license: apache-2.0
tags:
- image-segmentation
- segment-anything
- segment-anything-3
- open-vocabulary
- text-to-segmentation
- onnx
- onnxruntime
library_name: onnxruntime
base_model:
- facebook/sam3
Segment Anything 3 (SAM 3) — ONNX Models
ONNX-exported version of Meta's Segment Anything Model 3 (SAM 3), an open-vocabulary segmentation model that accepts text prompts in addition to points and rectangles.
SAM 3 uses a CLIP-based language encoder to let you describe objects in natural language (e.g., "truck", "person with hat") and segment them without task-specific training.
These models are used by AnyLabeling for AI-assisted image annotation, and exported by samexporter.
Available Models
| File | Contents | Description |
|---|---|---|
sam3_vit_h.zip |
3 ONNX files | SAM 3 ViT-H (all components) |
The zip contains three ONNX components that work together:
| ONNX File | Role | Runs |
|---|---|---|
sam3_image_encoder.onnx |
Extracts visual features from the input image | Once per image |
sam3_language_encoder.onnx |
Encodes text prompt tokens into feature vectors | Once per text query |
sam3_decoder.onnx |
Produces segmentation masks given image + language features | Per prompt |
Prompt Types
SAM 3 supports three prompt modalities:
| Prompt | Description |
|---|---|
| Text | Natural-language description, e.g. "truck" — unique to SAM 3 |
| Point | Click +point / -point to include/exclude regions |
| Rectangle | Draw a bounding box around the target object |
Text prompts are the recommended workflow: they drive detection open-vocabulary style, so you can label any object class without retraining.
Use with AnyLabeling (Recommended)
AnyLabeling is a desktop annotation tool with a built-in model manager that downloads, caches, and runs these models automatically — no coding required.
- Install:
pip install anylabeling - Launch:
anylabeling - Click the Brain button → select Segment Anything 3 (ViT-H) from the dropdown
- Type a text description (e.g.,
truck) in the text prompt field - Optionally refine with point/rectangle prompts
Use Programmatically with ONNX Runtime
import urllib.request, zipfile
url = "https://huggingface.co/vietanhdev/segment-anything-3-onnx-models/resolve/main/sam3_vit_h.zip"
urllib.request.urlretrieve(url, "sam3_vit_h.zip")
with zipfile.ZipFile("sam3_vit_h.zip") as z:
z.extractall("sam3")
Then use samexporter's inference module:
pip install samexporter
# Text prompt
python -m samexporter.inference \
--sam_variant sam3 \
--encoder_model sam3/sam3_image_encoder.onnx \
--decoder_model sam3/sam3_decoder.onnx \
--language_encoder_model sam3/sam3_language_encoder.onnx \
--image photo.jpg \
--prompt prompt.json \
--text_prompt "truck" \
--output result.png
Example prompt.json for a text-only query:
[{"type": "text", "data": "truck"}]
Model Architecture
SAM 3 follows the same encoder/decoder pattern as SAM and SAM 2, with an added CLIP-based language branch:
Input image ──► Image Encoder ──────────────────────────┐
▼
Text prompt ──► Language Encoder ──► Decoder ──► Masks + Scores + Boxes
▲
Optional: point / box prompts ──────────┘
The image encoder runs once per image and caches features. The language encoder runs once per text query. The decoder is lightweight and runs interactively for each prompt combination.
Re-export from Source
To re-export or customize the models using samexporter:
pip install samexporter
# Export all three SAM 3 ONNX components
python -m samexporter.export_sam3 --output_dir output_models/sam3
# Or use the convenience script:
bash convert_sam3.sh
Custom Model Config for AnyLabeling
To use a locally re-exported SAM 3 as a custom model in AnyLabeling, create a config.yaml:
type: segment_anything
name: sam3_vit_h_custom
display_name: Segment Anything 3 (ViT-H)
encoder_model_path: sam3_image_encoder.onnx
decoder_model_path: sam3_decoder.onnx
language_encoder_path: sam3_language_encoder.onnx
input_size: 1008
max_height: 1008
max_width: 1008
Then load it via Brain button → Load Custom Model in AnyLabeling.
Related Repositories
| Repo | Description |
|---|---|
| vietanhdev/samexporter | Export scripts, inference code, conversion tools |
| vietanhdev/anylabeling | Desktop annotation app powered by these models |
| facebook/sam3 | Original SAM 3 PyTorch checkpoint by Meta |
License
The ONNX models are derived from Meta's SAM 3, released under the SAM License. The export code is part of samexporter, released under the MIT license.