Comments (4)
You seem to be using Windows. We should clarify that the code does expect a Linux environment and has never been tested elsewhere. So the above recommendation to use instead WSL on Windows could indeed help although there is no guarantee this will actually work without more efforts.
Additionally, conda is the recommended way to install the right dependencies for the training and evaluation code. If you nevertherless want to use pip install
, AFAICT this command line should install the right packages (from an environment with just python 3.9):
pip install --extra-index-url https://download.pytorch.org/whl/cu117 --extra-index-url https://pypi.nvidia.com git+https://github.com/facebookresearch/dinov2
Or just:
pip install -r requirements.txt
to not even install the dinov2
package.
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use wsl, not windows
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Thank you,I think it's very effective@yosugahhh
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Thank you very much for your reminder. I was too careless to notice that the project needs to run on Linux. Thanks again. @patricklabatut
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Related Issues (20)
- Run vanilla DinoV2 training with unlabelled dataset to fit specific field data HOT 1
- why do Self-supervised image retrieval? HOT 1
- Can you release the weight of dino head and ibot head๏ผ HOT 2
- Resume training from intermediate checkpoint? HOT 1
- Why is total_iters unrelated to batch size and number of data? HOT 2
- ResNet50 architecture HOT 1
- Train depth / segmentation head HOT 1
- Implementation using ytorch lightning
- Drop path implementation
- Do black borders affect model performance? HOT 4
- run code at about [ 3170/125000], [W CudaIPCTypes.cpp:16] Producer process has been terminated before all shared CUDA tensors released. See Note [Sharing CUDA tensors]
- Memory-efficient attention forward operator error in Docker container with FastAPI and DINOv2 HOT 9
- torchrun --nproc_per_node=4 /data/zxb/DINO_FL/train/train.py HOT 2
- Fine-Tuning ViT backbone for Depth Estimation
- Upload 3D Ct Scans into Dinov2
- Patch to support torch version > 2.1 and running using torchrun
- Docker multi-architecture build issues for DINOv2 API on AWS and Mac
- Get visualization of the three first principal components HOT 1
- Loss doesn't go down.
- Is it possible to Use DINOv2 for Facial Recognition/Search HOT 1
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