---
license: other
license_name: sam-license
license_link: https://github.com/facebookresearch/sam3/blob/660a5e9e1b8b4c02c0ad97229b88a09a6e4ff5b7/LICENSE
pipeline_tag: image-segmentation
library_name: onnx
base_model: facebook/sam3
tags:
  - onnxruntime
  - onnx
  - segment-anything
  - image-segmentation
  - open-vocabulary
  - text-to-segmentation
  - anylearning
authors:
  - Viet-Anh Nguyen
---

# 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](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.