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README.md

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+---
+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. 
+
+![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 [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 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 80.4% and a top-5 accuracy of 95.0%.

config.json

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+}

preprocessor_config.json

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+  "do_normalize": true,
+  "do_resize": true,
+  "feature_extractor_type": "VideoMAEFeatureExtractor",
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+  "processor_class": "XCLIPProcessor",
+  "resample": 2,
+  "size": 224
+}

pytorch_model.bin

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special_tokens_map.json

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tokenizer_config.json

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+}