This project aims to demonstrate how to perform ImageNet classification using data version control. By leveraging remote storage for versioning data and integrating it with code hosted on GitHub, this project showcases a streamlined workflow for managing both code and data effectively.

ImageNet is a large-scale dataset for image classification, containing millions of labeled images across thousands of categories. This project focuses on building and training deep learning models to classify images from the ImageNet dataset.

• Data Version Control: Utilizes remote storage for versioning datasets.
• GitHub Integration: Seamless interaction between code and versioned data stored remotely.
• Deep Learning Models: Implementation of various deep learning architectures for ImageNet classification.
• Evaluation: Metrics and analysis of model performance on the ImageNet dataset.

• Python 3.x
• TensorFlow or PyTorch
• Git
• Remote storage provider account (e.g., AWS S3, Google Cloud Storage)

Clone the repository:

```
git clone https://github.com/your_username/image-net-classification.git
```

1. Data Versioning:

   • Upload ImageNet dataset or your custom dataset to the remote storage.
   • Use version control mechanisms provided by the storage provider to manage dataset versions.

2. Training:

   • Modify the training scripts to point to the dataset location in the remote storage.
   • Train the models using the provided scripts or customize them as needed.

3. Evaluation:

   • Evaluate model performance on the test set using evaluation scripts.
   • Analyze metrics such as accuracy, precision, recall, and F1-score.

Contributions are welcome! If you have ideas for improvements or new features, feel free to submit a pull request.

This project is licensed under the MIT License.

• ImageNet
• Data Version Control
• GitHub