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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...
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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 8 frames per video, at a resolution of 224x224.
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2 extraitsDisclaimer: 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.
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.
X-CLIP architecture
This allows the model to be used for tasks like zero-shot, few-shot or fully supervised video classification and video-text retrieval.
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.
For code examples, we refer to the documentation.
This model was trained on Kinetics-400.
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.
This model achieves a top-1 accuracy of 80.4% and a top-5 accuracy of 95.0%.
--- language: en license: mit tags: - vision - video-classification model-index: - name: nielsr/xclip-base-patch32 results: - task: type: video-classification dataset: name: Kinetics 400 type: kinetics-400 metrics: - type: top-1 accuracy value: 80.4 - type: top-5 accuracy value: 95.0 --- # X-CLIP (base-sized model) 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). This model was trained using 8 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.  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 [Kinetics-400](https://www.deepmind.com/open-source/kinetics). ### 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 f...
Source context: 5 downloads · 0 likes · Pipeline video-classification · Repo kas1293/xclip-base-patch32
