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Update local model loading way

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Lihe Yang
2024-03-02 17:16:13 +08:00
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commit d1ad9cdf69
1 changed files with 20 additions and 10 deletions
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@@ -19,6 +19,7 @@ This work presents Depth Anything, a highly practical solution for robust monocu
## News
* **2024-02-27:** Depth Anything is accepted by CVPR 2024.
* **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). An [online demo for video](https://huggingface.co/spaces/JohanDL/Depth-Anything-Video) is also available.
@@ -87,21 +88,30 @@ depth_anything = DepthAnything.from_pretrained('LiheYoung/depth_anything_{:}14'.
Depth Anything is also supported in [``transformers``](https://github.com/huggingface/transformers). You can use it for depth prediction within [3 lines of code](https://huggingface.co/docs/transformers/main/model_doc/depth_anything) (credit to [@niels](https://huggingface.co/nielsr)).
### No network connection, cannot load these models?
### *No network connection, cannot load these models?*
<details>
<summary>Click here for solutions</summary>
- First, please manually download our models (both config and checkpoints files) from here: [depth-anything-small](https://huggingface.co/LiheYoung/depth_anything_vits14), [depth-anything-base](https://huggingface.co/LiheYoung/depth_anything_vitb14), and [depth-anything-large](https://huggingface.co/LiheYoung/depth_anything_vitl14).
- First, manually download the three checkpoints: [depth-anything-large](https://huggingface.co/spaces/LiheYoung/Depth-Anything/blob/main/checkpoints/depth_anything_vitl14.pth), [depth-anything-base](https://huggingface.co/spaces/LiheYoung/Depth-Anything/blob/main/checkpoints/depth_anything_vitb14.pth), and [depth-anything-small](https://huggingface.co/spaces/LiheYoung/Depth-Anything/blob/main/checkpoints/depth_anything_vits14.pth).
- Second, upload the folder which contains config and checkpoint files to your remote server.
- Second, upload the folder containing the checkpoints to your remote server.
- Lastly, load the model locally by:
- Lastly, load the model locally:
```python
# suppose the config and checkpoint files are stored under the folder checkpoints/depth_anything_vitb14
depth_anything = DepthAnything.from_pretrained('checkpoints/depth_anything_vitb14', local_files_only=True)
```
from depth_anything.dpt import DepthAnything
model_configs = {
'vitl': {'encoder': 'vitl', 'features': 256, 'out_channels': [256, 512, 1024, 1024]},
'vitb': {'encoder': 'vitb', 'features': 128, 'out_channels': [96, 192, 384, 768]},
'vits': {'encoder': 'vits', 'features': 64, 'out_channels': [48, 96, 192, 384]}
}
encoder = 'vitl' # or 'vitb', 'vits'
depth_anything = DepthAnything.from_pretrained(model_configs[encoder])
depth_anything.load_state_dict(torch.load(f'./checkpoints/depth_anything_{encoder}14.pth'))
```
Note that in this locally loading manner, you also do not have to install the ``huggingface_hub`` package. In this way, please feel free to delete this [line](https://github.com/LiheYoung/Depth-Anything/blob/e7ef4b4b7a0afd8a05ce9564f04c1e5b68268516/depth_anything/dpt.py#L5) and the ``PyTorchModelHubMixin`` in this [line](https://github.com/LiheYoung/Depth-Anything/blob/e7ef4b4b7a0afd8a05ce9564f04c1e5b68268516/depth_anything/dpt.py#L169).
</details>
@@ -209,7 +219,7 @@ depth = pipe(image)["depth"]
## Community Support
**We sincerely appreciate all the extentions built on our Depth Anything from the community. Thank you a lot!**
**We sincerely appreciate all the extensions built on our Depth Anything from the community. Thank you a lot!**
Here we list the extensions we have found:
- Depth Anything TensorRT:
@@ -239,10 +249,10 @@ Besides, we thank the [MagicEdit](https://magic-edit.github.io/) team for provid
If you find this project useful, please consider citing:
```bibtex
@article{depthanything,
@inproceedings{depthanything,
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},
journal={arXiv:2401.10891},
booktitle={CVPR},
year={2024}
}
```