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DeepLab v3+ model in PyTorch. Support different backbones.

Results 111 pytorch-deeplab-xception issues
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When I train my coco-style dataset by use : bash train_coco.sh then the errors as follows: Namespace(backbone='resnet', base_size=513, batch_size=4, checkname='deeplab-resnet', crop_size=513, cuda=True, dataset='coco', epochs=10, eval_interval=1, freeze_bn=False, ft=False, gpu_ids=[0], loss_type='ce', lr=0.01,...

I've tried both xception and resnet-101 backbone. The resent-101 backbone is working fine but when using xception backbone, results are very bad. And even, training for longer making the resulting...

refactoring code with var unpack which is more pythonic, concise, readable and efficient

I wonder about the overfitting problem when training on Cityscapes and why it happened. How can I solve this problem? Thanks! ![image](https://user-images.githubusercontent.com/15827612/76694351-8fba9800-66ac-11ea-87cd-f47774554ad9.png)

Has anybody run the code efficient?

@stonessss yes, i write test code. 1. copy and overwrite. https://github.com/jaemin93/pytorch-deeplab-xception/blob/master/test.py https://github.com/jaemin93/pytorch-deeplab-xception/blob/master/dataloaders/__init__.py 2. add test.txt(you want to test img id) in VOC path /ImageSets/Segmentation test.txt is like val, train.txt 3....

File "E:\ProgramData\Anaconda3\envs\yolov5-master\lib\multiprocessing\popen_spawn_win32.py", line 89, in __init__ reduction.dump(process_obj, to_child) File "E:\ProgramData\Anaconda3\envs\yolov5-master\lib\multiprocessing\reduction.py", line 60, in dump ForkingPickler(file, protocol).dump(obj) TypeError: can't pickle module objects Traceback (most recent call last): File "E:\ProgramData\Anaconda3\envs\yolov5-master\lib\multiprocessing\spawn.py", line 115,...

Hi, I took the liberty of submitting the resnet pre-trained model results on VOC2012 TEST to the evaluation server. These are the results on the TEST set: Competition | Category...