Comments (5)
Hi,
- The program use
progress
to dynamically print logs to the screen. If it does not work, you can set--print_iters 1
to use the standard python print function. The log, as well as a tensorboard log, is also saved toexp/ctdet/$exp_id
. - The learning rate 5e-4 is set for batchsize 128, you will need to linearly scale it to your batchsize (in your case,
5e-4 * 47 / 128
). - It is recommended to test with fewer epochs first, e.g.,
--num_epochs 70 --lr_step 60
. - The "best" model (with the lowest loss) is usually not the best one with the highest AP.
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@xingyizhou thank you.
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@xingyizhou The learning rate 5e-4 is set for batchsize 128 on 8 GPUs (batchsize 16 on each GPU). If I use 2 GPUs and batchsize is 40, should I set lr = 5e-4 * (40 / 2) / (128 / 8)
?
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Anybody knows why?
The "best" model (with the lowest loss) is usually not the best one with the highest AP.
Many thanks!
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the program runs using nohup, and i can not see the loss and progress.
I can see the loss with this cmd:
nohup python main.py ctdet --exp_id coco_dla --batch_size 16 --master_batch 1 --lr 6.25e-5 -
-gpus 4,0 --print_iter 1 --num_workers 0 >../trian_log.json 2>&1 &
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