Export Segment Anything, MobileSAM, Segment Anything 2 / 2.1, and Segment Anything 3 to ONNX for easy, dependency-free deployment.
Supported models:
| Model | Prompt types | Notes |
|---|---|---|
| SAM ViT-B / ViT-L / ViT-H | Point, Rectangle | Original Meta SAM |
| SAM ViT-B / ViT-L / ViT-H (quantized) | Point, Rectangle | Smaller, faster variants |
| MobileSAM | Point, Rectangle | Lightweight; fast on CPU |
| SAM2 Tiny / Small / Base+ / Large | Point, Rectangle | Meta SAM 2 |
| SAM2.1 Tiny / Small / Base+ / Large | Point, Rectangle | Improved SAM 2 |
| SAM3 ViT-H | Text, Point, Rectangle | Open-vocabulary text-driven segmentation |
Requires Python 3.11+.
pip install torch==2.10.0 torchvision==0.25.0 --index-url https://download.pytorch.org/whl/cpu
pip install samexporterNote — Windows users: The optional
onnxsimmodel simplifier (used during ONNX export) has no pre-built wheel for Windows. If you plan to export models and want simplification, install with:pip install "samexporter[export]"or enable Windows Long Path support before installing.
pip install torch==2.10.0 torchvision==0.25.0 --index-url https://download.pytorch.org/whl/cpu
git clone --recurse-submodules https://github.com/vietanhdev/samexporter
cd samexporter
pip install -e .Place checkpoints in original_models/:
original_models/
sam_vit_b_01ec64.pth
sam_vit_l_0b3195.pth
sam_vit_h_4b8939.pth
mobile_sam.pt
Download links:
# SAM ViT-H (most accurate)
python -m samexporter.export_encoder \
--checkpoint original_models/sam_vit_h_4b8939.pth \
--output output_models/sam_vit_h_4b8939.encoder.onnx \
--model-type vit_h \
--quantize-out output_models/sam_vit_h_4b8939.encoder.quant.onnx \
--use-preprocess
# SAM ViT-B (fastest)
python -m samexporter.export_encoder \
--checkpoint original_models/sam_vit_b_01ec64.pth \
--output output_models/sam_vit_b_01ec64.encoder.onnx \
--model-type vit_b \
--quantize-out output_models/sam_vit_b_01ec64.encoder.quant.onnx \
--use-preprocesspython -m samexporter.export_decoder \
--checkpoint original_models/sam_vit_h_4b8939.pth \
--output output_models/sam_vit_h_4b8939.decoder.onnx \
--model-type vit_h \
--quantize-out output_models/sam_vit_h_4b8939.decoder.quant.onnx \
--return-single-maskRemove --return-single-mask to return multiple mask proposals.
Batch convert all SAM models:
bash convert_all_meta_sam.sh
bash convert_mobile_sam.shpython -m samexporter.inference \
--encoder_model output_models/sam_vit_h_4b8939.encoder.onnx \
--decoder_model output_models/sam_vit_h_4b8939.decoder.onnx \
--image images/truck.jpg \
--prompt images/truck_prompt.json \
--output output_images/truck.png \
--show
python -m samexporter.inference \
--encoder_model output_models/sam_vit_h_4b8939.encoder.onnx \
--decoder_model output_models/sam_vit_h_4b8939.decoder.onnx \
--image images/plants.png \
--prompt images/plants_prompt1.json \
--output output_images/plants_01.png \
--show
cd original_models && bash download_sam2.shOr download manually:
original_models/
sam2_hiera_tiny.pt
sam2_hiera_small.pt
sam2_hiera_base_plus.pt
sam2_hiera_large.pt
sam2.1_hiera_tiny.pt
sam2.1_hiera_small.pt
sam2.1_hiera_base_plus.pt
sam2.1_hiera_large.pt
pip install git+https://github.com/facebookresearch/segment-anything-2.git# Single model example (SAM2 Tiny)
python -m samexporter.export_sam2 \
--checkpoint original_models/sam2_hiera_tiny.pt \
--output_encoder output_models/sam2_hiera_tiny.encoder.onnx \
--output_decoder output_models/sam2_hiera_tiny.decoder.onnx \
--model_type sam2_hiera_tiny
# SAM2.1 example
python -m samexporter.export_sam2 \
--checkpoint original_models/sam2.1_hiera_tiny.pt \
--output_encoder output_models/sam2.1_hiera_tiny.encoder.onnx \
--output_decoder output_models/sam2.1_hiera_tiny.decoder.onnx \
--model_type sam2.1_hiera_tinyBatch convert all SAM2 / SAM2.1 models:
bash convert_all_meta_sam2.shpython -m samexporter.inference \
--encoder_model output_models/sam2_hiera_tiny.encoder.onnx \
--decoder_model output_models/sam2_hiera_tiny.decoder.onnx \
--image images/truck.jpg \
--prompt images/truck_prompt.json \
--sam_variant sam2 \
--output output_images/sam2_truck.png \
--show
SAM3 extends the SAM family with open-vocabulary, text-driven segmentation. In addition to point and rectangle prompts, it accepts text prompts (e.g., "truck", "person") to detect and segment objects without any prior training on those classes.
SAM3 exports into three separate ONNX models: an image encoder, a language (text) encoder, and a decoder.
Pre-exported models are available on HuggingFace and are downloaded automatically:
vietanhdev/segment-anything-3-onnx-models
sam3_image_encoder.onnx (+ .data)
sam3_language_encoder.onnx (+ .data)
sam3_decoder.onnx (+ .data)
# Clone the SAM3 source (required for export only, not inference)
git submodule update --init sam3
# Install SAM3 dependencies
pip install osam
# Export (add --simplify for ONNX simplification, requires [export] extra on Windows)
python -m samexporter.export_sam3 \
--output_dir output_models/sam3 \
--opset 18Text-only prompt (detects all instances matching the text):
python -m samexporter.inference \
--sam_variant sam3 \
--encoder_model output_models/sam3/sam3_image_encoder.onnx \
--decoder_model output_models/sam3/sam3_decoder.onnx \
--language_encoder_model output_models/sam3/sam3_language_encoder.onnx \
--image images/truck.jpg \
--prompt images/truck_sam3.json \
--text_prompt "truck" \
--output output_images/truck_sam3.png \
--showText + rectangle prompt (text guides detection, rectangle refines region):
python -m samexporter.inference \
--sam_variant sam3 \
--encoder_model output_models/sam3/sam3_image_encoder.onnx \
--decoder_model output_models/sam3/sam3_decoder.onnx \
--language_encoder_model output_models/sam3/sam3_language_encoder.onnx \
--image images/truck.jpg \
--prompt images/truck_sam3_box.json \
--text_prompt "truck" \
--output output_images/truck_sam3_box.png \
--showText + point prompt:
python -m samexporter.inference \
--sam_variant sam3 \
--encoder_model output_models/sam3/sam3_image_encoder.onnx \
--decoder_model output_models/sam3/sam3_decoder.onnx \
--language_encoder_model output_models/sam3/sam3_language_encoder.onnx \
--image images/truck.jpg \
--prompt images/truck_sam3_point.json \
--text_prompt "truck" \
--output output_images/truck_sam3_point.png \
--showNote: Always pass
--text_promptfor SAM3. Without it the model defaults to a "visual" text token and may produce zero detections.
Prompts are JSON files containing a list of mark objects:
[
{"type": "point", "data": [x, y], "label": 1},
{"type": "rectangle", "data": [x1, y1, x2, y2]},
{"type": "text", "data": "object description"}
]label: 1— foreground point;label: 0— background pointtype: "text"is specific to SAM3 (use--text_prompton the CLI instead for convenience)
- Use quantized models (
*.quant.onnx) for faster inference and smaller file size with minimal accuracy loss. - SAM ViT-B is the fastest SAM1 variant; SAM ViT-H is the most accurate.
- SAM2 Tiny / SAM2.1 Tiny are good CPU-friendly choices for SAM2.
- SAM3 is slower due to its three-model pipeline but uniquely supports natural-language object queries.
- Run the encoder once per image; the lightweight decoder handles prompt changes in real time.
pip install pytest
pytest tests/This package was originally developed for the auto-labeling feature in AnyLabeling. However, it can be used independently for any ONNX-based deployment scenario.
MIT — see LICENSE for details.
- ONNX-SAM2-Segment-Anything: https://github.com/ibaiGorordo/ONNX-SAM2-Segment-Anything
- sam3-onnx: https://github.com/wkentaro/sam3-onnx