Comments (1)
Hi, thank you for reproducing our work and the baseline methods.
When we run baseline method on COCO, the learning rate and other configs follow previous multi-label work, e.g. ML-GCN, MS-CMA. Specifically, the learning rate for backbone and fc are 0.01 (also mentioned in our paper), step_size is 15, and we train about 40 epochs. You can try using this settiing for reproducing baseline (can be some variance in terms of the results , but not too high).
When we use our method, since CSRA is a special module, we enlarge fc's learning rate to 0.1 for faster convergence, and step size and total epochs have been shortened consequently.
Best,
Authors.
from csra.
Related Issues (20)
- some questions about val.py HOT 1
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