PicoSAM3

Install

PicoSAM3 needs the sam extra: LibreYOLO's own weight download still goes through transformers' Hugging Face tooling, even though inference runs on a native, non-transformers CNN.

pip install "libreyolo[sam]"

Predict

LibreSAM(...) (or the family-specific LibrePicoSAM3(...)) 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 prompt. There is no libreyolo predict CLI command for this family; use the Python API.

from libreyolo import LibreSAM, SAMPLE_IMAGE # PicoSAM3 has a single size, "pico", so no other alias is needed.model = LibreSAM("picosam3") # bboxes= is the only supported prompt: [x1, y1, x2, y2] or a list of# boxes, one mask per box. Each box is expanded 10%, made square,# clipped to the image and resized to 96x96 before the CNN runs.result = model.predict(SAMPLE_IMAGE, bboxes=[300, 200, 900, 700])print(result.masks.xy)      # polygon per maskprint(result.boxes.xyxy)    # tight box derived from the mask

from libreyolo import LibrePicoSAM3, SAMPLE_IMAGE model = LibrePicoSAM3() # set_image() caches the source image; PicoSAM3 runs one full CNN# forward per box, so this saves the image load/decode, not an# encoder pass the way it does for the other SAM families.model.set_image(SAMPLE_IMAGE)a = model.predict(bboxes=[300, 200, 900, 700])b = model.predict(bboxes=[100, 100, 400, 400])model.reset_image()

PicoSAM3 accepts only bboxes=; passing points=, labels=, masks=, text=, multimask=True or omitting the box to segment everything all raise a clear ValueError, since none of those modes exist in the upstream model. conf filters by predicted mask quality (IoU), not a detection confidence, and must be between 0.0 and 1.0. Every mask carries class id 0, named "object". train(), val() and track() raise NotImplementedError; use LibreSAM2 or LibreSAM3 for point, text, mask or segment-everything prompts. See prediction for source types.

Variants

One size, pico, at a fixed 96 px ROI input: PicoSAM3 runs one full CNN forward per box rather than encoding the whole image once.

Export

PicoSAM3 is the only family in the SAM tier that exports: it ships its raw 96x96 ROI CNN to ONNX, roi_image -> mask_logits, with no NMS or mask post-processing baked in. The other SAM families raise NotImplementedError on export(), since their encoder/decoder split has no defined runtime export contract yet. An exported PicoSAM3 graph does not load back through LibreYOLO(); run it directly with a runtime such as onnxruntime, applying the same 10%-padded square-ROI preprocessing shown above.

from libreyolo import LibrePicoSAM3 model = LibrePicoSAM3()model.export(format="onnx", output_path="LibrePicoSAM3pico.onnx") # opset (default 13) and dynamic (default True, batch axis only) are# the only export arguments this family accepts.

import numpy as npimport onnxruntime as ort # PicoSAM3 exports its raw 96x96 ROI CNN: roi_image -> mask_logits.# There is no LibreYOLO-side pre/postprocessing to reuse here, since# export() is not routed back through LibreYOLO() the way a detector# checkpoint is.session = ort.InferenceSession("LibrePicoSAM3pico.onnx")name = session.get_inputs()[0].nameoutputs = session.run(None, {name: np.zeros((1, 3, 96, 96), dtype=np.float32)}) for meta, array in zip(session.get_outputs(), outputs):    print(meta.name, array.shape)

Checkpoints

Every published weight file for this family.

Licensing

Citation

@article{picosam3_2026,
      title={PicoSAM3: Real-Time In-Sensor Region-of-Interest Segmentation}, 
      author={Pietro Bonazzi and Nicola Farronato and Stefan Zihlmann and Haotong Qin and Michele Magno},
      journal={IEEE Sensors Journal},
      year={2026}
}