Add README

README.md

@@ -1,3 +1,101 @@
-# Segment Anything Model (SAM) in ONNX format
-
-Converted with [samexporter](https://github.com/vietanhdev/samexporter).
+---
+license: apache-2.0
+tags:
+  - image-segmentation
+  - segment-anything
+  - onnx
+  - onnxruntime
+library_name: onnxruntime
+---
+
+# Segment Anything (SAM + MobileSAM) — ONNX Models
+
+ONNX-exported versions of Meta's [Segment Anything Model (SAM)](https://github.com/facebookresearch/segment-anything) and [MobileSAM](https://github.com/ChaoningZhang/MobileSAM), 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)**.
+
+## Available Models
+
+| File | Variant | Encoder size | Notes |
+|------|---------|-------------|-------|
+| `sam_vit_b_01ec64.zip` | SAM ViT-B | ~90 MB | Fastest, lowest accuracy |
+| `sam_vit_b_01ec64_quant.zip` | SAM ViT-B (Quant) | ~25 MB | Quantized — smaller & faster |
+| `sam_vit_l_0b3195.zip` | SAM ViT-L | ~330 MB | Good balance |
+| `sam_vit_l_0b3195_quant.zip` | SAM ViT-L (Quant) | ~83 MB | Quantized — smaller & faster |
+| `sam_vit_h_4b8939.zip` | SAM ViT-H | ~630 MB | Highest accuracy |
+| `sam_vit_h_4b8939_quant.zip` | SAM ViT-H (Quant) | ~158 MB | Quantized — smaller & faster |
+| `mobile_sam_20230629.zip` | MobileSAM | ~9 MB | Ultra-lightweight |
+
+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 SAM 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, pathlib
+# Download and extract
+url = "https://huggingface.co/vietanhdev/segment-anything-onnx-models/resolve/main/sam_vit_b_01ec64.zip"
+urllib.request.urlretrieve(url, "sam_vit_b_01ec64.zip")
+with zipfile.ZipFile("sam_vit_b_01ec64.zip") as z:
+    z.extractall("sam_vit_b_01ec64")
+```
+
+Then use [samexporter](https://github.com/vietanhdev/samexporter)'s inference module:
+
+```bash
+pip install samexporter
+python -m samexporter.inference \
+    --encoder_model sam_vit_b_01ec64/sam_vit_b_encoder.onnx \
+    --decoder_model sam_vit_b_01ec64/sam_vit_b_decoder.onnx \
+    --image photo.jpg \
+    --prompt prompt.json \
+    --output result.png
+```
+
+## Re-export from Source
+
+To re-export or customize the models using [samexporter](https://github.com/vietanhdev/samexporter):
+
+```bash
+pip install samexporter
+# Export SAM ViT-H encoder + decoder
+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 --use-preprocess
+python -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 --return-single-mask
+# Or convert all SAM variants at once:
+bash convert_all_meta_sam.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 |
+| [facebookresearch/segment-anything](https://github.com/facebookresearch/segment-anything) | Original SAM by Meta |
+| [ChaoningZhang/MobileSAM](https://github.com/ChaoningZhang/MobileSAM) | Original MobileSAM |
+
+## License
+
+The ONNX models are derived from Meta's SAM and MobileSAM, both released under the **Apache 2.0** license.
+The export code is part of [samexporter](https://github.com/vietanhdev/samexporter), released under the **MIT** license.