Any one can reproduce pspnet result?

[sanweiliti]

I can only achieve a mIoU of ~ 52% using the trained model of PSPNet on the val set even with the full resolution on cityscapes with this psp model, which is far away from the reported result. I didn't train, just validate on the trained pspnet101_cityscapes.caffemodel published by the team of PSPNet, and it should directly get the reported result. Any one reproduce the result successfully?

[adam9500370]

Hi, @sanweiliti . You can do the followings:

• Download the converted Caffe pretrained weights here
• Set img_norm=False and version="pascal" arguments in data_loader (due to data preprocessing of original Caffe implementation)
• Replace ptsemseg/models/pspnet.py#L134 with x = F.interpolate(x, size=inp_shape, mode='bilinear', align_corners=True)
• Set the corresponding settings in your config file (e.g., img_rows: 1025, img_cols: 2049, resume: pspnet_101_cityscapes.pth)

You will get 78.65/96.31 (mIoU/pAcc) on the Cityscapes validation set.

[sanweiliti]

Hi @adam9500370 Finally get the result! Turns out to forget the data loader version to 'pascal' earlier.

[fido20160817]

Hi, @sanweiliti . You can do the followings:

• Download the converted Caffe pretrained weights here
• Set img_norm=False and version="pascal" arguments in data_loader (due to data preprocessing of original Caffe implementation)
• Replace ptsemseg/models/pspnet.py#L134 with x = F.interpolate(x, size=inp_shape, mode='bilinear', align_corners=True)
• Set the corresponding settings in your config file (e.g., img_rows: 1025, img_cols: 2049, resume: pspnet_101_cityscapes.pth)

You will get 78.65/96.31 (mIoU/pAcc) on the Cityscapes validation set.

This is my results by running validate.py after changing things according to those mentioned above:

Overall Acc: 0.8243936553782907 Mean Acc : 0.46220855109731096 FreqW Acc : 0.7202854165179821 Mean IoU : 0.36694185899912246

any tips? @adam9500370

[hjhjb]

我已收到，谢谢

[fido20160817]

and Overall Acc: 0.9424106973275609 Mean Acc : 0.7539844599183159 FreqW Acc : 0.895777388036502 Mean IoU : 0.676881206358246 after setting img_rows: 713, img_cols: 713.

[fido20160817]

I finally got normal results at https://github.com/hszhao/semseg after testing multiple codes. The key is to transform the gt labels rightly (for training and evaluation). for cityscapes: https://github.com/fyu/drn/tree/master/datasets/cityscapes. (you can transform gt labels ahead) for ade20k: https://github.com/CSAILVision/semantic-segmentation-pytorch/blob/master/mit_semseg/dataset.py#L70 (you can transform gt inside the code, not ahead training or evaluaiton)

def segm_transform(self, segm):
        # to tensor, -1 to 149
        segm = torch.from_numpy(np.array(segm)).long() - 1
        return segm

I have to calculate mIoU/mAcc/allAcc for my task, but semantic segmentation not my research, so it really takes me some time to calculate these metrics. Hope this is helpful to others.