This project demonstrates how to build an image recognition system using a Convolutional Neural Network (CNN). The system can classify images into different categories based on a dataset of labeled images. It uses deep learning techniques for training and predicting image labels.

• Image classification using CNNs
• Preprocessing pipeline including image resizing, normalization, and data augmentation
• Customizable architecture with options to modify layers, activation functions, etc.
• Training, evaluation, and inference modules
• Support for GPU acceleration with TensorFlow or PyTorch

                image-recognition/ 
                                 │
                                 ├── data/ # Dataset files 
                                         │ 
                                         ├── train # Training data (images) 
                                         │ 
                                         └── test # Test data (images) 
                                 ├── src/ 
                                        │ 
                                        ├── model.py # CNN model definition 
                                        │ 
                                        ├── train.py # Training script 
                                        │ 
                                        ├── evaluate.py # Evaluation script 
                                        │ 
                                        ├── predict.py # Prediction script 
                                        │ 
                                        └── utils.py # Utility functions (image loading, preprocessing, etc.) 
                                 ├── tests/ # Unit tests 
                                          │ 
                                          └── test_model.py # Unit tests for the CNN model 
                                 ├── requirements.txt # Dependencies 
                                 └── README.md # Project documentation

1. Clone the repository:

   git clone https://github.com/yourusername/image-recognition.git
   cd image-recognition
   
   

2. Install the required packages:

           pip install -r requirements.txt
   

The project expects a folder structure where the training data is stored in subfolders for each category.

            data/train/cat
            data/train/dog
            data/test/cat
            data/test/dog

You can use any labeled image dataset (e.g., CIFAR-10, ImageNet, or a custom dataset). Make sure the images are placed in corresponding subdirectories representing their class labels.

1. Training the Model Train the CNN model on your dataset:

     python src/train.py
   

The script will:-

1. Load the training images from the /data/train directory.

2. Preprocess the images (resize, normalize, augment).

3. Train the CNN model on the training dataset.

4. Save the trained model weights.

5. Evaluating the Model

Evaluate the performance of the trained model on the test dataset:

          python src/evaluate.py

This will load the test images from the /data/test directory and compute evaluation metrics like accuracy, precision, recall, and F1-score.

3. Predicting New Images

Use the trained model to classify new images:

   python src/predict.py --image path_to_image.jpg

The script will load a single image, preprocess it, and output the predicted class label.

Unit tests are provided to ensure that the core functions of the CNN model and utilities work as expected. Run the tests using:

            python -m unittest discover -s tests

The project uses deep learning frameworks like TensorFlow or PyTorch, along with additional libraries for image processing. You can install the required dependencies by running:

                              pip install -r requirements.txt

Required packages:

1. tensorflow or pytorch
2. numpy
3. pandas
4. opencv-python
5. scikit-learn

1. Transfer Learning: Add support for transfer learning using pre-trained models like VGG16, ResNet, or MobileNet.
2. Hyperparameter Tuning: Implement grid search or random search for optimizing hyperparameters.
3. Real-time Image Recognition: Add a feature to classify images in real-time using webcam input.

This project is licensed under the MIT License.