X-CLIP (base-sized model)

X-CLIP model (base-sized, patch resolution of 32) trained fully-supervised on Kinetics-400 . It was introduced in the paper Expanding Language-Image Pretrained Models for General Video Recognition by Ni et al. and first released in this repository .

This model was trained using 16 frames per video, at a resolution of 224x224.

Disclaimer: The team releasing X-CLIP did not write a model card for this model so this model card has been written by the Hugging Face team.

Model description

X-CLIP is a minimal extension of CLIP for general video-language understanding. The model is trained in a contrastive way on (video, text) pairs.

This allows the model to be used for tasks like zero-shot, few-shot or fully supervised video classification and video-text retrieval.

Intended uses & limitations

You can use the raw model for determining how well text goes with a given video. See the model hub to look for fine-tuned versions on a task that interests you.

How to use

For code examples, we refer to the documentation .

Training data

This model was trained on Kinetics-400 .

Preprocessing

The exact details of preprocessing during training can be found here .

The exact details of preprocessing during validation can be found here .

During validation, one resizes the shorter edge of each frame, after which center cropping is performed to a fixed-size resolution (like 224x224). Next, frames are normalized across the RGB channels with the ImageNet mean and standard deviation.

Evaluation results

This model achieves a top-1 accuracy of 81.1% and a top-5 accuracy of 95.5%.