0000000000
--- a/.github/workflows/ci.yml
+++ /dev/null
@@ -1,32 +0,0 @@
-name: Integration Tests
-
-on:
-    pull_request:
-        branches:
-            - main
-        types:
-            - opened
-            - synchronize
-            - reopened
-
-jobs:
-    integration-tests:
-        runs-on: ubuntu-latest
-        steps:
-            - name: Checkout code
-              uses: actions/checkout@v4
-
-            - name: Set up Python
-              uses: actions/setup-python@v5
-              with:
-                  python-version: '3.10'
-
-            - name: Install dependencies
-              run: |
-                  python -m pip install --upgrade pip
-                  pip install pipenv
-                  pipenv install --dev
-
-            - name: Run integration tests
-              run: |
-                  pipenv run python -m unittest discover -s tests/integration
\ No newline at end of file
diff --git a/reports/figures/main-model.png b/reports/figures/main-model.png
index 4d076e623dd1a23abf0a343449b4184b313f8aab..726336548d7dac8e2a43cdd63616551081442fc8 100644
GIT binary patch
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