Comments (4)
escort94
commented on May 23, 2024
Self answer :
"Epoch 199" finished train
Epoch: 198
Loss: 13.000350 Iterations: 99/1533 TrainExecTime: 99.4 ValLoss:12.821774 ValPSNR: 30.4979 ValEvalTime: 7.94 LearningRate: 0.000001
Loss: 13.356719 Iterations: 199/1533 TrainExecTime: 75.0 ValLoss:12.819451 ValPSNR: 30.5012 ValEvalTime: 7.92 LearningRate: 0.000001
Loss: 13.059358 Iterations: 299/1533 TrainExecTime: 74.9 ValLoss:12.819765 ValPSNR: 30.5010 ValEvalTime: 7.94 LearningRate: 0.000001
Loss: 13.048349 Iterations: 399/1533 TrainExecTime: 74.9 ValLoss:12.820006 ValPSNR: 30.5010 ValEvalTime: 7.91 LearningRate: 0.000001
Loss: 12.855513 Iterations: 499/1533 TrainExecTime: 74.9 ValLoss:12.817331 ValPSNR: 30.5020 ValEvalTime: 7.94 LearningRate: 0.000001
Loss: 13.217664 Iterations: 599/1533 TrainExecTime: 75.0 ValLoss:12.820163 ValPSNR: 30.5013 ValEvalTime: 7.92 LearningRate: 0.000001
Loss: 13.159157 Iterations: 699/1533 TrainExecTime: 74.9 ValLoss:12.822196 ValPSNR: 30.4989 ValEvalTime: 7.93 LearningRate: 0.000001
Loss: 13.104509 Iterations: 799/1533 TrainExecTime: 75.0 ValLoss:12.820226 ValPSNR: 30.5014 ValEvalTime: 7.97 LearningRate: 0.000001
Loss: 13.444480 Iterations: 899/1533 TrainExecTime: 75.0 ValLoss:12.820889 ValPSNR: 30.4989 ValEvalTime: 7.92 LearningRate: 0.000001
Loss: 13.407564 Iterations: 999/1533 TrainExecTime: 75.0 ValLoss:12.821120 ValPSNR: 30.4996 ValEvalTime: 7.91 LearningRate: 0.000001
Loss: 13.205854 Iterations: 1099/1533 TrainExecTime: 74.9 ValLoss:12.822172 ValPSNR: 30.4992 ValEvalTime: 7.94 LearningRate: 0.000001
Loss: 13.371440 Iterations: 1199/1533 TrainExecTime: 75.0 ValLoss:12.821109 ValPSNR: 30.4985 ValEvalTime: 7.97 LearningRate: 0.000001
Loss: 13.313439 Iterations: 1299/1533 TrainExecTime: 74.9 ValLoss:12.821505 ValPSNR: 30.4963 ValEvalTime: 7.96 LearningRate: 0.000001
Loss: 12.777627 Iterations: 1399/1533 TrainExecTime: 75.0 ValLoss:12.824670 ValPSNR: 30.4991 ValEvalTime: 7.93 LearningRate: 0.000001
Loss: 13.095275 Iterations: 1499/1533 TrainExecTime: 74.9 ValLoss:12.824434 ValPSNR: 30.4954 ValEvalTime: 7.93 LearningRate: 0.000001
Epoch: 199
Loss: 12.839095 Iterations: 99/1533 TrainExecTime: 99.5 ValLoss:12.823287 ValPSNR: 30.4983 ValEvalTime: 7.95 LearningRate: 0.000001
Loss: 13.009885 Iterations: 199/1533 TrainExecTime: 74.9 ValLoss:12.822529 ValPSNR: 30.5003 ValEvalTime: 7.92 LearningRate: 0.000001
Loss: 12.888221 Iterations: 299/1533 TrainExecTime: 75.0 ValLoss:12.820976 ValPSNR: 30.5024 ValEvalTime: 7.94 LearningRate: 0.000001
Loss: 13.258962 Iterations: 399/1533 TrainExecTime: 75.0 ValLoss:12.821937 ValPSNR: 30.5014 ValEvalTime: 7.91 LearningRate: 0.000001
Loss: 13.225136 Iterations: 499/1533 TrainExecTime: 74.9 ValLoss:12.822439 ValPSNR: 30.5010 ValEvalTime: 7.94 LearningRate: 0.000001
Loss: 13.328113 Iterations: 599/1533 TrainExecTime: 74.9 ValLoss:12.821028 ValPSNR: 30.4995 ValEvalTime: 7.92 LearningRate: 0.000001
Loss: 13.903210 Iterations: 699/1533 TrainExecTime: 75.0 ValLoss:12.819799 ValPSNR: 30.5001 ValEvalTime: 7.93 LearningRate: 0.000001
Loss: 13.608873 Iterations: 799/1533 TrainExecTime: 75.0 ValLoss:12.823103 ValPSNR: 30.5001 ValEvalTime: 7.97 LearningRate: 0.000001
Loss: 13.383028 Iterations: 899/1533 TrainExecTime: 74.9 ValLoss:12.822050 ValPSNR: 30.4996 ValEvalTime: 7.95 LearningRate: 0.000001
Loss: 13.163999 Iterations: 999/1533 TrainExecTime: 75.1 ValLoss:12.822468 ValPSNR: 30.5001 ValEvalTime: 7.95 LearningRate: 0.000001
Loss: 13.089723 Iterations: 1099/1533 TrainExecTime: 74.9 ValLoss:12.822032 ValPSNR: 30.4987 ValEvalTime: 7.97 LearningRate: 0.000001
Loss: 12.768606 Iterations: 1199/1533 TrainExecTime: 74.9 ValLoss:12.821181 ValPSNR: 30.4998 ValEvalTime: 7.93 LearningRate: 0.000001
Loss: 12.896706 Iterations: 1299/1533 TrainExecTime: 74.9 ValLoss:12.821431 ValPSNR: 30.5002 ValEvalTime: 7.94 LearningRate: 0.000001
Loss: 12.946524 Iterations: 1399/1533 TrainExecTime: 74.9 ValLoss:12.822370 ValPSNR: 30.5009 ValEvalTime: 7.93 LearningRate: 0.000001
Loss: 13.589174 Iterations: 1499/1533 TrainExecTime: 75.1 ValLoss:12.821481 ValPSNR: 30.5010 ValEvalTime: 7.93 LearningRate: 0.000001
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tingxueronghua
commented on May 23, 2024
excuse me please, but what I got behind the "Iterations" was only 162, would you have any idea about that?
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tingxueronghua
commented on May 23, 2024
ummm, I really want to know whether you use the videos provided to make the dataset?
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avinashpaliwal
commented on May 23, 2024
ummm, I really want to know whether you use the videos provided to make the dataset?
I didn't understand your question. Can you elaborate more?
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Related Issues (20)
- ffmpeg error while running evaluation code. HOT 1
- Output interpolated image frames are of different size then input frames?
- What is the function of transforms.Normalize(mean=negmean, std=std)? Why use a negative mean๏ผ
- Error converting file:D:\Coding\JAVASCRIPT\recless\out\4fpsRecording.mkv. Exiting.
- PackagesNotFoundError: torchvision-cpu==0.2.0 HOT 3
- Runtime Error (Traceback Attached) HOT 23
- Frames are not extracted for any input video
- Differences with butterflow
- Slowdown a point of the video HOT 1
- It does not seem to work for scale value below 4 HOT 1
- "index 1 is out of bounds for dimension 0 with size 1" HOT 2
- Reference frames are different from the original ones. HOT 1
- sh: /Users/*/Downloads/ffmpeg/ffmpeg: No such file or directory HOT 2
- Add local file to Google Colab?? Or change length??
- How can I find the dataset of 240-fps video from YouTube HOT 1
- Training
- Objective metrics
- Could be faster by not saving PNG frames
- Image edge flicker
- low rate of utilization in test video
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