edgetam-dimos 1.0
EdgeTAM: On-Device Track Anything Model
License: Apache 2.0
Requires Python: >=3.10.0

# EdgeTAM: On-Device Track Anything Model

[Chong Zhou<sup>1,2*</sup>](https://chongzhou96.github.io/),
[Chenchen Zhu<sup>1</sup>](https://sites.google.com/andrew.cmu.edu/zcckernel/home),
[Yunyang Xiong<sup>1</sup>](https://pages.cs.wisc.edu/~yunyang/),
[Saksham Suri<sup>1</sup>](https://www.cs.umd.edu/~sakshams/),
[Fanyi Xiao<sup>1</sup>](https://fanyix.cs.ucdavis.edu/),
[Lemeng Wu<sup>1</sup>](https://sites.google.com/view/lemeng-wu/home),
[Raghuraman Krishnamoorthi<sup>1</sup>](https://scholar.google.com/citations?user=F1mr9C0AAAAJ&hl=en),
[Bo Dai<sup>3,4</sup>](https://daibo.info/),
[Chen Change Loy<sup>2</sup>](https://www.mmlab-ntu.com/person/ccloy/),
[Vikas Chandra<sup>1</sup>](https://v-chandra.github.io/),
[Bilge Soran<sup>1</sup>](https://scholar.google.com/citations?user=9nXD6pwAAAAJ&hl=en)

<sup>1</sup>Meta Reality Labs,
<sup>2</sup>S-Lab, Nanyang Technological University,
<sup>3</sup>University of Hong Kong,
<sup>4</sup>Feeling AI

(*) Work done during the internship at Meta Reality Labs.

[[`Paper`](https://arxiv.org/abs/2501.07256)] [[`Demo`](https://huggingface.co/spaces/facebook/EdgeTAM)] [[`BibTeX`](#citing-edgetam)]


## Overview

**EdgeTAM** is an on-device executable variant of the SAM 2 for promptable segmentation and tracking in videos.
It runs **22× faster** than SAM 2 and achieves **16 FPS** on iPhone 15 Pro Max without quantization.

<p align="center">
  <img src="assets/speed-performance.png?raw=true" width="400"/>
</p>

*In this figure, we show the speed-performance trade-offs of EdgeTAM and other models on iPhone 15 Pro Max (red) and NVIDIA A100 (blue). We report the J&F on the SA-V val dataset as the evaluation metric.*

## Installation

EdgeTAM needs to be installed first before use. The code requires `python>=3.10`, as well as `torch>=2.3.1` and `torchvision>=0.18.1`. Please follow the instructions [here](https://pytorch.org/get-started/locally/) to install both PyTorch and TorchVision dependencies. You can install EdgeTAM on a GPU machine using:

```bash
git clone https://github.com/facebookresearch/EdgeTAM.git && cd EdgeTAM

pip install -e .
```

To use the EdgeTAM predictor and run the example notebooks, `jupyter` and `matplotlib` are required and can be installed by:

```bash
pip install -e ".[notebooks]"
```

For CoreML export to deploy EdgeTAM on iOS/macOS devices, install the CoreML dependencies:

```bash
pip install -e ".[coreml]"
```

Note:
1. It's recommended to create a new Python environment via [Anaconda](https://www.anaconda.com/) for this installation and install PyTorch 2.3.1 (or higher) via `pip` following https://pytorch.org/. If you have a PyTorch version lower than 2.3.1 in your current environment, the installation command above will try to upgrade it to the latest PyTorch version using `pip`.
2. The step above requires compiling a custom CUDA kernel with the `nvcc` compiler. If it isn't already available on your machine, please install the [CUDA toolkits](https://developer.nvidia.com/cuda-toolkit-archive) with a version that matches your PyTorch CUDA version.
3. If you see a message like `Failed to build the SAM 2 CUDA extension` during installation, you can ignore it and still use EdgeTAM (some post-processing functionality may be limited, but it doesn't affect the results in most cases).


## Getting Started

### Downloading the model

Model is available [here](https://github.com/facebookresearch/EdgeTAM/tree/main/checkpoints/edgetam.pt).

### On-device Gradio demo for EdgeTAM
Follow the instructions below to run the on-device demo for EdgeTAM. If you want to quickly try out the demo, you can also go to [Hugging Face Spaces](https://huggingface.co/spaces/facebook/EdgeTAM).

Install the dependencies for the Gradio demo:

```bash
pip install -e ".[gradio]"
```

Run the demo:

```bash
python gradio_app.py
```

The demo will be available at http://127.0.0.1:7860/ by default. You can change the port by setting the `--port` argument.

### Image prediction

EdgeTAM has all the capabilities of [SAM](https://github.com/facebookresearch/segment-anything) on static images, and we provide image prediction APIs that closely resemble SAM for image use cases. The `SAM2ImagePredictor` class has an easy interface for image prompting.

```python
import torch
from sam2.build_sam import build_sam2
from sam2.sam2_image_predictor import SAM2ImagePredictor

checkpoint = "./checkpoints/edgetam.pt"
model_cfg = "configs/edgetam.yaml"
predictor = SAM2ImagePredictor(build_sam2(model_cfg, checkpoint))

with torch.inference_mode(), torch.autocast("cuda", dtype=torch.bfloat16):
    predictor.set_image(<your_image>)
    masks, _, _ = predictor.predict(<input_prompts>)
```

Please refer to the examples in [image_predictor_example.ipynb](./notebooks/image_predictor_example.ipynb) for static image use cases.

EdgeTAM also supports automatic mask generation on images just like SAM. Please see [automatic_mask_generator_example.ipynb](./notebooks/automatic_mask_generator_example.ipynb) for automatic mask generation in images.

### Video prediction

For promptable segmentation and tracking in videos, we provide a video predictor with APIs for example to add prompts and propagate masklets throughout a video. EdgeTAM supports video inference on multiple objects and uses an inference state to keep track of the interactions in each video.

```python
import torch
from sam2.build_sam import build_sam2_video_predictor

checkpoint = "./checkpoints/edgetam.pt"
model_cfg = "configs/edgetam.yaml"
predictor = build_sam2_video_predictor(model_cfg, checkpoint)

with torch.inference_mode(), torch.autocast("cuda", dtype=torch.bfloat16):
    state = predictor.init_state(<your_video>)

    # add new prompts and instantly get the output on the same frame
    frame_idx, object_ids, masks = predictor.add_new_points_or_box(state, <your_prompts>):

    # propagate the prompts to get masklets throughout the video
    for frame_idx, object_ids, masks in predictor.propagate_in_video(state):
        ...
```

Please refer to the examples in [video_predictor_example.ipynb](./notebooks/video_predictor_example.ipynb) for details on how to add click or box prompts, make refinements, and track multiple objects in videos.

### CoreML export for iOS/macOS deployment

EdgeTAM can be exported to CoreML format for deployment on iOS and macOS devices, enabling on-device inference with hardware acceleration.

```bash
# Export EdgeTAM to CoreML format
python ./coreml/export_to_coreml.py \
  --sam2_cfg ./sam2/configs/edgetam.yaml \
  --sam2_checkpoint ./checkpoints/edgetam.pt \
  --output_dir ./coreml_models
```

This creates three optimized CoreML models:
- **Image Encoder**: Processes input images to feature embeddings (~9.6MB)
- **Prompt Encoder**: Handles user prompts (points, boxes, masks) (~2MB) 
- **Mask Decoder**: Generates segmentation masks from features (~8MB)


## Performance
### Promptable Video Segmentation (PVS)
<p align="center">
  <img src="assets/pvs.png?raw=true" width="700"/>
</p>

*Zero-shot PVS accuracy across 9 datasets in offline and online settings.*

### Video Object Segmentation (VOS)
| Method         | MOSE val | DAVIS 2017 val | SA-V val | SA-V test | YTVOS 2019 val | A100  | V100  | iPhone |
|----------------|----------|----------------|----------|-----------|----------------|-------|-------|--------|
| STCN           | 52.5     | 85.4           | 61.0     | 62.5      | 82.7           | 62.8  | 13.2  | -      |
| SwinB-AOT      | 59.4     | 85.4           | 51.1     | 50.3      | 84.5           | -     | -     | -      |
| SwinB-DeAOT    | 59.9     | 86.2           | 61.4     | 61.8      | 86.1           | -     | -     | -      |
| RDE            | 46.8     | 84.2           | 51.8     | 53.9      | 81.9           | 88.8  | 24.4  | -      |
| XMem           | 59.6     | 86.0           | 60.1     | 62.3      | 85.6           | 61.2  | 22.6  | -      |
| SimVOS-B       | -        | 88.0           | 44.2     | 44.1      | 84.2           | -     | 3.3   | -      |
| JointFormer    | -        | 90.1           | -        | -         | 87.4           | -     | 3.0   | -      |
| ISVOS          | -        | 88.2           | -        | -         | 86.3           | -     | 5.8   | -      |
| DEVA           | 66.0     | 87.0           | 55.4     | 56.2      | 85.4           | 65.2  | 25.3  | -      |
| Cutie-base     | 69.9     | 87.9           | 60.7     | 62.7      | 87.0           | 65.0  | 36.4  | -      |
| Cutie-base+    | 71.7     | 88.1           | 61.3     | 62.8      | 87.5           | 57.2  | 17.9  | -      |
| SAM 2-B+       | 75.8     | **90.9**       | 73.6     | 74.1      | 88.4           | 64.8  | -     | 0.7    |
| SAM 2.1-B+     | **76.6** | 90.2           | **76.8** | **77.0**  | **88.6**       | 64.1  | -     | 0.7    |
| **EdgeTAM**    | 70.0     | 87.7           | 72.3     | 71.7      | 86.2           | **150.9** | - | **15.7** |

*We report the G for YTVOS and J&F for other datasets. The FPS on A100 is obtained with torch compile. Nota that, for SAM 2, SAM 2.1, and EdgeTAM, we evaluate all the datasets with the same model.*

### Segment Anything (SA)
| Model    | Data        | SA-23 All        | SA-23 Image      | SA-23 Video      | FPS   |
|----------|-------------|------------------|------------------|------------------|-------|
| SAM      | SA-1B       | 58.1 (81.3)      | 60.8 (82.1)      | 54.5 (80.3)      | -     |
| SAM 2    | SA-1B       | 58.9 (81.7)      | 60.8 (82.1)      | 56.4 (81.2)      | 1.3   |
| SAM 2    | SAM2’s mix  | 61.4 (83.7)      | 63.1 (83.9)      | 59.1 (83.3)      | 1.3   |
| SAM 2.1  | SAM2’s mix  | **61.9 (83.5)**  | **63.3 (83.8)**  | **60.1 (83.2)**  | 1.3   |
| **EdgeTAM** | Our mix  | 55.5 (81.7)      | 56.0 (81.9)      | 54.8 (81.5)      | **40.4** |

*We report 1 (5) click mIoU results. FPS is measured on iPhone 15 Pro Max. Our mix does not contain the internal datasets that SAM 2 uses.*

## License

The EdgeTAM model checkpoints and code are licensed under [Apache 2.0](./LICENSE).


## Citing EdgeTAM

If you use EdgeTAM in your research, please use the following BibTeX entry.

```bibtex
@article{zhou2025edgetam,
  title={EdgeTAM: On-Device Track Anything Model},
  author={Zhou, Chong and Zhu, Chenchen and Xiong, Yunyang and Suri, Saksham and Xiao, Fanyi and Wu, Lemeng and Krishnamoorthi, Raghuraman and Dai, Bo and Loy, Chen Change and Chandra, Vikas and Soran, Bilge},
  journal={arXiv preprint arXiv:2501.07256},
  year={2025}
}
```