Comments (3)
re introduce the carbion emissions for the dataset creation
Compared to large pretrainings, the data curation is (overall) much lighter compute-wise and intermediate results are easier to re-use.
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okay, got it - sad since if one would like to do something/better he/she will be force to re introduce the carbion emissions for the dataset creation - thanks for being so open meta β€οΈ
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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 headοΌ 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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