---
license: other
license_name: sam3
license_link: https://huggingface.co/facebook/sam3
pipeline_tag: mask-generation
library_name: onnxruntime
base_model: facebook/sam3
tags:
  - onnxruntime
  - onnx
  - sam3
  - segment-anything
  - segment-anything-3
  - mask-generation
  - image-segmentation
  - open-vocabulary
  - text-to-segmentation
  - edge-ai
  - quantized
  - uint8
---

> This is a duplicate of [rectlabel/segment-anything-onnx-models](https://huggingface.co/rectlabel/segment-anything-onnx-models) (sam3_v2.zip) for [GAME-Map2Mask-exploration.html](https://katehuuh.github.io/demos/[GAME]-Map2Mask-exploration.html), unzipped so each file is directly downloadable. Original export by [RectLabel](https://rectlabel.com).

# SAM3 v2 ONNX (quantized uint8)

Text-prompted image segmentation. Give it an image and a word like "road" - it gives you a binary mask. No box prompts or point clicks needed.

Quantized with `onnxruntime.quantization` (QUInt8, per-tensor, dynamic). Exported using [sam3-cpp-macos/export_v2.py](https://github.com/ryouchinsa/sam3-cpp-macos) at 1008x1008 resolution.

## Files

| File | Size | What it does |
|---|---|---|
| `vision-encoder.onnx` | 510 MB | Image in, FPN features out |
| `text-encoder.onnx` | 339 MB | Token IDs in, text features out |
| `decoder.onnx` | 35 MB | Features + text + box in, masks out |
| `tokenizer.json` | 3.5 MB | CLIP tokenizer vocab |

Total: ~887 MB

## How to use (Python, onnxruntime)

```python
import onnxruntime as ort, numpy as np, json
from PIL import Image

# Load sessions
vis = ort.InferenceSession("vision-encoder.onnx")
txt = ort.InferenceSession("text-encoder.onnx")
dec = ort.InferenceSession("decoder.onnx")

# Preprocess image (1008x1008, ImageNet normalize)
img = np.array(Image.open("photo.jpg").convert("RGB").resize((1008, 1008))).astype(np.float32)
img = ((img - [123.675, 116.28, 103.53]) / [58.395, 57.12, 57.375]).transpose(2, 0, 1)[None]

# Tokenize (CLIP-style, "road" = [49406, 1759, 49407])
ids = np.zeros((1, 32), dtype=np.int64)
mask = np.zeros((1, 32), dtype=np.int64)
ids[0, :3] = [49406, 1759, 49407]  # BOS, road, EOS
mask[0, :3] = 1

# Run pipeline
vis_out = vis.run(None, {"images": img.astype(np.float32)})
vis_map = dict(zip([o.name for o in vis.get_outputs()], vis_out))

txt_out = txt.run(None, {"input_ids": ids, "attention_mask": mask})
txt_map = dict(zip([o.name for o in txt.get_outputs()], txt_out))

dec_out = dec.run(None, {
    **{k: vis_map[k] for k in ["fpn_feat_0", "fpn_feat_1", "fpn_feat_2", "fpn_pos_2"]},
    "text_features": txt_map["text_features"],
    "text_mask": txt_map["text_mask"],
    "input_boxes": np.array([[[0, 0, 1, 1]]], dtype=np.float32),
    "input_boxes_labels": np.array([[1]], dtype=np.int64),
})

# Best mask
logits = dec_out[2][0]
binary_mask = dec_out[0][0, logits.argmax()] > 0
```

## How to use (browser, onnxruntime-web)

Works with onnxruntime-web WASM or WebGPU. Load each .onnx file via `ort.InferenceSession.create(url)`. Same pipeline as above but with `ort.Tensor` instead of numpy arrays. Token IDs need `BigInt64Array` for int64 support.

## Model I/O shapes

**vision-encoder.onnx**
- Input: `images` [batch, 3, 1008, 1008] float32
- Output: `fpn_feat_0` [batch, 256, 288, 288], `fpn_feat_1` [batch, 256, 144, 144], `fpn_feat_2` [batch, 256, 72, 72], `fpn_pos_2` [batch, 256, 72, 72]

**text-encoder.onnx**
- Input: `input_ids` [batch, 32] int64, `attention_mask` [batch, 32] int64
- Output: `text_features` [batch, 32, 256] float32, `text_mask` [batch, 32] bool

**decoder.onnx**
- Input: all vision outputs + text outputs + `input_boxes` [batch, N, 4] float32 + `input_boxes_labels` [batch, N] int64
- Output: `pred_masks` [batch, M, H, W] float32, `pred_boxes` [batch, 200, 4], `pred_logits` [batch, M], `presence_logits` [batch, M]

Use `input_boxes = [[[0,0,1,1]]]` with `input_boxes_labels = [[1]]` for full-image text-prompted segmentation.

## Source

- Base model: [facebook/sam3](https://huggingface.co/facebook/sam3)
- ONNX export script: [ryouchinsa/sam3-cpp-macos](https://github.com/ryouchinsa/sam3-cpp-macos)
- Original ZIP: [rectlabel/segment-anything-onnx-models](https://huggingface.co/rectlabel/segment-anything-onnx-models) (sam3_v2.zip)