Comments (2)
deqings
commented on August 23, 2024
Thanks for your interest in our work.
The training is for FlyingChairs only. Are you using a HDD? We used SSD to store the LMDB file, which is much faster than HDD.
You can check the details on fine-tuning in this extended version:
Models Matter, So Does Training: An Empirical Study of CNNs for Optical Flow Estimation
https://arxiv.org/pdf/1809.05571.pdf
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rajat95
commented on August 23, 2024
Hi, When you refer to learning rate disruption in the extended paper, does that mean you keep learning rate very close to 0 for some iterations before increasing it again?
from pwc-net.
Related Issues (20)
- correlation-pytorch torch>=1.0 HOT 3
- Performance of pwc_net_chair.pth.tar on FlyingThings3D dataset
- How to understand the values 0.625, 1.25, 2.5, 5.0 in the PWCNet.py HOT 6
- How to realize the last item of loss
- Correlation - Python Implementation HOT 2
- Why mask is needed in warp function? HOT 1
- Question about large difference warping HOT 1
- forward warp
- about autoflow HOT 1
- Is there a cpu implementation of correlation module
- Disabling cost volume HOT 1
- Inference time on pytorch version HOT 6
- Running on Docker with the system requirements. HOT 1
- training data augmentations
- Optical flow for point cloud extraction
- Incorrect dimension when input image has shape `480 * 640` HOT 3
- Average EPE score with PyTorch implementation
- Trying to install dependencies using conda on Windows HOT 1
- Not Using the First Feature (c11 and c21) for the flow estimation; Upsampling not present in the shallowest flow estimation HOT 1
- CUDA error: the provided PTX was compiled with an unsupported toolchain.
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