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xclip-base-patch16-ucf-2-shot

Video Classification
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xclip
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xclip-base-patch16-ucf-2-shot
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---
language: en
license: mit
tags:
- vision
- video-classification
model-index:
- name: nielsr/xclip-base-patch16-ucf-2-shot
  results:
  - task:
      type: video-classification
    dataset:
      name: UCF101
      type: ucf101
    metrics:
    - type: top-1 accuracy
      value: 76.4
---

# X-CLIP (base-sized model) 

X-CLIP model (base-sized, patch resolution of 16) trained in a few-shot fashion (K=2) on [UCF101](https://www.crcv.ucf.edu/data/UCF101.php). It was introduced in the paper [Expanding Language-Image Pretrained Models for General Video Recognition](https://arxiv.org/abs/2208.02816) by Ni et al. and first released in [this repository](https://github.com/microsoft/VideoX/tree/master/X-CLIP).

This model was trained using 32 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](https://huggingface.co/docs/transformers/model_doc/clip) for general video-language understanding. The model is trained in a contrastive way on (video, text) pairs. 

![X-CLIP architecture](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/xclip_architecture.png)

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](https://huggingface.co/models?search=microsoft/xclip) to look for
fine-tuned versions on a task that interests you.

### How to use

For code examples, we refer to the [documentation](https://huggingface.co/transformers/main/model_doc/xclip.html#).

## Training data

This model was trained on [UCF101](https://www.crcv.ucf.edu/data/UCF101.php).

### Preprocessing

The exact details of preprocessing during training can be found [here](https://github.com/microsoft/VideoX/blob/40f6d177e0a057a50ac69ac1de6b5938fd268601/X-CLIP/datasets/build.py#L247).

The exact details of preprocessing during validation can be found [here](https://github.com/microsoft/VideoX/blob/40f6d177e0a057a50ac69ac1de6b5938fd268601/X-CLIP/datasets/build.py#L285).

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 76.4%.