Details about baseline resnet-101 in paper

[zhifanlight]

Hi, thanks for your excellent work! But I'm confused of the detail about baseline-model settings in your paper.

Take training resnet-101 without cutmix on coco2014 as an example:

With the following training configurations as baseline setting, I get 81.3 mAP after 7 epochs (30 in total, still in training process...), which is much higher than that in your paper (79.4 mAP). python main.py --num_heads 4 --lam 0 --dataset coco --num_cls 80 --checkpoint coco14/resnet101

So, what is the correct settings to reproduce the baseline result as in your paper? Thanks again.

[Kevinz-code]

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.