Microsam Vit B Lm Onnx
Promptable micro-sam vitblm exported to ONNX for client-side inference with onnxruntime-web + WebGPU, used by the JAX Image Tools viewer (jit-ui#90) SAM segment tool.
Model source
- Kind
- Other
- File size
- 15.7–172 MB
- Version
- vDecoder
- Updated
- Nov 1, 2026
- Source
- Hugging Face
Source description
micro-sam ViT-B (light microscopy) — ONNX (browser/WebGPU)
Promptable micro-sam
vit_b_lm exported to ONNX for client-side inference with onnxruntime-web +
WebGPU, used by the JAX Image Tools viewer (jit-ui#90) SAM segment tool.
Two graphs (SAM v1 I/O):
| file | in → out | run |
|---|---|---|
encoder.fp16.onnx (fp16, ~172 MB) | image (1,3,1024,1024) fp32 → embedding (1,256,64,64) fp32 | once per image |
decoder.onnx (fp32, ~16 MB) | embedding + box/point prompts → mask (resized to orig size) | per prompt |
- Image preprocessing (resize long side → 1024, SAM normalize, pad) is done on the client; the encoder is pure tensor I/O. fp16 keeps fp32 I/O.
- fp16 produced with
onnxruntime.transformers.float16(cosine ≈ 1.0000 vs fp32). - Box prompt = two points labelled
2/3; positive/negative points1/0.
Export tooling: the sam-js project (export/export_sam_onnx.py,
export/quantize_fp16.py). Base model: micro-sam vit_b_lm; underlying
architecture Segment Anything (Apache-2.0). See micro-sam for model license.
Sources
1 source
Sources
1 sourceVerified Nov 1
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Model artifacts
2 artifacts
Model artifacts
2 artifactsencoder.fp16.onnx
onnx · FP16 (unverified) · 172 MB · SHA-256 fdec6fb1cc43…2bea · Hugging Face
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Source excerpts
2 excerpts--- license: cc-by-4.0 tags: - segment-anything - micro-sam - onnx - webgpu - microscopy - image-segmentation library_name: onnxruntime --- # micro-sam ViT-B (light microscopy) — ONNX (browser/WebGPU) Promptable [micro-sam](https://github.com/computational-cell-analytics/micro-sam) `vit_b_lm` exported to ONNX for client-side inference with **onnxruntime-web + WebGPU**, used by the JAX Image Tools viewer (jit-ui#90) SAM segment tool. Two graphs (SAM v1 I/O): | file | in → out | run | |---|---|---| | `encoder.fp16.onnx` (fp16, ~172 MB) | image `(1,3,1024,1024)` fp32 → embedding `(1,256,64,64)` fp32 | once per image | | `decoder.onnx` (fp32, ~16 MB) | embedding + box/point prompts → mask (resized to orig size) | per prompt | - Image preprocessing (resize long side → 1024, SAM normalize, pad) is done on the client; the encoder is pure tensor I/O. fp16 keeps fp32 I/O. - fp16 produced with `onnxruntime.transformers.float16` (cosine ≈ 1.0000 vs fp32). - Box prompt = two points labelled `2`/`3`; positive/negative points `1`/`0`. Export tooling: the `sam-js` project (`export/export_sam_onnx.py`, `export/quantize_fp16.py`). Base model: micro-sam `vit_b_lm`; underlying architecture Segment Anything (Apache-2.0). See micro-sam for model license.
Source context: 0 downloads · 0 likes · Pipeline image-segmentation · Library onnxruntime · Repo Ballon999/microsam-vit-b-lm-onnx
