Cat-Dog Classifier Project

This project implements an end-to-end pipeline for building, training, and deploying a Cat-Dog Classifier using PyTorch and FastAPI. The classifier is based on a simple convolutional neural network (CNN) trained on the CIFAR-10 dataset.

---

Features

• Data Preparation: Script to download and preprocess the CIFAR-10 dataset.
• Model Training: Train a CNN model to classify images into one of 10 categories.
• Model Deployment: Deploy the trained model as a REST API using FastAPI.
• Docker Support: Dockerfile provided for easy deployment.

---

Project Structure

cat_dog_classifier/
├── data/                # Directory for storing dataset
├── model/               # Directory for saving the trained model
├── scripts/             # Python scripts for data preparation and training
│   ├── data_prep.py     # Script to download and preprocess the dataset
│   ├── train_model.py   # Script to train the CNN model
├── app.py               # FastAPI app for model inference
├── requirements.txt     # Dependencies required for the project
├── Dockerfile           # Dockerfile for deploying the API

---

Getting Started

Prerequisites

• Python 3.8 or higher
• Pip
• PyTorch
• FastAPI
• Docker (optional, for containerization)

Installation

1. Clone the repository:

   git clone <repository_url>
   cd cat_dog_classifier

2. Install the required dependencies:

   pip install -r requirements.txt

---

Steps to Run the Project

1. Data Preparation

Run the script to download and preprocess the CIFAR-10 dataset:

python scripts/data_prep.py

2. Model Training

Train the CNN model:

python scripts/train_model.py

The trained model will be saved in the model/ directory as cat_dog_model.pth.

3. Start the API

Run the FastAPI app:

uvicorn app:app --reload

Access the API documentation at http://127.0.0.1:8000/docs.

4. Make Predictions

Use the /predict endpoint to classify an image. Example using curl:

curl -X POST "http://127.0.0.1:8000/predict/" -H "accept: application/json" -H "Content-Type: multipart/form-data" -F "file=@path_to_image.jpg"

---

Docker Deployment

1. Build the Docker image:

   docker build -t cat-dog-api .

2. Run the container:

   docker run -p 8000:8000 cat-dog-api

3. Access the API at http://127.0.0.1:8000.

---

License

This project is licensed under the MIT License. See the LICENSE file for details.