# EdgeTAM
EdgeTAM is an on-device variant of SAM 2, built for mobile inference speed while keeping the same point-and-box promptable workflow. LibreYOLO supports its image segmentation path through a dedicated LibreSAM factory, separate from the LibreYOLO() detector factory.
Tasks: Instance segmentation. Install: pip install "libreyolo[sam]".
Verified against LibreYOLO v1.5.0.

## Install

EdgeTAM needs the `sam` extra, which pulls in `transformers` and `timm`.

```bash
pip install "libreyolo[sam]"
```

## Predict

`LibreSAM(...)` (or the family-specific `LibreEdgeTAM(...)`) is a separate
entry point from `LibreYOLO(...)`: it returns a promptable segmenter rather
than a detector, because a forward pass here is meaningless without a
spatial prompt. There is no `libreyolo predict` CLI command for this family;
use the Python API. Only image segmentation is supported; EdgeTAM's video
tracking is out of scope here.

**Point and box prompts**

```python
from libreyolo import LibreSAM, SAMPLE_IMAGE

# EdgeTAM has a single size, "edge". Aliases: "edgetam", "edge-tam",
# "edgetam-edge".
model = LibreSAM("edgetam")

# A point prompt: [x, y] in pixel coordinates, label 1 = foreground.
result = model.predict(SAMPLE_IMAGE, points=[640, 420], labels=[1])
print(result.masks.xy)      # polygon per mask
print(result.boxes.xyxy)    # tight box derived from the mask

# A box prompt instead of a point.
result = model.predict(SAMPLE_IMAGE, bboxes=[300, 200, 900, 700])

# No prompt at all segments the whole image (a simplified automatic
# mask generator, not the exhaustive reference one).
result = model.predict(SAMPLE_IMAGE)
```

**Encode once, prompt many**

```python
from libreyolo import LibreEdgeTAM, SAMPLE_IMAGE

model = LibreEdgeTAM()

# The image encoder is the expensive part. set_image() runs it once;
# every predict() call after that reuses the cached embedding.
model.set_image(SAMPLE_IMAGE)
a = model.predict(points=[640, 420], labels=[1])
b = model.predict(bboxes=[300, 200, 900, 700])
model.reset_image()
```

A point prompt accepts `[x, y]` for one object, `[[x, y], ...]` for several, or
numpy arrays; `labels` marks each point `1` (foreground) or `0` (background)
and defaults to all foreground. A box prompt takes `[x1, y1, x2, y2]` or a list
of boxes, one mask per box. Omitting both prompts segments the whole image by
prompting a dense grid and keeping the confident, non-overlapping masks; this
"segment everything" mode is simplified against the reference automatic mask
generator and can under-segment crowded scenes, so a real point or box prompt
is the precise path. `conf` filters by predicted mask quality (IoU), not a
detection confidence: pass `0.0` to keep every candidate. `multimask=True`
returns all three of SAM's whole-versus-part ambiguity masks per prompt
instead of the single best one. `device=` moves the model and, if a
`set_image()` session is active, its cached embedding. Every mask carries
class id `0`, named `"object"`, since a promptable mask has no fixed class
set. `train()`, `val()`, `export()` and `track()` all raise
`NotImplementedError` for this family: image inference is what LibreYOLO
supports here. See [prediction](/docs/predict) for source types.

## Variants

One size, edge, at a fixed input resolution, so choosing this family over the
rest of the SAM tier is a hardware decision rather than a sizing one: EdgeTAM
exists specifically for constrained, on-device inference.

## Checkpoints

Every published weight file for this family.



## Licensing

Check the license on the Hugging Face repository of the specific weights you download. That repository is authoritative and licenses are not always uniform across a family. This is a description of the licenses involved, not legal advice.

- Original work: EdgeTAM, Meta Reality Labs
- Upstream license: Apache-2.0
- Upstream source: https://github.com/facebookresearch/EdgeTAM
- LibreYOLO code: MIT
- Weights: Apache-2.0, republished at https://huggingface.co/LibreYOLO
- Interpretation: Apache-2.0 is a permissive license, so this code and these weights can be used in commercial and closed-source products. It asks you to keep the license text and attribution notices with any copy you redistribute, and it grants a patent license. LibreYOLO does not vendor EdgeTAM's model source: it calls the Apache-2.0 Transformers adapter and reproduces the pinned upstream image and prompt-coordinate transforms from facebookresearch/EdgeTAM commit 7711e012a30a2402c4eaab637bdb00a521302c91. The republished LibreYOLO/LibreEdgeTAM snapshot is converted from facebook/EdgeTAM revision 14d7ecc48c656b94e5184519f698cd5386c5a2bf, checked tensor-by-tensor against a Transformers-format reference before publication, and tagged Apache-2.0 on the LibreYOLO Hugging Face org. LibreYOLO ships image inference only; EdgeTAM's video tracking is out of scope for this family.

## Citation

```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}
}
```

Copied from https://github.com/facebookresearch/EdgeTAM#citing-edgetam


## EdgeTAM architecture

- EdgeTAM edge: [SVG](/diagrams/models/edgetam/edge-box-segment.svg), [interactive diagram](/diagrams/models/edgetam/edge-box-segment.html)