Update README.md

README.md

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-# Depth Anything V2 for Metric Depth Estimation
-
-![teaser](./assets/compare_zoedepth.png)
-
-We here provide a simple codebase to fine-tune our Depth Anything V2 pre-trained encoder for metric depth estimation. Built on our powerful encoder, we use a simple DPT head to regress the depth. We fine-tune our pre-trained encoder on synthetic Hypersim / Virtual KITTI datasets for indoor / outdoor metric depth estimation, respectively.
-
-
-# Pre-trained Models
-
-We provide **six metric depth models** of three scales for indoor and outdoor scenes, respectively.
-
-| Base Model | Params | Indoor (Hypersim) | Outdoor (Virtual KITTI 2) |
-|:-|-:|:-:|:-:|
-| Depth-Anything-V2-Small | 24.8M | [Download](https://huggingface.co/depth-anything/Depth-Anything-V2-Metric-Hypersim-Small/resolve/main/depth_anything_v2_metric_hypersim_vits.pth?download=true) | [Download](https://huggingface.co/depth-anything/Depth-Anything-V2-Metric-VKITTI-Small/resolve/main/depth_anything_v2_metric_vkitti_vits.pth?download=true) |
-| Depth-Anything-V2-Base | 97.5M | [Download](https://huggingface.co/depth-anything/Depth-Anything-V2-Metric-Hypersim-Base/resolve/main/depth_anything_v2_metric_hypersim_vitb.pth?download=true) | [Download](https://huggingface.co/depth-anything/Depth-Anything-V2-Metric-VKITTI-Base/resolve/main/depth_anything_v2_metric_vkitti_vitb.pth?download=true) |
-| Depth-Anything-V2-Large | 335.3M | [Download](https://huggingface.co/depth-anything/Depth-Anything-V2-Metric-Hypersim-Large/resolve/main/depth_anything_v2_metric_hypersim_vitl.pth?download=true) | [Download](https://huggingface.co/depth-anything/Depth-Anything-V2-Metric-VKITTI-Large/resolve/main/depth_anything_v2_metric_vkitti_vitl.pth?download=true) |
-
-*We recommend to first try our larger models (if computational cost is affordable) and the indoor version.*
-
-## Usage
-
-### Prepraration
-
-```bash
-git clone https://github.com/DepthAnything/Depth-Anything-V2
-cd Depth-Anything-V2/metric_depth
-pip install -r requirements.txt
-```
-
-Download the checkpoints listed [here](#pre-trained-models) and put them under the `checkpoints` directory.
-
-### Use our models
-```python
-import cv2
-import torch
-
-from depth_anything_v2.dpt import DepthAnythingV2
-
-model_configs = {
-    'vits': {'encoder': 'vits', 'features': 64, 'out_channels': [48, 96, 192, 384]},
-    'vitb': {'encoder': 'vitb', 'features': 128, 'out_channels': [96, 192, 384, 768]},
-    'vitl': {'encoder': 'vitl', 'features': 256, 'out_channels': [256, 512, 1024, 1024]}
-}
-
-encoder = 'vitl' # or 'vits', 'vitb'
-dataset = 'hypersim' # 'hypersim' for indoor model, 'vkitti' for outdoor model
-max_depth = 20 # 20 for indoor model, 80 for outdoor model
-
-model = DepthAnythingV2(**{**model_configs[encoder], 'max_depth': max_depth})
-model.load_state_dict(torch.load(f'checkpoints/depth_anything_v2_metric_{dataset}_{encoder}.pth', map_location='cpu'))
-model.eval()
-
-raw_img = cv2.imread('your/image/path')
-depth = model.infer_image(raw_img) # HxW depth map in meters in numpy
-```
-
-### Running script on images
-
-Here, we take the `vitl` encoder as an example. You can also use `vitb` or `vits` encoders.
-
-```bash
-# indoor scenes
-python run.py \
-  --encoder vitl \
-  --load-from checkpoints/depth_anything_v2_metric_hypersim_vitl.pth \
-  --max-depth 20 \
-  --img-path <path> --outdir <outdir> [--input-size <size>] [--save-numpy]
-
-# outdoor scenes
-python run.py \
-  --encoder vitl \
-  --load-from checkpoints/depth_anything_v2_metric_vkitti_vitl.pth \
-  --max-depth 80 \
-  --img-path <path> --outdir <outdir> [--input-size <size>] [--save-numpy]
-```
-
-### Project 2D images to point clouds:
-
-```bash
-python depth_to_pointcloud.py \
-  --encoder vitl \
-  --load-from checkpoints/depth_anything_v2_metric_hypersim_vitl.pth \
-  --max-depth 20 \
-  --img-path <path> --outdir <outdir>
-```
-
-### Reproduce training
-
-Please first prepare the [Hypersim](https://github.com/apple/ml-hypersim) and [Virtual KITTI 2](https://europe.naverlabs.com/research/computer-vision/proxy-virtual-worlds-vkitti-2/) datasets. Then:
-
-```bash
-bash dist_train.sh
-```
-
-
-## Citation
-
-If you find this project useful, please consider citing:
-
-```bibtex
-@article{depth_anything_v2,
-  title={Depth Anything V2},
-  author={Yang, Lihe and Kang, Bingyi and Huang, Zilong and Zhao, Zhen and Xu, Xiaogang and Feng, Jiashi and Zhao, Hengshuang},
-  journal={arXiv:2406.09414},
-  year={2024}
-}
-
-@inproceedings{depth_anything_v1,
-  title={Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data}, 
-  author={Yang, Lihe and Kang, Bingyi and Huang, Zilong and Xu, Xiaogang and Feng, Jiashi and Zhao, Hengshuang},
-  booktitle={CVPR},
-  year={2024}
-}
-```
+---
+title: Diffuse2PBR
+emoji: 🏆
+colorFrom: pink
+colorTo: yellow
+sdk: gradio
+sdk_version: 5.6.0
+app_file: app.py
+pinned: false
+license: cc-by-nc-2.0
+short_description: Convert Diffuse Textures to Height and Normal maps
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference