Thanks for sharing a good job! I have a question. When I train cityscapes using 1/8 labeled data, two models(init from coco and imagenet) can reach nearly 59 mIOU in val set, close to 59.65 presented in the paper. However, after 5 iterations, the metric descends to 53(coco) and 22(imagenet). I check the pseudo label using the model of 59 mIOU and it is not particularly good. I don't know if that affected the results.

Hello, your paper and code are very good, thank you for your efforts. Now I have a question to ask you, the details are as follows: First of all, I conducted experiments on my own data, and the results have been obtained. How can I use these weights to test test sets? In addition, I used dmT-VOC-20-1__p5 -- I , and use the training model to test, the effect is very poor, I do not know when the test method is correct.

您好，您的论文和代码非常棒，感谢您的付出。现在我有个问题想请教您，具体如下：首先我是在我自己的数据上进行实验，且已经跑出结果。我如何能够用这些权值来测试测试集？此外，我使用了dmt-voc-20-1__p5--i的权重，并利用训练的模型来进行测试，效果很差，我不知道测试方法时候正确。

Hello, i'm using a segmentation dataset with two classes and grayscale images. I'm duplicating the channels of the image with elif pic.mode == 'L': img = torch.from_numpy(np.array(pic, np.uint8, copy=False)).expand([3, 224, 224]).reshape(-1) While training the baseline without DMT i get only accuracy values for one of the classes with nan values for the other: average row correct: ['99.52', 'nan'].

Do you have any idea what i'd might have done wrong/missed? Thanks in advance!

When I was using part of your code about cityscapes benchmark, I met up with the error that

IndexError: Caught IndexError in DataLoader worker process 0. Original Traceback (most recent call last): File "/usr/local/lib/python3.6/dist-packages/torch/utils/data/_utils/worker.py", line 185, in _worker_loop data = fetcher.fetch(index) File "/usr/local/lib/python3.6/dist-packages/torch/utils/data/_utils/fetch.py", line 44, in fetch data = [self.dataset[idx] for idx in possibly_batched_index] File "/usr/local/lib/python3.6/dist-packages/torch/utils/data/_utils/fetch.py", line 44, in data = [self.dataset[idx] for idx in possibly_batched_index] File "../utils/datasets.py", line 155, in getitem img1, target1 = self.transforms(img, target) File "../utils/transforms.py", line 27, in call image, target = t(image, target) File "../utils/transforms.py", line 216, in call target = target if type(target) == str else self.label_id_map[target] IndexError: index 255 is out of bounds for dimension 0 with size 34

It seems that the LabelMap(label_id_map_city), didn't work correctly. It's the first time to using this benchmark, so I dont know how to deal with this problem, could you plz give me some hints?

First of all, thank you very much for your previous help. Now I can train on my own data set, but now I have another problem. I want to convert the output of the network into a mask file like the given label. I want to know how this should be How to do it, can you help me? Can the output use softmax and then set the threshold to generate the final mask?

I would like to ask if you have used a better model for experimentation, such as deeplab V3+. Will it bring better accuracy if you use a better model?

Thanks for your hard work!

I am new to this question. Can you explain the meaning of splits in generate_splits.py, like setting [2, 4, 8, 20, 29.75] for cityscapes? I only know that it means the ratio of labeld data and unlabeled data and really don't know why you set those values. Furthermore, if I want to train it on my own data, how can I set this variable according to the ratio of my labeled data and unlabeled data?

Thank you for your help.

Hello, I want to ask why the accuracy has suddenly dropped, and the accuracy of my reproduced article is much lower than that of the original text. I use a single 3090ti graphics card for training.

Hello ! When I match my dataset to the cityscapes, it does not work in the model initialization phase. RuntimeError: Error(s) in loading state_dict for DeepLab: size mismatch for classifier.0.convs.0.weight: copying a param with shape torch.Size([19, 2048, 3, 3]) from checkpoint, the shape in current model is torch.Size([4, 2048, 3, 3]).

My dataset contains only 5% labeled images. The size is 2048*1024，which is the same as the cityscapes. Could you help me find the probelm?

Thank you very much!

Warning: multi_tensor_applier fused unscale kernel is unavailable, possibly because apex was installed without --cuda_ext --cpp_ext. Using Python fallback. Original ImportError was: ModuleNotFoundError("No module named 'amp_C'",)

The version of pytorch I installed is 1.2.0 and the version of torchvision is 0.4.0，and the version of apex is 0.1

Thanks for your nice work and congratulations on your good results!

I have several questions.

• Will your model extended to Parallel (distributed data-parallel) in the future.
• Why don't you try to use deeplabv3+, will it lead to a better result?

Best.