House Monitoring – TensorFlow Object Detection (TF1)

End-to-end pipeline to train and validate an SSD MobileNet V2 object detection model using the TensorFlow Object Detection API on custom, PASCAL‑VOC–style annotated data. Includes dataset preparation (XML→CSV→TFRecord), training with TensorBoard, exporting a SavedModel, and running inference/visualization on test images.

Colab-first: The notebooks are written for Google Colab and mount Google Drive for data/model I/O. You can also run locally with TF 1.15 and the TensorFlow Object Detection API (legacy).

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Repository structure

.
├── House_monitoring.ipynb                 # training & export
├── vaildation_HM.ipynb                    # quick inference
├── comment_vaildation_HM_(1)_txt.ipynb    # commented inference walkthrough
└── (data/, training/, fine_tuned_model/, ...)  # created at runtime on Drive/locally

What the notebooks do

• House_monitoring.ipynb — Main training pipeline: installs deps, converts VOC XML to CSV, builds TFRecords, configures SSD MobileNet V2, runs training with TensorBoard & exports a SavedModel.
• vaildation_HM.ipynb — Loads the exported SavedModel and runs inference on sample/test images with visualizations.
• comment_vaildation_HM_(1)_txt.ipynb — Commented validation walkthrough with the same inference flow as above, plus additional explanations.

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Setup (Colab)

The notebooks install exact versions inside the runtime. Key packages observed in the code:

• tensorflow==1.15.0 (with %tensorflow_version 1.x in Colab)
• tf_slim
• pycocotools, lxml, Pillow, matplotlib, Cython
• (occasionally) lvis
• TensorFlow Object Detection API (legacy, compiled with protoc)

Steps you will see in the notebooks:

1. Mount Drive

   from google.colab import drive
   drive.mount('/drive')

2. Install dependencies (done in-notebook using pip/apt-get).
   Compile TF OD API protos:

   %cd /drive/MyDrive/Housedamage/models/research/
   protoc object_detection/protos/*.proto --python_out=.

3. (Optional) TensorBoard via ngrok tunnel for remote viewing while training.

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Data preparation

The training notebook expects PASCAL VOC XML annotations and converts them to CSV, then to TFRecords.

1) XML → CSV

A helper like xml_to_csv() scans train_label/ and test_label/ folders and produces CSVs with columns: filename, width, height, class, xmin, ymin, xmax, ymax

Place your images + XMLs in:

• train_label/ (training split)
• test_label/ (evaluation split)

2) Label map

Create data/label_map.pbtxt mapping your classes to integer IDs, e.g.:

item { id: 1  name: "class_a" }
item { id: 2  name: "class_b" }
# add more as needed

3) CSV → TFRecord

The notebook builds train.record and test.record from those CSVs and images.

Tip: Keep image file names consistent with the filename field in the XMLs.

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Model & training

The pipeline uses SSD MobileNet V2 (COCO) configuration adapted for your dataset. In the notebook you will see a block defining or referencing the .config with fields like:

• num_classes (match to your label map)
• fine_tune_checkpoint (COCO checkpoint)
• train_input_reader / eval_input_reader with *.record paths and label_map_path
• image_resizer (e.g., 300×300)

Start training via:

python3 object_detection/model_main.py   --pipeline_config_path=/drive/MyDrive/Housedamage/models/research/object_detection/samples/configs/ssd_mobilenet_v2_coco.config   --model_dir=training/

Monitoring

TensorBoard is launched in-notebook and (optionally) exposed via ngrok. Loss curves and eval metrics appear under training/.

Exporting the model

After training, the notebook picks the latest model.ckpt-XXXX and exports an inference graph / SavedModel:

python object_detection/export_inference_graph.py   --input_type=image_tensor   --pipeline_config_path=.../ssd_mobilenet_v2_coco.config   --output_directory=./fine_tuned_model   --trained_checkpoint_prefix=training/model.ckpt-<STEP>

In the commented validation, a SavedModel is loaded with:

tf.saved_model.load("/drive/MyDrive/Housedamage/models/research/fine_tuned_model/saved_model")

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Inference & visualization

Both validation notebooks load the SavedModel, then run detection + draw boxes/labels:

def run_inference_for_single_image(model, image_np):
    input_tensor = tf.convert_to_tensor(image_np)[tf.newaxis, ...]
    outputs = model(input_tensor)
    # post-process: num_detections, classes (int), boxes, scores, masks (if any)
    ...

from object_detection.utils import label_map_util, visualization_utils as vis_util

# Label map & test images
PATH_TO_LABELS = '/drive/MyDrive/Housedamage/data/label_map.pbtxt'
TEST_IMAGE_PATHS = list(Path('/drive/MyDrive/Housedamage/data/test').glob('*.jpg'))

# Visualize
vis_util.visualize_boxes_and_labels_on_image_array(
    image_np,
    output_dict['detection_boxes'],
    output_dict['detection_classes'],
    output_dict['detection_scores'],
    category_index,
    use_normalized_coordinates=True,
    line_thickness=2
)

Save or display the annotated images directly in Colab.

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How to reproduce (quick start)

1. Put your dataset on Drive, e.g. under /drive/MyDrive/Housedamage/ with subfolders:

   data/
     label_map.pbtxt
     train_label/  # images + XML
     test_label/   # images + XML
   

2. Open House_monitoring.ipynb in Colab and run all cells (edit paths/classes as needed).
3. After training finishes and the model is exported, open vaildation_HM.ipynb (or the commented version) and run inference on sample images in data/test/.
4. (Optional) Commit exported artifacts (config, label map, a few sample images) to this repo for reproducibility.

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Results

Add sample detections, mAP/precision, confusion matrix, and training curves here.
E.g., include a few before/after images and report precision @ IoU=0.5 on your test split.

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Notes

• TensorFlow 1.x is EOL; consider porting to TF2 OD API or [Ultralytics YOLOv5/8] for simpler modern pipelines.
• If you change image size or aspect ratio, ensure your training config and preprocessing match.
• Keep the label map IDs stable between training and inference.

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Citation / Acknowledgements

• TensorFlow Object Detection API (legacy): https://github.com/tensorflow/models/tree/master/research/object_detection
• SSD MobileNet V2 (COCO) checkpoint & sample configs from TensorFlow Models.

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