readme: ---
  language: en
  license: mit
  tags:
  - vision
  - video-classification
  model-index:
  - name: nielsr/xclip-base-patch16-hmdb-2-shot
    results:
    - task:
        type: video-classification
      dataset:
        name: HMDB-51
        type: hmdb-51
      metrics:
      - type: top-1 accuracy
        value: 53.0
  ---
  
  # X-CLIP (base-sized model) 
  
  X-CLIP model (base-sized, patch resolution of 16) trained in a few-shot fashion (K=2) on [HMDB-51](https://serre-lab.clps.brown.edu/resource/hmdb-a-large-human-motion-database/). 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 [HMDB-51](https://serre-lab.clps.brown.edu/resource/hmdb-a-large-human-motion-database/).
  
  ### 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 53.0%.
  