# MobileSAM
MobileSAM replaces SAM's ViT-H image encoder with a distilled TinyViT encoder, so the same promptable point-and-box workflow runs on lighter hardware. LibreYOLO carries a native port of it 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

MobileSAM needs the `sam` extra: LibreYOLO's own weight download still goes
through `transformers`' Hugging Face snapshot tooling, even though inference
runs on a native, non-`transformers` decoder.

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

## Predict

`LibreSAM(...)` (or the family-specific `LibreMobileSAM(...)`) 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.

**Point and box prompts**

```python
from libreyolo import LibreSAM, SAMPLE_IMAGE

# MobileSAM has a single size, "tiny", so no other alias is needed.
model = LibreSAM("mobilesam")

# 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 LibreMobileSAM, SAMPLE_IMAGE

model = LibreMobileSAM()

# 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: MobileSAM is predict-only in
LibreYOLO. See [prediction](/docs/predict) for source types.

## Variants

One size, tiny, at a fixed 1024 px input: MobileSAM ships a single TinyViT
encoder rather than the base/large/huge ladder SAM-1 offers.

## Checkpoints

Every published weight file for this family.

| File | Input (px) | Task | Weights license |
| --- | --- | --- | --- |
| `LibreMobileSAM.pt` |  | Instance segmentation | apache-2.0 |

## 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: MobileSAM, Kyung Hee University
- Upstream license: Apache-2.0
- Upstream source: https://github.com/ChaoningZhang/MobileSAM
- LibreYOLO code: Apache-2.0
- 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 carries a native port of the TinyViT image encoder, prompt encoder, two-way transformer and mask decoder rather than vendoring upstream files unmodified, checked for bit-exact parity against the original Apache-2.0 implementation, with a NOTICE recording that provenance. The converted checkpoint is hosted as LibreMobileSAM.pt on the LibreYOLO Hugging Face org, tagged Apache-2.0 there as well.

## Citation

```bibtex
@article{mobile_sam,
  title={Faster Segment Anything: Towards Lightweight SAM for Mobile Applications},
  author={Zhang, Chaoning and Han, Dongshen and Qiao, Yu and Kim, Jung Uk and Bae, Sung-Ho and Lee, Seungkyu and Hong, Choong Seon},
  journal={arXiv preprint arXiv:2306.14289},
  year={2023}
}
```

Copied from https://github.com/ChaoningZhang/MobileSAM#bibtex-of-our-mobilesam


## MobileSAM architecture

- MobileSAM tiny: [SVG](/diagrams/models/mobilesam/tiny-box-segment.svg), [interactive diagram](/diagrams/models/mobilesam/tiny-box-segment.html)