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onnx-and-browser

onnx-and-browser

Export Segment Anything's prompt encoder and mask decoder to ONNX, quantize it, prepare matching image embeddings, and wire the model into ONNXRuntime or browser workflows.

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Documentation de la Skill

GitHub

name: onnx-and-browser description: "Export Segment Anything's prompt encoder and mask decoder to ONNX, quantize it, prepare matching image embeddings, and wire the model into ONNXRuntime or browser workflows." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

ONNX and Browser Workflows

Use this sub-skill when the user asks to export SAM to ONNX, quantize a SAM ONNX model, run the mask decoder in ONNXRuntime Web, prepare a .npy image embedding for a browser demo, or diagnose ONNX/browser input shape issues.

Route First

  • For prompted Python inference with SamPredictor.predict, use ../prompted-segmentation/ unless ONNX export or embeddings are part of the request.
  • For full-image automatic mask generation with SamAutomaticMaskGenerator, use ../automatic-mask-generation/.
  • For browser workflows, export both a mask decoder ONNX file and image embeddings produced by the same checkpoint/model type/image preprocessing path.

Fast Paths

  • Export ONNX with python sub-skills/onnx-and-browser/scripts/export_onnx_model.py --checkpoint sam_vit_h_4b8939.pth --model-type vit_h --output sam_onnx.onnx.
  • Quantize for browser size/runtime with --quantize-out sam_onnx_quantized.onnx; this requires onnxruntime because quantization uses ONNXRuntime tooling.
  • Prepare a matching embedding by adapting scripts/prepare_embedding_template.py; save the .npy beside the browser image asset.
  • In browser feeds, use input names image_embeddings, point_coords, point_labels, mask_input, has_mask_input, and orig_im_size.

References

  • references/onnx-export.md explains export flags, tensor contracts, dynamic axes, quantization, and embedding compatibility.
  • references/browser-demo.md explains browser asset layout, ONNXRuntime Web feeds, scaling, and SharedArrayBuffer headers.
  • references/api-reference.md summarizes SamOnnxModel, export-script behavior, input/output names, and expected shapes.
  • references/troubleshooting.md maps common export/runtime/browser errors to concrete fixes.

Bundled Scripts

  • scripts/export_onnx_model.py is an adapted export helper with safe --help behavior and clearer optional-dependency errors.
  • scripts/prepare_embedding_template.py is a template for exporting a .npy image embedding from SamPredictor for ONNX/browser use.
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