halbielee / sec_pytorch Goto Github PK

I don't know why the model after being trained in train.script always generates all-0 value maps, but I test the best model you provide shows the result is right...

First of all, Thank you for your hard work. I am familiar with Pytorch, so your work is very helpful.
I am curious about the condition that you got the performance (mIoU 50.6049 and mIoU 49.6978) you mentioned. When I download the weight and evaluate it, I get the same result as the performance you mentioned.

meanIOU : 50.6049

background : 82.1013
aeroplane : 59.3787
bicycle : 25.7055
bird : 61.6386
boat : 26.7922
bottle : 40.3784
bus : 66.6121
car : 63.3243
cat : 75.6848
chair : 22.2849
cow : 54.4826
diningtable : 28.8335
dog : 66.1762
horse : 57.7841
motorbike : 63.0711
person : 53.3926
pottedplant : 32.1166
sheep : 61.7726
sofa : 32.0557
train : 44.8119
tvmonitor : 44.3050

However, if I try to train from scratch, I cannot get the same result. This is the result of testing by saving the checkpoint when the loss is minimum without changing the hyperparameter setting.

meanIOU : 10.8535

background : 74.5545
aeroplane : 12.2275
bicycle : 0.0005
bird : 0.0000
boat : 1.5335
bottle : 0.1878
bus : 0.6897
car : 0.0151
cat : 8.4732

The learning loss is shown below. Even if I change the hyperparameters, the sum of losses converges to 6.xx (seed loss 2.xx, expand loss 4.xx, constrain loss 0.5xx). It seems that training is not working in my case.

What I'm curious about is your learning loss decreasing well? What is the saving checkpoint criteria? (minimal loss or something?)

_

Can you tell me how do you get the localization_cues.pickle.gz of COCO ? Thanks.

After I run test_multiprocess.py, the generated pred_images are all black. What is the reason for this?

As mentioned above.