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---
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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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datasets:
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- kinetics-400
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- kinetics-600
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- something-something-v1
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- something-something-v2
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---
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# UniFormer (video model)
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UniFormer models are trained on [Kinetics](https://deepmind.com/research/open-source/kinetics) and [Something-Something](https://20bn.com/datasets/something-something) at resolution 224x224.
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It was introduced in the paper [UniFormer: Unified Transformer for Efficient Spatial-Temporal Representation Learning](https://arxiv.org/abs/2201.04676) by Li et al,
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and first released in [this repository](https://github.com/Sense-X/UniFormer).
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## Model description
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The UniFormer is a type of Vision Transformer, which can seamlessly integrate merits of convolution and self-attention in a concise transformer format.
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It adopt local MHRA in shallow layers to largely reduce computation burden and global MHRA in deep layers to learn global token relation.
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Without any extra training data,
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UniFormer achieves **86.3** top-1 accuracy on ImageNet-1K classification.
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With only ImageNet-1K pre-training, it can simply achieve state-of-the-art performance in a broad range of downstream tasks.
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UniFormer obtains **82.9/84.8** top-1 accuracy on Kinetics-400/600,
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and **60.9/71.2** top-1 accuracy on Something-Something V1/V2 video classification tasks.
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It also achieves **53.8** box AP and **46.4** mask AP on COCO object detection task,
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**50.8** mIoU on ADE20K semantic segmentation task,
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and **77.4** AP on COCO pose estimation task.
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[Source](https://paperswithcode.com/paper/uniformer-unified-transformer-for-efficient)
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## Intended uses & limitations
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You can use the raw model for video classification.
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We now only upload the powerful models with **single clip**.
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More models can be found in [the model hub](https://github.com/Sense-X/UniFormer/tree/main/video_classification).
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### Kinetics
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| Model | #Frame | Sampling Stride | FLOPs | K400 Top-1 | K600 Top-1 |
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| ----------- | ------ | --------------- | ----- | ---------- | ---------- |
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| UniFormer-S | 16x1x1 | 8 | 41.8G | 78.4 | 80.8 |
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| UniFormer-B | 16x1x1 | 8 | 96.7G | 79.3 | 81.7 |
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| UniFormer-B | 32x1x1 | 4 | 259G | 80.9 | 82.4 |
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### Something-Something
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| Model | #Frame | FLOPs | SSV1 Top-1 | SSV2 Top-1 |
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| ----------- | ------ | ----- | ---------- | ---------- |
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| UniFormer-S | 16x1x1 | 41.8G | 54.4 | 65.0 |
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| UniFormer-B | 32x1x1 | 259G | 58.0 | 67.5 |
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### How to use
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You can followed our [demo](https://huggingface.co/spaces/Sense-X/uniformer_video_demo/tree/main) to use our models.
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```python
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from uniformer import uniformer_small
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from kinetics_class_index import kinetics_classnames
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model = uniformer_small()
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# load state
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model_path = hf_hub_download(repo_id="Sense-X/uniformer_video", filename="uniformer_small_k400_16x8.pth")
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state_dict = torch.load(model_path, map_location='cpu')
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model.load_state_dict(state_dict)
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# set to eval mode
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model = model.to(device)
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model = model.eval()
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# please refer to the following url to process video of Kinetics:
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# https://huggingface.co/spaces/Sense-X/uniformer_video_demo/blob/main/app.py
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vid = load_video(video)
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# model predicts one of the 400 Kintics classes
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prediction = model(vid)
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predicted_class_idx = prediction.flatten().argmax(-1).item()
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print("Predicted class:", kinetics_classnames[str(predicted_class_idx)])
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```
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### BibTeX entry and citation info
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```bibtex
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@misc{li2022uniformer,
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title={UniFormer: Unified Transformer for Efficient Spatiotemporal Representation Learning},
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author={Kunchang Li and Yali Wang and Peng Gao and Guanglu Song and Yu Liu and Hongsheng Li and Yu Qiao},
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year={2022},
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eprint={2201.04676},
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archivePrefix={arXiv},
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primaryClass={cs.CV}
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}
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``` |
