Add gallery

README.md

@@ -19,6 +19,7 @@ This work presents Depth Anything, a highly practical solution for robust monocu
 
 ## News
 
+* **2024-02-05:** [Depth Anything Gallery](./gallery.md) is released. Thank all the users!
 * **2024-02-02:** Depth Anything serves as the default depth processor for [InstantID](https://github.com/InstantID/InstantID) and [InvokeAI](https://github.com/invoke-ai/InvokeAI/releases/tag/v3.6.1).
 * **2024-01-25:** Support [video depth visualization](./run_video.py).
 * **2024-01-23:** The new ControlNet based on Depth Anything is integrated into [ControlNet WebUI](https://github.com/Mikubill/sd-webui-controlnet) and [ComfyUI's ControlNet](https://github.com/Fannovel16/comfyui_controlnet_aux).

metric_depth/README.md

@@ -53,7 +53,7 @@ Please follow [ZoeDepth](https://github.com/isl-org/ZoeDepth) to prepare the tra
 
 ## Evaluation
 
-Make sure you have downloaded our pre-trained models [here](https://huggingface.co/spaces/LiheYoung/Depth-Anything/tree/main/checkpoints_metric_depth) and put them under the ``checkpoints`` directory.
+Make sure you have downloaded our pre-trained metric-depth models [here](https://huggingface.co/spaces/LiheYoung/Depth-Anything/tree/main/checkpoints_metric_depth) (for evaluation) and pre-trained relative-depth model [here](https://huggingface.co/spaces/LiheYoung/Depth-Anything/blob/main/checkpoints/depth_anything_vitl14.pth) (for initializing the encoder) and put them under the ``checkpoints`` directory.
 
 Indoor:
 ```bash
@@ -67,7 +67,7 @@ python evaluate.py -m zoedepth --pretrained_resource="local::./checkpoints/depth
 
 ## Training
 
-Please first download our Depth Anything pre-trained models [here](https://huggingface.co/spaces/LiheYoung/Depth-Anything/blob/main/checkpoints/depth_anything_vitl14.pth), and put it under the ``checkpoints`` directory.
+Please first download our Depth Anything pre-trained model [here](https://huggingface.co/spaces/LiheYoung/Depth-Anything/blob/main/checkpoints/depth_anything_vitl14.pth), and put it under the ``checkpoints`` directory.
 
 ```bash
 python train_mono.py -m zoedepth -d <nyu | kitti> --pretrained_resource=""

semseg/README.md

@@ -37,18 +37,22 @@ Note that our results are obtained *without* Mapillary pre-training.
 - [Cityscapes-ViT-L-mIoU-86.4](https://huggingface.co/spaces/LiheYoung/Depth-Anything/blob/main/checkpoints_semseg/cityscapes_vitl_mIoU_86.4.pth)
 - [ADE20K-ViT-L-mIoU-59.4](https://huggingface.co/spaces/LiheYoung/Depth-Anything/blob/main/checkpoints_semseg/ade20k_vitl_mIoU_59.4.pth)
 
-**Note:** If you want to reproduce the training process, please 1) download the [Depth Anything pre-trained model](https://huggingface.co/spaces/LiheYoung/Depth-Anything/blob/main/checkpoints/depth_anything_vitl14.pth) (to initialize the encoder) and 2) put it under the ``checkpoints`` folder.
-
 
 ## Installation
 
 Please refer to [MMSegmentation](https://github.com/open-mmlab/mmsegmentation/blob/main/docs/en/get_started.md#installation) for instructions.
 
+Please also install mmdet to support Mask2Former:
+```bash
+pip install "mmdet>=3.0.0rc4"
+```
+
 After installation:
 - move our [config/depth_anything](./config/depth_anything/) to mmseg's [config](https://github.com/open-mmlab/mmsegmentation/tree/main/configs)
 - move our [dinov2.py](./dinov2.py) to mmseg's [backbones](https://github.com/open-mmlab/mmsegmentation/tree/main/mmseg/models/backbones)
 - add DINOv2 in mmseg's [models/backbones/\_\_init\_\_.py](https://github.com/open-mmlab/mmsegmentation/blob/main/mmseg/models/backbones/__init__.py)
 - download our provided [torchhub](https://github.com/LiheYoung/Depth-Anything/tree/main/torchhub) directory and put it at the root of your working directory
 
-For training or inference with our pre-trained models, please refer to MMSegmentation [instructions](https://github.com/open-mmlab/mmsegmentation/blob/main/docs/en/user_guides/4_train_test.md).
+**Note:** If you want to reproduce the **training** process, please 1) download the [Depth Anything pre-trained model](https://huggingface.co/spaces/LiheYoung/Depth-Anything/blob/main/checkpoints/depth_anything_vitl14.pth) (to initialize the encoder) and 2) put it under the ``checkpoints`` folder.
 
+For training or inference with our pre-trained models, please refer to MMSegmentation [instructions](https://github.com/open-mmlab/mmsegmentation/blob/main/docs/en/user_guides/4_train_test.md).