awesome-semantic-segmentation-pytorch
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Tramac
Semantic Segmentation on PyTorch (include FCN, PSPNet, Deeplabv3, Deeplabv3+, DANet, DenseASPP, BiSeNet, EncNet, DUNet, ICNet, ENet, OCNet, CCNet, PSANet, CGNet, ESPNet, LEDNet, DFANet)
About DFAnet
Did u test the inference speed of DFAnet? The result of my test by mxnet is over 40ms for one image(size:1024*1024), which is far away from the speed reported in...
No such file or directory: 'gtFine_trainvaltest.zip' does this need to download self?could you give a link? 1 -> gtFine_trainvaltest.zip (241MB) 2 -> gtCoarse.zip (1.3GB) 3 -> leftImg8bit_trainvaltest.zip (11GB) 4 ->...
Hi, when I train the model dfanet, it comes the error, at the core/models/dfanet.py line 77: fusion = fusion + stage1_fca_decoder + stage2_fca_decoder + stage3_fca_decoder RuntimeError: The size of tensor...
I want to use the dfanet, which need the xception_a.pth file Where can we download the checkpoint?
Is bilinear interpolation better than deconvolution in testing?
I think the your ICNet implementation is not accurate. First, there is no PPM in the head. Second, no shared computation. In the implementation, forward twice lead to slower speed....
(pytorch) G:\awesome-semantic-segmentation-pytorch-master\core\nn>python setup.py build develop running build running build_ext F:\Anaconda3\envs\pytorch\lib\site-packages\torch\utils\cpp_extension.py:184: UserWarning: Error checking compiler version for cl: [WinError 2] 系统找不到指定的文件。 warnings.warn('Error checking compiler version for {}: {}'.format(compiler, error)) building '._C'...
(pytorch) D:\awesome-semantic-segmentation-pytorch-master\core\nn>python setup.py build develop running build running build_ext D:\Anaconda3\envs\pytorch\lib\site-packages\torch\utils\cpp_extension.py:184: UserWarning: Error checking compiler version for cl: [WinError 2] 系统找不到指定的文件。 warnings.warn('Error checking compiler version for {}: {}'.format(compiler, error)) building '._C'...
Hi,thank you for your great job. I'm a new pytorch learner,I was wondering how to train mscoco dataset when model is fcn32s. The choice in '--dataset' in demo.py are ['pascal_voc/pascal_aug/ade20k/citys']....
