Update README.md

README.md

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 ---
 license: apache-2.0
+tags:
+- computer-vision
+- image-segmentation
+- mask-generation
+- vision-transformer
+- onnx
+- segment-anything
+- sam
+pipeline_tag: image-segmentation
 ---
+
+# Segment Anything (SAM) ONNX Weights
+
+This repository contains ONNX-optimized weights for the **Segment Anything Model (SAM)**, a foundation model designed for promptable and automatic mask generation (AMG).
+
+The architecture is decoupled into separate **Image Encoder** and **Mask Decoder** ONNX graphs. This design allows running heavy image encoding once while executing lightweight mask decoding interactively across multiple point prompts on both CPU and GPU without PyTorch dependencies.
+
+## Available Files
+* **`vit_b_encoder.onnx` & `vit_b_decoder.onnx`**: ViT-Base backbone, optimized for reduced memory footprint and higher throughput.
+* **`vit_l_encoder.onnx` & `vit_l_decoder.onnx`**: ViT-Large backbone, balanced representation capacity and inference speed.
+* **`vit_h_encoder.onnx` & `vit_h_decoder.onnx`**: ViT-Huge backbone, standard high-fidelity segmentation model.
+
+---
+
+## How to Use
+
+The easiest way to load and run these models is through the **[spatialhub](https://github.com/spatialhub-ai/spatialhub)** Python library:
+
+```python
+from spatialhub import SAM
+
+# Initialize SAM automatic mask generator
+segmentor = SAM(model_variant="vit_h")
+
+# Generate instance mask proposals across a uniform grid
+result = segmentor.generate_masks("image.jpg", points_per_side=32)
+
+# Save segmentation overlay
+result.visualize_mask("sam_output.png")
+
+
+@article{kirillov2023segany,
+  title={Segment Anything},
+  author={Kirillov, Alexander and Mintun, Eric and Ravi, Nikhila and Mao, Hanzi and Rolland, Chloe and Gustafson, Laura and Xiao, Tete and Whitehead, Spencer and Berg, Alexander C. and Lo, Wan-Yen and Doll{\'a}r, Piotr and Girshick, Ross},
+  journal={arXiv preprint arXiv:2304.02643},
+  year={2023}
+}