Aller au contenu
ModelPrésentéExterne
Browse files

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.

TypeOtherVersionDecoderCClasse

Source du modèle

Type
Other
Taille du fichier
15,7–172 MB
Version
vDecoder
Mis à jour
1 nov. 2026
Source
Hugging Face

Description de la source

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):

filein → outrun
encoder.fp16.onnx (fp16, ~172 MB)image (1,3,1024,1024) fp32 → embedding (1,256,64,64) fp32once 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.

Sources

1 source
Hugging FacePrincipalVersion b983c4dc68f7c3b646df0b063aee7cb89f794a81
huggingface.co/Ballon999/microsam-vit-b-lm-onnx/tree/b983c4dc68f7c3b646df0b063aee7cb89f794a81

Vérifié 1 nov.

Download files

Artefacts du modèle

2 artefacts

decoder.onnx

onnx · 15,7 MB · SHA-256 2ba9433cf9b6…4926 · Hugging Face

Download file

encoder.fp16.onnx

onnx · FP16 (unverified) · 172 MB · SHA-256 fdec6fb1cc43…2bea · Hugging Face

Download file

Extraits de sources

2 extraits

--- 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