Comments (2)
tzzcl
commented on June 9, 2024
Hi, Thank you for your interest in our project. We may not provide the convert code soon, but I can give you some instructions.
DETAD uses a standard evaluation process with GT json (they provided that in their repo) and predicted json, you only need to transfer the output to a valid json format.
With THUMOS14 dataset as an example, you first need to get the prediction output, you can use the saveonly args to get an output pickle file. The only thing is that you need to transfer the target pickle file to the specific json format. I think you only need to convert the class name.
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happyharrycn
commented on June 9, 2024
Mark as resolved.
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Related Issues (20)
- Plan to release Ego4d trained model HOT 2
- How to reproduce results using i3d for ActivityNet? HOT 8
- Question about such strong I3D feature HOT 6
- How to improve？ HOT 1
- Why are the reported results of TadTR on Thumos14 and ActivityNet quite different with those in TadTR paper? HOT 2
- about win_size HOT 2
- what is the h5py version?
- What is the test video number of Thumos14 dataset? HOT 2
- Visualization code HOT 3
- why set num_classes as 1 in ActivityNet dataset module? HOT 2
- About n_mha_win_size in eval. HOT 4
- Question about the choice of regression_range. HOT 3
- About extracting feature. HOT 2
- Question about regression ranges HOT 5
- import nms_1d_cpu ImportError HOT 4
- feat_offset HOT 2
- nms_1d_cpu HOT 2
- Measure GMACs/FLOPS HOT 2
- About thumos14 features using TSP HOT 6
- error HOT 2
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