diff --git a/README.md b/README.md index 87ddd81..e1c80e8 100644 --- a/README.md +++ b/README.md @@ -11,7 +11,7 @@ Paper PDF Project Page - + Benchmark @@ -19,8 +19,11 @@ This work presents Depth Anything V2. It significantly outperforms [V1](https:// ![teaser](assets/teaser.png) + ## News +- **2024-07-06:** Depth Anything V2 is supported in [Transformers](https://github.com/huggingface/transformers/). See the [instructions](https://huggingface.co/docs/transformers/main/en/model_doc/depth_anything_v2) for convenient usage. +- **2024-06-25:** Depth Anything is integrated into [Apple Core ML Models](https://developer.apple.com/machine-learning/models/). See the instructions ([V1](https://huggingface.co/apple/coreml-depth-anything-small), [V2](https://huggingface.co/apple/coreml-depth-anything-v2-small)) for usage. - **2024-06-22:** We release [smaller metric depth models](https://github.com/DepthAnything/Depth-Anything-V2/tree/main/metric_depth#pre-trained-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. @@ -37,6 +40,7 @@ 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 | + ## Usage ### Prepraration @@ -75,6 +79,19 @@ raw_img = cv2.imread('your/image/path') depth = model.infer_image(raw_img) # HxW raw depth map in numpy ``` +If you do not want to clone this repository, you can also load our models through [Transformers](https://github.com/huggingface/transformers/). Below is a simple code snippet. Please refer to the [official page](https://huggingface.co/docs/transformers/main/en/model_doc/depth_anything_v2) for more details. + +- Note 1: Make sure you can connect to Hugging Face and have installed the latest Transformers. +- Note 2: Due to the [upsampling difference](https://github.com/huggingface/transformers/pull/31522#issuecomment-2184123463) between OpenCV (we used) and Pillow (HF used), predictions may differ slightly. So you are more recommended to use our models through the way introduced above. +```python +from transformers import pipeline +from PIL import Image + +pipe = pipeline(task="depth-estimation", model="depth-anything/Depth-Anything-V2-Small-hf") +image = Image.open('your/image/path') +depth = pipe(image)["depth"] +``` + ### Running script on *images* ```bash @@ -105,7 +122,6 @@ python run_video.py \ ***Our larger model has better temporal consistency on videos.*** - ### Gradio demo To use our gradio demo locally: @@ -119,7 +135,6 @@ You can also try our [online demo](https://huggingface.co/spaces/Depth-Anything/ ***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. - ## Fine-tuned to Metric Depth Estimation Please refer to [metric depth estimation](./metric_depth). @@ -129,11 +144,21 @@ Please refer to [metric depth estimation](./metric_depth). Please refer to [DA-2K benchmark](./DA-2K.md). + ## Community Support **We sincerely appreciate all the community support for our Depth Anything series. Thank you a lot!** -- TensorRT: https://github.com/spacewalk01/depth-anything-tensorrt +- Apple Core ML: + - https://developer.apple.com/machine-learning/models + - https://huggingface.co/apple/coreml-depth-anything-v2-small + - https://huggingface.co/apple/coreml-depth-anything-small +- Transformers: + - https://huggingface.co/docs/transformers/main/en/model_doc/depth_anything_v2 + - https://huggingface.co/docs/transformers/main/en/model_doc/depth_anything +- TensorRT: + - https://github.com/spacewalk01/depth-anything-tensorrt + - https://github.com/zhujiajian98/Depth-Anythingv2-TensorRT-python - ONNX: https://github.com/fabio-sim/Depth-Anything-ONNX - ComfyUI: https://github.com/kijai/ComfyUI-DepthAnythingV2 - Transformers.js (real-time depth in web): https://huggingface.co/spaces/Xenova/webgpu-realtime-depth-estimation @@ -142,6 +167,12 @@ Please refer to [DA-2K benchmark](./DA-2K.md). - https://github.com/FeiGeChuanShu/ncnn-android-depth_anything +## Acknowledgement + +We are sincerely grateful to the awesome Hugging Face team ([@Pedro Cuenca](https://huggingface.co/pcuenq), [@Niels Rogge](https://huggingface.co/nielsr), [@Merve Noyan](https://huggingface.co/merve), [@Amy Roberts](https://huggingface.co/amyeroberts), et al.) for their huge efforts in supporting our models in Transformers and Apple Core ML. + +We also thank the [DINOv2](https://github.com/facebookresearch/dinov2) team for contributing such impressive models to our community. + ## LICENSE