Comments (3)
Not sure if this is resolved. Could you please try backtracking through the code to see what is causing the problem? I do not recall getting NaNs in mAP.
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Hi, have you solved this issue? I encountered the same problem, particularly, the detection IoU and training loss became nan after about 1000 iterations. And I used the following parameter to run:
--dataset-name ActivityNet1.2 --num-class 100 --max-seqlen 3000 --model-name anet1.2 --batch-size 16
from wtalc-pytorch.
hi, when I train on ActivityNet v1.2. the I got mIOU is nan.
how to solve the problem?
Hi, I solved this problem. This is because thumos14 used validation set to train, while activitynet used training set to train. So you have to change the subset name in detectionMAP.py (maybe some other files).
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Related Issues (20)
- The model results vary greatly HOT 1
- I believe labels_all.npy is wrong HOT 1
- About Qualitative Results
- difference between labels.npy and labels_all.npy HOT 1
- Both Classification and Localization Performance is higher in my experiment than your eccv18 paper in Thumos14 HOT 4
- Specific parameter setting when training on activityNet v1.2 HOT 5
- How can i extract the feature by myself? HOT 1
- RuntimeError: The size of tensor a (101) must match the size of tensor b (20) at non-singleton dimension 1
- Visualization
- FPS of ActivityNet?
- Could you provide the pre-trained encoder?
- Regarding feature extraction HOT 2
- Can you share the UntrimmedNet feature for ActivityNet1.2
- regarding result
- Specific composition of features HOT 1
- calculate features HOT 13
- features download HOT 4
- overfitting HOT 1
- extracted_fps of Thumos14reduced-I3D-JOINTFeatures HOT 3
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