| --- |
| license: apache-2.0 |
| base_model: |
| - depth-anything/Depth-Anything-V2-Small-hf |
| library_name: executorch |
| pipeline_tag: depth-estimation |
| inference: false |
| tags: |
| - executorch |
| - webgpu |
| - on-device |
| - browser |
| --- |
| # Depth Anything V2 Small for ExecuTorch WebGPU |
|
|
| This repository contains an [ExecuTorch](https://github.com/pytorch/executorch) conversion of |
| [depth-anything/Depth-Anything-V2-Small-hf](https://huggingface.co/depth-anything/Depth-Anything-V2-Small-hf) 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)). |
|
|
| - **Base model:** [depth-anything/Depth-Anything-V2-Small-hf](https://huggingface.co/depth-anything/Depth-Anything-V2-Small-hf) |
| - **Format:** ExecuTorch `.pte` programs delegated to the WebGPU backend |
| - **Quantization:** fp32 (as exported) |
| - **Total size:** 92.8 MB |
|
|
| > **Note:** This is the Small variant, which is licensed Apache-2.0 (the larger Depth Anything V2 variants use a different license). |
|
|
| ## About the original model |
|
|
| The Small variant of Depth Anything V2, a monocular depth estimation model trained on about 595K synthetic labeled images and 62M+ real unlabeled images. |
|
|
| - **Developed by:** Lihe Yang et al. (HKU and TikTok) |
| - **Paper:** [Depth Anything V2](https://arxiv.org/abs/2406.09414) |
| - 24.8M parameters |
| - Predicts relative depth from a single image |
|
|
| For training data, evaluation results and full details, see the original model card: [depth-anything/Depth-Anything-V2-Small-hf](https://huggingface.co/depth-anything/Depth-Anything-V2-Small-hf). |
|
|
| ## Intended use and limitations |
|
|
| Relative depth maps for photos and camera frames. |
|
|
| Outputs relative (not metric) depth. Accuracy drops on reflective or transparent surfaces and unusual scenes. |
|
|
| ## What changed in this conversion |
|
|
| - Fixed 224x224 input (upstream processes larger images) |
|
|
| ## Inputs and outputs |
|
|
| - Input `[1, 3, 224, 224]` float32 RGB, ImageNet-normalized: `(x / 255 - mean) / std`, mean 0.485/0.456/0.406, std 0.229/0.224/0.225 |
| - Output `[1, 224, 224]` float32 relative inverse depth (larger means closer) |
|
|
| ## Files |
|
|
| | File | Size | SHA-256 | |
| |---|---|---| |
| | `depth_v2s_webgpu.pte` | 92.8 MB | `285602ed3a34a26a…` | |
|
|
| Full checksums are in `manifest.json`. The playground verifies them after download. |
|
|
| ## Running it |
|
|
| 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 |
|
|
| - **Weights:** `depth-anything/Depth-Anything-V2-Small-hf` @ `5426e4f0f36572d16453bbda7a8389317b1bef99`, file `None`, sha256 `None` (official) |
| - **Exported with:** executorch 1.5.1+cpu, torch 2.14.1+cpu, torchao 0.18.0+cpu, transformers 5.19.0 (executorch git `3b6068392324`) |
| - **Reproduce:** `python recipes/depth-anything-v2-small.py --out out/depth-anything-v2-small` (recipe in `export/`) |
| - Re-running the recipe produces byte-identical files. |
|
|
| ## Citation |
|
|
| If you use this model, please cite the original work: |
|
|
| ```bibtex |
| @misc{yang2024depth, |
| title={Depth Anything V2}, |
| author={Lihe Yang and Bingyi Kang and Zilong Huang and Zhen Zhao and Xiaogang Xu and Jiashi Feng and Hengshuang Zhao}, |
| year={2024}, |
| eprint={2406.09414}, |
| archivePrefix={arXiv}, |
| primaryClass={cs.CV} |
| } |
| ``` |
|
|
| ## 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) by Meta Platforms, Inc. |
|
|
