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SAM2.1 Highlight Mask Endpoint
Custom Hugging Face Inference Endpoint handler for Agatha collection highlights.
The endpoint wraps facebook/sam2.1-hiera-base-plus with Meta's official
sam2.sam2_image_predictor.SAM2ImagePredictor API and exposes the exact contract
the backend expects:
{
"inputs": {
"image_base64": "...",
"mime_type": "image/png",
"boxes": [
{ "id": "sofa", "box": { "x1": 120, "y1": 300, "x2": 640, "y2": 760 } }
]
}
}
Response:
{
"masks": [
{
"id": "sofa",
"score": 0.93,
"mask_png_base64": "...",
"box": { "x1": 120, "y1": 300, "x2": 640, "y2": 760 },
"mime_type": "image/png"
}
]
}
requirements.txt intentionally avoids transformers. The model card supports
a Transformers route, but the stable first-party path for this custom endpoint is
SAM2ImagePredictor.from_pretrained("facebook/sam2.1-hiera-base-plus"). The
handler calls set_image() once per request and segments all provided boxes in
one predictor call.
Upload handler.py and requirements.txt to a Hugging Face model repo, then
deploy that repo as an Inference Endpoint with task Custom.
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