This repository contains an implementation of an image classification model using TensorFlow. The project demonstrates how to build, train, and evaluate a neural network for classifying images into different categories.
- Deep learning model implementation using TensorFlow/Keras
- Image preprocessing and data augmentation techniques
- Training pipeline for image classification
- Model evaluation and performance metrics
- Prediction functionality for new images
The project utilizes convolutional neural networks (CNNs) to extract features from images and classify them into predefined categories. It showcases:
- Data loading and preprocessing
- Model architecture design
- Training process with validation
- Evaluation of model performance
- Making predictions on new data
- TensorFlow/Keras
- Python
- NumPy
- Matplotlib for visualization
The repository provides code examples for training your own image classifier as well as using pre-trained models for inference. It serves as both a learning resource and a practical implementation that can be adapted for various image classification tasks.