low map issue...
xarryon opened this issue · comments
Hi, thank you for your interest.
Unfortunately, the training is not fully deterministic, so it might return low mAP sometimes. Can you please try re-training?
Yes, your training output looks good.
Can you please download and put this file into states
directory and run:
python src/evaluate.py --base_net vgg --state_path states/vgg_epoch_20.pt
Its output should be as follows:
Avg AP: [0.38831317 0.45263229 0.24562751 0.26572207 0.12294847 0.59961043
0.47957328 0.24194355 0.09629139 0.29096157 0.15507882 0.26510262
0.31636181 0.53462855 0.06115122 0.16652659 0.33271927 0.29181557
0.47336772 0.51627529]
Avg mAP: 0.3148325589240723
thanks author! The results I run nearly same as the one that you give.
So... it looks strange and I think something wrong in training.
Maybe the number of epoch is too small?
You are welcome! The pretrained state file I've sent is a result of 20 epochs training, so I don't think epoch count is a problem.
On the other hand, the training might stuck at a local minimum. Can you please try with the following:
python src/train.py --base_net vgg --lr 1e-4
thanks author! it can work!
Thank you too!