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patho-sam-vit-b-onnx
jax-image-tools/patho-sam-vit-b-onnx
patho-sam-vit-b-onnx is a image segmentation model from jax-image-tools. Use it for the image segmentation task on the model card, and read the license before you ship it in a product. It is set up for onnxruntime. The card lists the license as cc-by-4.0.
Promptable patho-sam (vitbhistopathology, distributed via micro-sam's model registry) exported to ONNX for client-side inference with onnxruntime-web + WebGPU, used by the JAX Image Tools viewer (jit-ui90) SAM segment…
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Updated Aug 26, 2026
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At a glance
- Task
- Image Segmentation
- Library
- onnxruntime
- License
- cc-by-4.0
- Access
- Public
- Created
- Aug 26, 2026
- Updated
- Aug 26, 2026
- SHA
- 5e187861
Model card
From the Hugging Face model README
patho-sam ViT-B (histopathology) — ONNX (browser/WebGPU)
Promptable patho-sam
(vit_b_histopathology, distributed via micro-sam's model registry) exported to
ONNX for client-side inference with onnxruntime-web + WebGPU, used by the JAX
Image Tools viewer (jit-ui#90) SAM segment tool. Finetuned for H&E / stained
tissue — the histopathology sibling of microsam-vit-b-lm-onnx.
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. - Box prompt = two points labelled
2/3; positive/negative points1/0.
Export tooling: the sam-js project (export/export_sam_onnx.py --microsam vit_b_histopathology).
Base model: patho-sam vit_b_histopathology (ViT-Base; Segment Anything, Apache-2.0).
See patho-sam / micro-sam for model license.
- Task
- Image Segmentation
- Library
- onnxruntime
- License
- cc-by-4.0
- Created
- Aug 26, 2026
- Updated
- Aug 26, 2026