Comments (4)
Note that the classification performance you obtain when using Thumos14reduced is on the reduced dataset (200 data points for 20 categories). Whereas the numbers mentioned in paper is for the entire dataset, which is Thumos14. It is true that the localization performance obtained using this code is a bit higher than reported in the paper, and we obtained this after some code cleaning.
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Hi sujoyp,
Thanks for your answer. I have another question if I may ask. Is there any reason you write the evaluation code on your own rather than using the official one simply? Have you tested on the official thumos14 matlab code? How is the performance? I am quite curious about it and too lazy to check it on my own. Thanks again!
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We did try the official evaluation repo (which is in Matlab) and found the same results as our python implementation.
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Hi @sujoyp, I see! Thanks for it!
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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
- 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
- training on ActivityNet v1.2 HOT 3
- overfitting HOT 1
- extracted_fps of Thumos14reduced-I3D-JOINTFeatures HOT 3
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