From f5115d398d833b04667aff64df1f725b5f176e6f Mon Sep 17 00:00:00 2001 From: Lihe Yang Date: Fri, 21 Jun 2024 09:40:06 -0700 Subject: [PATCH] Release smaller metric depth models --- README.md | 73 ++++++++++++++++++++++--------------- metric_depth/README.md | 81 ++++++++++++++++++++++++++++++++++++------ 2 files changed, 115 insertions(+), 39 deletions(-) diff --git a/README.md b/README.md index 5f66353..d420b07 100644 --- a/README.md +++ b/README.md @@ -21,6 +21,7 @@ This work presents Depth Anything V2. It significantly outperforms [V1](https:// ## News +- **2024-06-22:** We release smaller metric depth models based on Depth-Anything-V2-Small and Base. - **2024-06-20:** Our repository and project page are flagged by GitHub and removed from the public for 6 days. Sorry for the inconvenience. - **2024-06-14:** Paper, project page, code, models, demo, and benchmark are all released. @@ -36,26 +37,9 @@ We provide **four models** of varying scales for robust relative depth estimatio | Depth-Anything-V2-Large | 335.3M | [Download](https://huggingface.co/depth-anything/Depth-Anything-V2-Large/resolve/main/depth_anything_v2_vitl.pth?download=true) | | Depth-Anything-V2-Giant | 1.3B | Coming soon | - -### Code snippet to use our models -```python -import cv2 -import torch - -from depth_anything_v2.dpt import DepthAnythingV2 - -# take depth-anything-v2-large as an example -model = DepthAnythingV2(encoder='vitl', features=256, out_channels=[256, 512, 1024, 1024]) -model.load_state_dict(torch.load('checkpoints/depth_anything_v2_vitl.pth', map_location='cpu')) -model.eval() - -raw_img = cv2.imread('your/image/path') -depth = model.infer_image(raw_img) # HxW raw depth map -``` - ## Usage -### Installation +### Prepraration ```bash git clone https://github.com/DepthAnything/Depth-Anything-V2 @@ -63,14 +47,43 @@ cd Depth-Anything-V2 pip install -r requirements.txt ``` -### Running +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]}, + 'vitg': {'encoder': 'vitg', 'features': 384, 'out_channels': [1536, 1536, 1536, 1536]} +} + +encoder = 'vitl' # or 'vits', 'vitb', 'vitg' + +model = DepthAnythingV2(**model_configs[encoder]) +model.load_state_dict(torch.load(f'checkpoints/depth_anything_v2_{encoder}.pth', map_location='cpu')) +model.eval() + +raw_img = cv2.imread('your/image/path') +depth = model.infer_image(raw_img) # HxW raw depth map in numpy +``` + +### Running script on *images* ```bash -python run.py --encoder --img-path --outdir [--input-size ] [--pred-only] [--grayscale] +python run.py \ + --encoder \ + --img-path --outdir \ + [--input-size ] [--pred-only] [--grayscale] ``` Options: - `--img-path`: You can either 1) point it to an image directory storing all interested images, 2) point it to a single image, or 3) point it to a text file storing all image paths. -- `--input-size` (optional): By default, we use input size `518` for model inference. **You can increase the size for even more fine-grained results.** +- `--input-size` (optional): By default, we use input size `518` for model inference. ***You can increase the size for even more fine-grained results.*** - `--pred-only` (optional): Only save the predicted depth map, without raw image. - `--grayscale` (optional): Save the grayscale depth map, without applying color palette. @@ -79,13 +92,16 @@ For example: python run.py --encoder vitl --img-path assets/examples --outdir depth_vis ``` -**If you want to use Depth Anything V2 on videos:** +### Running script on *videos* ```bash -python run_video.py --encoder --video-path assets/examples_video --outdir video_depth_vis [--input-size ] [--pred-only] [--grayscale] +python run_video.py \ + --encoder \ + --video-path assets/examples_video --outdir video_depth_vis \ + [--input-size ] [--pred-only] [--grayscale] ``` -*Please note that our larger model has better temporal consistency on videos.* +***Our larger model has better temporal consistency on videos.*** ### Gradio demo @@ -98,7 +114,7 @@ python app.py You can also try our [online demo](https://huggingface.co/spaces/Depth-Anything/Depth-Anything-V2). -**Note: Compared to V1, we have made a minor modification to the DINOv2-DPT architecture (originating from this [issue](https://github.com/LiheYoung/Depth-Anything/issues/81)).** In V1, we *unintentionally* used features from the last four layers of DINOv2 for decoding. In V2, we use [intermediate features](https://github.com/DepthAnything/Depth-Anything-V2/blob/2cbc36a8ce2cec41d38ee51153f112e87c8e42d8/depth_anything_v2/dpt.py#L164-L169) instead. Although this modification did not improve details or accuracy, we decided to follow this common practice. +***Note: Compared to V1, we have made a minor modification to the DINOv2-DPT architecture (originating from this [issue](https://github.com/LiheYoung/Depth-Anything/issues/81)).*** In V1, we *unintentionally* used features from the last four layers of DINOv2 for decoding. In V2, we use [intermediate features](https://github.com/DepthAnything/Depth-Anything-V2/blob/2cbc36a8ce2cec41d38ee51153f112e87c8e42d8/depth_anything_v2/dpt.py#L164-L169) instead. Although this modification did not improve details or accuracy, we decided to follow this common practice. @@ -115,9 +131,10 @@ Please refer to [DA-2K benchmark](./DA-2K.md). **We sincerely appreciate all the community support for our Depth Anything series. Thank you a lot!** -- Depth Anything V2 TensorRT: https://github.com/spacewalk01/depth-anything-tensorrt -- Depth Anything V2 in ComfyUI: https://github.com/kijai/ComfyUI-DepthAnythingV2 -- Depth Anything V2 in Android: +- TensorRT: https://github.com/spacewalk01/depth-anything-tensorrt +- ComfyUI: https://github.com/kijai/ComfyUI-DepthAnythingV2 +- Transformers.js (real-time depth in web): https://huggingface.co/spaces/Xenova/webgpu-realtime-depth-estimation +- Android: - https://github.com/shubham0204/Depth-Anything-Android - https://github.com/FeiGeChuanShu/ncnn-android-depth_anything diff --git a/metric_depth/README.md b/metric_depth/README.md index f59ab70..fc84a2f 100644 --- a/metric_depth/README.md +++ b/metric_depth/README.md @@ -5,31 +5,83 @@ 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 -### Inference +### Prepraration -Please first download our pre-trained metric depth models and put them under the `checkpoints` directory: -- [Indoor model from Hypersim](https://huggingface.co/depth-anything/Depth-Anything-V2-Metric-Hypersim-Large/resolve/main/depth_anything_v2_metric_hypersim_vitl.pth?download=true) -- [Outdoor model from Virtual KITTI 2](https://huggingface.co/depth-anything/Depth-Anything-V2-Metric-VKITTI-Large/resolve/main/depth_anything_v2_metric_vkitti_vitl.pth?download=true) +```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 --outdir [--input-size ] [--save-numpy] + --encoder vitl \ + --load-from checkpoints/depth_anything_v2_metric_hypersim_vitl.pth \ + --max-depth 20 \ + --img-path --outdir [--input-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 --outdir [--input-size ] [--save-numpy] + --encoder vitl \ + --load-from checkpoints/depth_anything_v2_metric_vkitti_vitl.pth \ + --max-depth 80 \ + --img-path --outdir [--input-size ] [--save-numpy] ``` -You can also project 2D images to point clouds: +### 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 --outdir + --encoder vitl \ + --load-from checkpoints/depth_anything_v2_metric_hypersim_vitl.pth \ + --max-depth 20 \ + --img-path --outdir ``` ### Reproduce training @@ -52,4 +104,11 @@ If you find this project useful, please consider citing: 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} +} ```