What does the following code mean?img[0,:,:]=img[0,:,:]-92.8207477031

[CheungYooo]

img[0,:,:]=img[0,:,:]-92.8207477031 img[1,:,:]=img[1,:,:]-95.2757037428 img[2,:,:]=img[2,:,:]-104.877445883

@leeyeehoo Thank you for your sharing. When you validate CSRNet with 'val.ipynb',you use the code above.So,my question is why you minus the specific values above(92.8207477031,95.2757037428,104.877445883).What is the mean of the values?Why can't they be other values?

[wzhings]

Do you understand it now? I also get confused about these values.

[Cli98]

you should not use those value. As the author uses pretrain weights instead of training from zero. However, if you train from zero, you need those values then.

[Muhibullah1]

it seems he is taking mean pixel values across each channel (R, B, G) and then subtract them for the sake of normalization

[baosongliang]

@CheungYooo I think you should use following code instead:

`from torchvision import datasets, transforms transform=transforms.Compose([ transforms.ToTensor(),transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), ])

mae = 0 for i in range(len(img_paths)): img = transform(Image.open(img_paths[i]).convert('RGB')).cuda() gt_file = h5py.File(img_paths[i].replace('.jpg','.h5').replace('images','ground_truth'),'r') groundtruth = np.asarray(gt_file['density']) output = model(img.unsqueeze(0)) mae += abs(output.detach().cpu().sum().numpy()-np.sum(groundtruth)) print(i,mae) print (mae/len(img_paths))`