| | ---
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| | license: apache-2.0
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| | tags:
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| | - image-segmentation
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| | - segment-anything
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| | - segment-anything-2
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| | - onnx
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| | - onnxruntime
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| | library_name: onnxruntime
|
| | ---
|
| |
|
| | # Segment Anything 2.1 (SAM 2.1) — ONNX Models
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| |
|
| | ONNX-exported versions of Meta's **Segment Anything Model 2.1 (SAM 2.1)** — an improved version of [SAM 2](https://github.com/facebookresearch/segment-anything-2) with better accuracy and robustness — ready for CPU/GPU inference with [ONNX Runtime](https://onnxruntime.ai/).
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| |
|
| | These models are used by **[AnyLabeling](https://github.com/vietanhdev/anylabeling)** for AI-assisted image annotation, and exported by **[samexporter](https://github.com/vietanhdev/samexporter)**.
|
| |
|
| | ## Available Models
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| |
|
| | | File | Variant | Notes |
|
| | |------|---------|-------|
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| | | `sam2.1_hiera_tiny_20260221.zip` | SAM 2.1 Hiera-Tiny | Smallest, fastest |
|
| | | `sam2.1_hiera_small_20260221.zip` | SAM 2.1 Hiera-Small | Good balance |
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| | | `sam2.1_hiera_base_plus_20260221.zip` | SAM 2.1 Hiera-Base+ | Higher accuracy |
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| | | `sam2.1_hiera_large_20260221.zip` | SAM 2.1 Hiera-Large | Most accurate |
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| |
|
| | Each zip contains two ONNX files: an **encoder** (runs once per image) and a **decoder** (runs interactively for each prompt).
|
| |
|
| | ## What's Improved vs SAM 2?
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| |
|
| | SAM 2.1 offers improved segmentation accuracy and better handling of edge cases compared to SAM 2. The ONNX conversion process is identical, and both use the same inference API.
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| |
|
| | ## Prompt Types
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| |
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| | - **Point** (`+point` / `-point`): click to include/exclude regions
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| | - **Rectangle**: draw a bounding box around the target object
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| |
|
| | ## Use with AnyLabeling (Recommended)
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| |
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| | [AnyLabeling](https://github.com/vietanhdev/anylabeling) is a desktop annotation tool with a built-in model manager that downloads, caches, and runs these models automatically — no coding required.
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| |
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| | 1. Install: `pip install anylabeling`
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| | 2. Launch: `anylabeling`
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| | 3. Click the **Brain** button → select a **Segment Anything 2.1** model from the dropdown
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| | 4. Use point or rectangle prompts to segment objects
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| |
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| | [](https://github.com/vietanhdev/anylabeling)
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| |
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| | ## Use Programmatically with ONNX Runtime
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| |
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| | ```python
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| | import urllib.request, zipfile
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| | url = "https://huggingface.co/vietanhdev/segment-anything-2.1-onnx-models/resolve/main/sam2.1_hiera_tiny_20260221.zip"
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| | urllib.request.urlretrieve(url, "sam2.1_hiera_tiny.zip")
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| | with zipfile.ZipFile("sam2.1_hiera_tiny.zip") as z:
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| | z.extractall("sam2.1_hiera_tiny")
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| | ```
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| |
|
| | Then use [samexporter](https://github.com/vietanhdev/samexporter)'s inference module:
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| |
|
| | ```bash
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| | pip install samexporter
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| | python -m samexporter.inference \
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| | --encoder_model sam2.1_hiera_tiny/sam2.1_hiera_tiny.encoder.onnx \
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| | --decoder_model sam2.1_hiera_tiny/sam2.1_hiera_tiny.decoder.onnx \
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| | --image photo.jpg \
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| | --prompt prompt.json \
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| | --output result.png \
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| | --sam_variant sam2
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| | ```
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| |
|
| | ## Re-export from Source
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| |
|
| | To re-export or customize the models using [samexporter](https://github.com/vietanhdev/samexporter):
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| |
|
| | ```bash
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| | pip install samexporter
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| | pip install git+https://github.com/facebookresearch/segment-anything-2.git
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| |
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| | # Download SAM 2.1 checkpoints
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| | bash download_all_models.sh
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| |
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| | # Export Tiny variant
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| | python -m samexporter.export_sam2 \
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| | --checkpoint original_models/sam2.1_hiera_tiny.pt \
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| | --output_encoder output_models/sam2.1_hiera_tiny.encoder.onnx \
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| | --output_decoder output_models/sam2.1_hiera_tiny.decoder.onnx \
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| | --model_type sam2.1_hiera_tiny
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| |
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| | # Or convert all SAM 2 and SAM 2.1 variants at once:
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| | bash convert_all_meta_sam2.sh
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| | ```
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| |
|
| | ## Related Repositories
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| |
|
| | | Repo | Description |
|
| | |------|-------------|
|
| | | [vietanhdev/samexporter](https://github.com/vietanhdev/samexporter) | Export scripts, inference code, conversion tools |
|
| | | [vietanhdev/anylabeling](https://github.com/vietanhdev/anylabeling) | Desktop annotation app powered by these models |
|
| | | [vietanhdev/segment-anything-2-onnx-models](https://huggingface.co/vietanhdev/segment-anything-2-onnx-models) | Original SAM 2 ONNX models |
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| | | [facebookresearch/segment-anything-2](https://github.com/facebookresearch/segment-anything-2) | Original SAM 2 / SAM 2.1 by Meta |
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| |
|
| | ## License
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| |
|
| | The ONNX models are derived from Meta's SAM 2.1, released under the **Apache 2.0** license.
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| | The export code is part of [samexporter](https://github.com/vietanhdev/samexporter), released under the **MIT** license.
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| |
|
