62 lines
2.7 KiB
Markdown
62 lines
2.7 KiB
Markdown
---
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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-patch32
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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: Kinetics 400
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type: kinetics-400
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metrics:
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- type: top-1 accuracy
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value: 80.4
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- type: top-5 accuracy
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value: 95.0
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---
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# X-CLIP (base-sized model)
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X-CLIP model (base-sized, patch resolution of 32) trained fully-supervised on [Kinetics-400](https://www.deepmind.com/open-source/kinetics). 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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This model was trained using 8 frames per video, at a resolution of 224x224.
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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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## Model description
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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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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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## Intended uses & limitations
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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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### How to use
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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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## Training data
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This model was trained on [Kinetics-400](https://www.deepmind.com/open-source/kinetics).
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### Preprocessing
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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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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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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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## Evaluation results
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This model achieves a top-1 accuracy of 80.4% and a top-5 accuracy of 95.0%.
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