xclip base patch32

Introduction

The X-CLIP (base-sized model) is designed for general video-language understanding, extending the capabilities of the CLIP model to video classification. It was trained on the Kinetics-400 dataset and can be applied to zero-shot, few-shot, or fully-supervised video classification tasks.

Architecture

The X-CLIP model builds on the CLIP architecture, incorporating video-language understanding through contrastive training on video-text pairs. It processes video input as sequences of frames, maintaining a patch resolution of 32 and a frame resolution of 224x224.

Training

The model was trained on the Kinetics-400 dataset, which includes a variety of video clips. Training involves contrastive learning on video-text pairs, enabling the model to associate textual descriptions with corresponding video content. The preprocessing during training and validation involves resizing, cropping, and normalizing video frames.

Guide: Running Locally

1. Setup Environment: Install the required dependencies such as PyTorch and Hugging Face Transformers.
2. Download Model: Retrieve the model from the Hugging Face model hub.
3. Preprocess Data: Follow the preprocessing steps used during training, including resizing, cropping, and normalization.
4. Run Inference: Use the model to perform video classification or retrieval tasks.
5. Evaluation: Optionally, evaluate the model's performance on your data.

For optimal performance, consider using cloud GPU services like AWS, Google Cloud, or Azure.

License

The X-CLIP model is released under the MIT license, allowing for wide usage and modification in various applications.