Pokemon Image Classification

This project is a deep learning model for classifying images of Pokemon using TensorFlow and Keras. The model is built on top of a pre-trained ResNet50 architecture and fine-tuned for the specific task of Pokemon classification.

Project Structure

• model.ipynb: Jupyter notebook containing the code for data preprocessing, model building, training, and evaluation.
• .gitignore: Specifies files and directories to be ignored by Git.

Setup

1. Clone the repository:

   git clone <repository_url>
   cd <repository_directory>

2. Download the dataset:

   # !kaggle datasets download -d thedagger/pokemon-generation-one
   # !unzip /content/pokemon-generation-one.zip

Data Preprocessing

• The dataset is loaded using image_dataset_from_directory with a validation split of 20%.
• Data augmentation techniques such as random flipping, rotation, zoom, contrast, brightness, and translation are applied to the training dataset.
• The images are resized to 224x224 pixels and preprocessed using the preprocess_input function from tensorflow.keras.applications.resnet50.

Model Architecture

• The base model is a pre-trained ResNet50 without the top classification layers.
• The base model's layers are frozen to prevent them from being updated during training.
• A new model is created on top of the base model with additional layers:
  • GlobalAveragePooling2D
  • Dense layer with 128 units and ReLU activation
  • Dropout layer with a rate of 0.5
  • Dense output layer with softmax activation for classification

Training

• The model is compiled with the Adam optimizer, sparse categorical cross-entropy loss, and accuracy as the metric.
• Early stopping is used to monitor the validation loss and restore the best weights.
• Class weights are computed to handle class imbalance.
• The model is trained for 10 epochs with the specified class weights and early stopping callback.

Evaluation

• The training and validation loss are plotted to visualize the model's performance.
• The model's predictions can be evaluated using metrics such as classification report (commented out in the notebook).

Usage

1. Run the Jupyter notebook model.ipynb to execute the entire workflow from data preprocessing to model evaluation.
2. Modify the notebook as needed to experiment with different hyperparameters, data augmentation techniques, or model architectures.

License

This project is licensed under the MIT License.