Add ExecuTorch WebGPU program for sam2-hiera-tiny

NOTICE

@@ -1,6 +1,6 @@
 This repository contains a modified version of facebook/sam2-hiera-tiny.
 
 Modifications by Meta Platforms, Inc.: conversion to the ExecuTorch program format (.pte) for the
-ExecuTorch WebGPU backend, and quantization as described in README.md.
+ExecuTorch WebGPU backend.
 
 The original work and these modifications are distributed under the license in LICENSE.

README.md

@@ -2,9 +2,9 @@
 license: apache-2.0
 base_model:
 - facebook/sam2-hiera-tiny
-base_model_relation: quantized
 library_name: executorch
 pipeline_tag: mask-generation
+inference: false
 tags:
 - executorch
 - webgpu
@@ -15,14 +15,41 @@ tags:
 
 This repository contains an [ExecuTorch](https://github.com/pytorch/executorch) conversion of
 [facebook/sam2-hiera-tiny](https://huggingface.co/facebook/sam2-hiera-tiny) for the ExecuTorch **WebGPU** backend, so the model runs entirely in a web browser on
-the visitor's GPU. It powers the corresponding demo in the
-ExecuTorch WebGPU Playground (launching soon on [executorch.ai](https://executorch.ai)).
+the visitor's GPU. It powers the corresponding demo in the ExecuTorch WebGPU Playground (launching soon on [executorch.ai](https://executorch.ai)).
 
 - **Base model:** [facebook/sam2-hiera-tiny](https://huggingface.co/facebook/sam2-hiera-tiny)
 - **Format:** ExecuTorch `.pte` programs delegated to the WebGPU backend
 - **Quantization:** fp32 (as exported)
 - **Total size:** 149.3 MB
 
+## About the original model
+
+The tiny Hiera variant of SAM 2, a foundation model for promptable visual segmentation in images and videos.
+
+- **Developed by:** Meta FAIR
+- **Paper:** [SAM 2: Segment Anything in Images and Videos](https://arxiv.org/abs/2408.00714)
+- 38.9M parameters
+- Segments any object from point or box prompts
+
+For training data, evaluation results and full details, see the original model card: [facebook/sam2-hiera-tiny](https://huggingface.co/facebook/sam2-hiera-tiny).
+
+## Intended use and limitations
+
+Interactive segmentation of objects in images from clicks or boxes.
+
+This conversion covers single-image segmentation only (no video memory). Masks can be imprecise on thin structures, low-contrast boundaries or heavily occluded objects.
+
+## What changed in this conversion
+
+- Image encoder and mask decoder exported as separate programs
+- Fixed 512x512 input (upstream: 1024x1024)
+- Prompt embeddings are precomputed and shipped as `prompt_*.bin` files
+
+## Inputs and outputs
+
+- `sam2_encoder.pte`: one 512x512 RGB image, ImageNet-normalized (mean 0.485/0.456/0.406, std 0.229/0.224/0.225), to image embeddings
+- `sam2_decoder.pte`: image embeddings plus prompt tokens built from `prompt_*.bin`, to mask logits
+
 ## Files
 
 | File | Size | SHA-256 |
@@ -42,18 +69,35 @@ Full checksums are in `manifest.json`. The playground verifies them after downlo
 
 ## Running it
 
-These programs need the ExecuTorch WebGPU runtime compiled to WebAssembly
-([backends/webgpu](https://github.com/pytorch/executorch/tree/main/backends/webgpu)). The easiest way to
-try the model is the ExecuTorch WebGPU Playground (launching soon on [executorch.ai](https://executorch.ai)),
-which downloads these files once, caches them in the browser, and runs everything locally.
+These programs were exported with the ExecuTorch WebGPU backend
+([backends/webgpu](https://github.com/pytorch/executorch/tree/main/backends/webgpu)) and validated with the
+WebAssembly runtime build used by the ExecuTorch WebGPU Playground (launching soon on [executorch.ai](https://executorch.ai)). That runtime build is not published yet; until it is,
+the playground is the supported way to run them. It downloads these files once, caches them in the browser,
+and runs everything locally.
+
+Requirements: a browser with WebGPU enabled.
 
 ## Provenance
 
 - Converted from the upstream checkpoint by the ExecuTorch team with the WebGPU export path.
 - A reproducible export recipe will be published alongside the open-source ExecuTorch WebGPU runtime.
 
+## Citation
+
+If you use this model, please cite the original work:
+
+```bibtex
+@article{ravi2024sam2,
+  title={SAM 2: Segment Anything in Images and Videos},
+  author={Ravi, Nikhila and Gabeur, Valentin and Hu, Yuan-Ting and Hu, Ronghang and Ryali, Chaitanya and Ma, Tengyu and Khedr, Haitham and R{\"a}dle, Roman and Rolland, Chloe and Gustafson, Laura and Mintun, Eric and Pan, Junting and Alwala, Kalyan Vasudev and Carion, Nicolas and Wu, Chao-Yuan and Girshick, Ross and Doll{\'a}r, Piotr and Feichtenhofer, Christoph},
+  journal={arXiv preprint arXiv:2408.00714},
+  url={https://arxiv.org/abs/2408.00714},
+  year={2024}
+}
+```
+
 ## License
 
 This is a derivative of the upstream model and is distributed under the same terms
 (`apache-2.0`); see `LICENSE`.
-Modifications (conversion to the ExecuTorch format and quantization) by Meta Platforms, Inc.
+Modifications (conversion to the ExecuTorch format) by Meta Platforms, Inc.