Comments (4)
The model was trained on 2 Quadro RTX 6000s for about 6 days: 1 day for pre-training on RedWeb + about 5 days for training on MIX5. Multi-GPU with low batch size was not an issue, because we froze all batch norm layers.
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@ranftlr thanks for your quick responce. I am training MidasNet on 2 tesla v100 gpus, but it's much slower. So the mean and val of bn layers is not updated during the training process?
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Correct, mean and variance are fixed and are not updated. I can't comment on the speed on a v100 as I don't have any available.
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@Tord-Zhang
I am reproducing the training processing on one V100. I am pretty new to this. I wonder what's the amount of data and how long the training takes?
Thanks
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Related Issues (20)
- del
- Depth value problem
- AttributeError: 'Block' object has no attribute 'drop_path' HOT 3
- An error occurred when I tried to run the make_onnx_model.py to convert MiDas 3.1 to onnx HOT 1
- issue converting model dpt_beit_large_512 to onnx HOT 1
- ROS1 Packages Build Issue
- Error loading model with timm==0.9.7 HOT 1
- Tuple error, as of today HOT 2
- post training quantization HOT 2
- module 'cv2.cv2' has no attribute 'COLORMAP_INFERNO'
- What is the loss function?
- Copy of MiDaS model made with `copy.deepcopy` does not work. HOT 1
- swin2_tiny failed to run forward(): RuntimeError: unflatten: Provided sizes [64, 64] don't multiply up to the size of dim 2 (64) in the input tensor. HOT 1
- [question] Any suggestions on normalizing the outputs better? HOT 9
- Error Loading Pre-trained Weights: Size Mismatch in DPTDepthModel when trying to run for first time. HOT 7
- System crash when loading DPT_Hybrid
- PyTorch Pipeline Broken HOT 2
- gibberish output? HOT 4
- DPT 3.1 models are now available in the Transformers library HOT 1
- Question about COCO dataset HOT 1
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