Add README

README.md

@@ -1,6 +1,104 @@
----
-license: apache-2.0
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-# Segment Anything Model 2 (SAM 2) in ONNX format
-
-Converted with [samexporter](https://github.com/vietanhdev/samexporter).
+---
+license: apache-2.0
+tags:
+  - image-segmentation
+  - segment-anything
+  - segment-anything-2
+  - onnx
+  - onnxruntime
+library_name: onnxruntime
+---
+
+# Segment Anything 2 (SAM 2) — ONNX Models
+
+ONNX-exported versions of Meta's [Segment Anything Model 2 (SAM 2)](https://github.com/facebookresearch/segment-anything-2), ready for CPU/GPU inference with [ONNX Runtime](https://onnxruntime.ai/) — no PyTorch required at runtime.
+
+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)**.
+
+> **Looking for SAM 2.1?** See [vietanhdev/segment-anything-2.1-onnx-models](https://huggingface.co/vietanhdev/segment-anything-2.1-onnx-models) — an improved version with better accuracy.
+
+## Available Models
+
+| File | Variant | Notes |
+|------|---------|-------|
+| `sam2_hiera_tiny.zip` | SAM 2 Hiera-Tiny | Smallest, fastest |
+| `sam2_hiera_small.zip` | SAM 2 Hiera-Small | Good balance |
+| `sam2_hiera_base_plus.zip` | SAM 2 Hiera-Base+ | Higher accuracy |
+| `sam2_hiera_large.zip` | SAM 2 Hiera-Large | Most accurate |
+
+Each zip contains two ONNX files: an **encoder** (runs once per image) and a **decoder** (runs interactively for each prompt).
+
+## Prompt Types
+
+- **Point** (`+point` / `-point`): click to include/exclude regions
+- **Rectangle**: draw a bounding box around the target object
+
+## Use with AnyLabeling (Recommended)
+
+[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.
+
+1. Install: `pip install anylabeling`
+2. Launch: `anylabeling`
+3. Click the **Brain** button → select a **Segment Anything 2** model from the dropdown
+4. Use point or rectangle prompts to segment objects
+
+[![AnyLabeling demo](https://user-images.githubusercontent.com/18329471/236625792-07f01838-3f69-48b0-a12e-30bad27bd921.gif)](https://github.com/vietanhdev/anylabeling)
+
+## Use Programmatically with ONNX Runtime
+
+```python
+import urllib.request, zipfile
+url = "https://huggingface.co/vietanhdev/segment-anything-2-onnx-models/resolve/main/sam2_hiera_tiny.zip"
+urllib.request.urlretrieve(url, "sam2_hiera_tiny.zip")
+with zipfile.ZipFile("sam2_hiera_tiny.zip") as z:
+    z.extractall("sam2_hiera_tiny")
+```
+
+Then use [samexporter](https://github.com/vietanhdev/samexporter)'s inference module:
+
+```bash
+pip install samexporter
+python -m samexporter.inference \
+    --encoder_model sam2_hiera_tiny/sam2_hiera_tiny.encoder.onnx \
+    --decoder_model sam2_hiera_tiny/sam2_hiera_tiny.decoder.onnx \
+    --image photo.jpg \
+    --prompt prompt.json \
+    --output result.png \
+    --sam_variant sam2
+```
+
+## Re-export from Source
+
+To re-export or customize the models using [samexporter](https://github.com/vietanhdev/samexporter):
+
+```bash
+pip install samexporter
+pip install git+https://github.com/facebookresearch/segment-anything-2.git
+
+# Download SAM 2 checkpoints
+cd original_models && bash download_sam2.sh && cd ..
+
+# Export Tiny variant
+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
+
+# Or convert all SAM 2 variants at once:
+bash convert_all_meta_sam2.sh
+```
+
+## Related Repositories
+
+| 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-2.1-onnx-models](https://huggingface.co/vietanhdev/segment-anything-2.1-onnx-models) | Improved SAM 2.1 ONNX models |
+| [facebookresearch/segment-anything-2](https://github.com/facebookresearch/segment-anything-2) | Original SAM 2 by Meta |
+
+## License
+
+The ONNX models are derived from Meta's SAM 2, released under the **Apache 2.0** license.
+The export code is part of [samexporter](https://github.com/vietanhdev/samexporter), released under the **MIT** license.