Very low testing mIoU

[Fansiee]

I train the model on VOC2012 segmentation, using 250 epoch and 20 batch_size. The training accu is 0.985~.(It should be the pixel accu, right?) But the testing mIoU is just 0.56025, which has a big gap with the result in the paper. What's your result? And maybe the hyperparameter is not optimal?

[ 0.9077976 0.75288631 0.47242709 0.58043887 0.50712961 0.47588975
  0.72664924 0.68814923 0.66110743 0.20972129 0.53752346 0.31540707
  0.5731418 0.50105971 0.64968748 0.76978092 0.43357291 0.5765887
  0.32090376 0.61638412 0.49687383]
meanIOU: 0.560625
pixel acc: 0.898940

[aurora95]

Which model did you use? And can you list your hyperparameters? The hyperparameters I left in the code were used for resnet. Also, how many training images are there in your dataset? The dataset used for those papers should include about 10k images for training.

[Fansiee]

My hyperparameters is same as your except the batch_size is 20. And the model is resnet50, same as in your code. I just changed the file path and the batch_size. The training images number is 1400+, I don't use the SegmentationClassAUG. I am training on the SegAug label right now. I will update my result later. And what's your result in the original Seg label and the SegAug label?

[Fansiee]

Update: I run the model in SegmentationAug data using 25 epoch and 32 batch_size, and keeping other hyperparameters unchangeable. The testing mIoU is 0.661076.

It seems acceptable. And maybe I can boost it by training more epoches.

Could you please tell me your results? I want to compare with yours. Thanks.

[aurora95]

Hmm... Actually, I got 65.75 mIOU using 25 epoch and 32 batch size. Your result is slightly better than mine...

[Fansiee]

I run more 25 epoches based on the 0.661076 model, but the result is almost the same. BTW, thank you for your code of the FCN in keras, its very helpful for me!

[aurora95]

@Fansiee Glad to help :)

[ahundt]

@Fansiee Aren't the classes a bit different in the augmented pascal VOC?

I assume you're discussing these:

    # original PASCAL VOC 2012
    # wget http://host.robots.ox.ac.uk/pascal/VOC/voc2012/VOCtrainval_11-May-2012.tar # 2 GB
    # berkeley augmented Pascal VOC
    # wget http://www.eecs.berkeley.edu/Research/Projects/CS/vision/grouping/semantic_contours/benchmark.tgz # 1.3 GB

Could you perhaps create a pull request with your code changes to run what you used?

[aurora95]

@ahundt I think your link is correct and the classes should be the same. Just add all augmented data to your training set and change the hyper parameters, and you should be able to reproduce the result.

[JeremyFxx]

Hi, @aurora95 @ahundt I also got a low testing mIoU with all the default setting in train.py using AtrousFCN_Resnet50_16s.

IOU: [0.88228786 0.80926575 0.35438381 0.65577184 0.4825115 0.37554799 0.70274595 0.68739528 0.65441523 0.25122642 0.55061205 0.29527007 0.58391853 0.56140901 0.68124345 0.70453396 0.37578266 0.61919163 0.36438029 0.70442827 0.61359128] meanIOU: 0.567139 pixel acc: 0.885961

I noticed that there were two issues have been discussed:

1. SegmentationClassAUG, namely the berkeley augmented Pascal VOC should be utilized;
2. the batch should be changed to 32 instead of 16, and the training epochs should be changed to 25 instead of 250.

Now I have the following question:

1. Following the instruction, I used @ahundt 's tf-image-segmentation to prepare the datasets by running the _python data_pascal_voc.py pascal_vocsetup. And in the train.py, the default setting for the dataset _dataset = 'VOC2012BERKELEY' is remained. Do these settings ensure that I am using the berkeley augmented Pascal VOC?

2. I tried to increase the batch size to 32. However, the memory error occurred. What type of GPU are you using for the training? Did you use multi-gpu?

3. The default learning rate scheduler is _'powerdecay', with the _lr_base = 0.01 * (float(batchsize) / 16). Should I change any of these settings?

[Tyler-D]

@JeremyFxx Similar low mean IOU here (~58%). And I've adapted the repo to Keras 2.2.4 ---- not sure if this is the reason for lower mIOU.