EdgeTAM

Install

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

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.

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 maskprint(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)

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 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

Citation

@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}
}