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segment-anything-3-onnx-models
vietanhdev/segment-anything-3-onnx-models
segment-anything-3-onnx-models is a image segmentation model from vietanhdev. 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 onnx. The card lists the license as other.
This repository contains an ONNX conversion of Meta's SAM3 ViT-H model for text, point, box, and combined-prompt segmentation in AnyLearning inference and its authenticated server.
Access
Public
Updated Aug 30, 2026
Repo size
10.2 GB
Likes
25
Public
Repo makeup
Click a slice to open those files.
.zip3.4 GB · 100%
At a glance
- Task
- Image Segmentation
- Library
- onnx
- License
- other
- Access
- Public
- Created
- Feb 21, 2026
- Updated
- Aug 30, 2026
- SHA
- 77dfc320
Base models
Model card
From the Hugging Face model README
SAM3 ViT-H ONNX graph triplet for AnyLearning
This repository contains an ONNX conversion of Meta's SAM3 ViT-H model for text, point, box, and combined-prompt segmentation in AnyLearning inference and its authenticated server.
License
The model weights and converted ONNX artifacts remain subject to Meta's SAM License, not Apache-2.0. Read LICENSE before downloading, using, or redistributing the model. The exporter/runtime wrapper used for the conversion is MIT-licensed; that code license does not relicense the weights.
Immutable provenance
- Upstream model/code revision:
facebookresearch/sam3@660a5e9e1b8b4c02c0ad97229b88a09a6e4ff5b7 - Exporter revision:
vietanhdev/samexporter@35133ce8670e0d190ac10cc08efba9b9a443fb51 - Archive:
sam3_vit_h.zip - Archive SHA-256:
1617436f9db7eb9dd69029c2c465d54b316c36c21143f0fde0d1be513f15b7d5
MANIFEST.json records every archive member's exact byte size and SHA-256.
AnyLearning verifies the three graph files and every referenced external tensor
file independently before creating ONNX Runtime sessions.
Graph contract
| Role | Input | Outputs |
|---|---|---|
| Image encoder | RGB uint8 CHW [3,1008,1008] | six FPN/position feature maps |
| Language encoder | CLIP tokens int64 [1,32] | attention mask, memory, embeddings |
| Decoder | image/language features and one fixed geometric slot | boxes, scores, native-size masks |
The decoder graph in this bundle emits processed boxes, scores, and masks. Its
historical .data companion is present in the original archive but is not
referenced by the decoder graph; AnyLearning does not load it.
Safe download and extraction
Treat model archives as untrusted data. Verify the archive digest, require the
exact members and sizes from MANIFEST.json, reject links and unexpected paths,
and extract each approved member to an explicit destination. Avoid generic
archive extraction calls.
The graph triplet is several gigabytes. Keep downloads resumable, enforce disk and memory quotas, and use AnyLearning's digest-addressed external-data loader instead of reading all tensor files into Python memory.
Validation
The pinned bundle is tested with real images through both in-process inference and the password-authenticated HTTP server. Retained reports cover text, text-plus-point, and box prompts, deterministic repeated results, load/unload cycles, stage timings, peak RSS, editable polygons, and visual inspection.
Citation
Please cite Meta's canonical SAM3 release for the underlying model and record this repository revision when using this ONNX conversion.
- Task
- Image Segmentation
- Library
- onnx
- License
- other
- Created
- Feb 21, 2026
- Updated
- Aug 30, 2026