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+ ---
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+ language: en
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+ license: mit
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+ tags:
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+ - vision
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+ - video-classification
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+ model-index:
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+ - name: nielsr/xclip-base-patch16-ucf-4-shot
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+ results:
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+ - task:
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+ type: video-classification
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+ dataset:
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+ name: UCF101
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+ type: ucf101
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+ metrics:
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+ - type: top-1 accuracy
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+ value: 83.4
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+ ---
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+
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+ # X-CLIP (base-sized model)
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+
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+ X-CLIP model (base-sized, patch resolution of 16) trained in a few-shot fashion (K=4) 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).
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+
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+ This model was trained using 32 frames per video, at a resolution of 224x224.
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+
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+ 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.
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+
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+ ## Model description
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+
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+ 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.
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+
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+ ![X-CLIP architecture](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/xclip_architecture.png)
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+
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+ This allows the model to be used for tasks like zero-shot, few-shot or fully supervised video classification and video-text retrieval.
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+
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+ ## Intended uses & limitations
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+
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+ 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
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+ fine-tuned versions on a task that interests you.
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+
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+ ### How to use
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+
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+ For code examples, we refer to the [documentation](https://huggingface.co/transformers/main/model_doc/xclip.html#).
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+
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+ ## Training data
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+
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+ This model was trained on [UCF101](https://www.crcv.ucf.edu/data/UCF101.php).
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+
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+ ### Preprocessing
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+
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+ The exact details of preprocessing during training can be found [here](https://github.com/microsoft/VideoX/blob/40f6d177e0a057a50ac69ac1de6b5938fd268601/X-CLIP/datasets/build.py#L247).
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+
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+ The exact details of preprocessing during validation can be found [here](https://github.com/microsoft/VideoX/blob/40f6d177e0a057a50ac69ac1de6b5938fd268601/X-CLIP/datasets/build.py#L285).
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+
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+ 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.
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+
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+ ## Evaluation results
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+
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+ This model achieves a top-1 accuracy of 83.4%.