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# Efficient Track Anything
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[[`📕Project`](https://yformer.github.io/efficient-track-anything/)][[`🤗Gradio Demo`](https://bea2c478296e25b3ce.gradio.live)][[`📕Paper`](https://arxiv.org/pdf/2411.18933)]
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The **Efficient Track Anything Model(EfficientTAM)** takes a vanilla lightweight ViT image encoder. An efficient memory cross-attention is proposed to further improve the efficiency. Our EfficientTAMs are trained on SA-1B (image) and SA-V (video) datasets. EfficientTAM achieves comparable performance with SAM 2 with improved efficiency. Our EfficientTAM can run **>10 frames per second** with reasonable video segmentation performance on **iPhone 15**. Try our demo with a family of EfficientTAMs at [[`🤗Gradio Demo`](https://bea2c478296e25b3ce.gradio.live)].
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# Efficient Track Anything
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[[`🤗Checkpoints`]](https://huggingface.co/yunyangx/efficient-track-anything/tree/main)[[`📕Project`](https://yformer.github.io/efficient-track-anything/)][[`🤗Gradio Demo`](https://bea2c478296e25b3ce.gradio.live)][[`📕Paper`](https://arxiv.org/pdf/2411.18933)]
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The **Efficient Track Anything Model(EfficientTAM)** takes a vanilla lightweight ViT image encoder. An efficient memory cross-attention is proposed to further improve the efficiency. Our EfficientTAMs are trained on SA-1B (image) and SA-V (video) datasets. EfficientTAM achieves comparable performance with SAM 2 with improved efficiency. Our EfficientTAM can run **>10 frames per second** with reasonable video segmentation performance on **iPhone 15**. Try our demo with a family of EfficientTAMs at [[`🤗Gradio Demo`](https://bea2c478296e25b3ce.gradio.live)].
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