![]() L = R * 299/1000 + G * 587/1000 + B * 114/1000īy iterating through each pixel you can convert 24-bit to 8-bit or 3 channel to 1 channel for each pixel by using the formula above. ITU-R 601 7th Edition Construction of Luminance formula: One of the standards that can be used is Recommendation 601 from ITU-R (Radiocommunication Sector of International Telecommunication Union or ITU) organization which is also used by pillow library while converting color images to grayscale. So, how do we achieve one value from those three pixel values? We need some kind of averaging. L mode on the other hand only uses one value between 0-255 for each pixel (8-bit). In summary, color images usually use the RGB format which means every pixel is represented by a tuple of three value (red, green and blue) in Python. Inputs, labels = data.to(device), data.There are different image hashes that can be used to transform color images to grayscale. Optimizer = optim.SGD((), lr=0.001, momentum=0.9)ĭevice = vice(“cuda:0” if _available() else “cpu”)įor epoch in range(2): # loop over the dataset multiple timesįor i, data in enumerate(trainloader, 0): ![]() # col_names=, # uncomment for smaller outputĬol_names=,įor name, child in alexnet.named_children():įor param in ():Īlexnet.classifier = nn.Linear(4096,10) Input_size=(4, 3, 227, 227), # make sure this is “input_size”, not “input_shape” #plt.imshow(np.transpose(npimg, (1, 2, 0)))Īlexnet = models.alexnet(pretrained=True) # This will download the weights for the network first time it is run! Print(‘Class labels of 10 examples:’, labels) ![]() Print(‘Image label dimensions:’, labels.shape) Print(‘Image batch dimensions:’, images.shape) Testloader = (test_data, batch_size=4, shuffle=False) Trainloader = (train_data, batch_size=4, shuffle=True) Transforms.Grayscale(num_output_channels=3), The posted screenshot also doesn’t represent your code as I see: model = models.alexnet() Could you describe what “snap” is why you are not expecting 10 output features even though you explicitly replace the last linear layer with out_features=10? Sorry, but I don’t fully understand this claim. ![]() This is the snap of my summary after frozen layers and after updated last layer I got 10 classes output in place of 10000. Print(’ ‘.join(’%5s’ % classes] for j in range(4))) Test_data = (root=‘./data’, train=False, download=True, transform=transform) Trainloader = (train_data, batch_size=4, shuffle=True, num_workers=2) Train_data = (root=‘./data’, train=True, download=True, transform=transform) How to convert it into rgb?įrom torchvision import transforms as transforms I have mnist dataset that is in pytorch API its grayscale and I want to implement transfer learning using Alexnet. Actually I discovered I also have images with four channels so I implemented this code in my custom dataset import osĭef _init_(self,csv_file,root_dir,transform=None): Hello ptrblck, Thanks for your quick response. But I don’t know how to do it or where exactly on my special code. Now I know I have to convert these grayscale images if I want to train…my question is where can I catch the grayscale images and convert them to rgb? In matlab would be something like rgbImage = cat(3, A,A, A) where A is the grayscale image. I didn’t know what ImageNet had grayscale images and I actually found some and read them on matlab and yes they are grayscale…that’s the reason Im getting the error of batch size mismatch at position 0. Test_loader = DataLoader(test_dataset, batch_size,num_workers=num_workers, Train_loader = DataLoader(train_dataset, batch_size,num_workers=num_workers, Test_dataset = TransformedDataset(test_dataset, partial(map_targets_fn, target_mapping=labels_mapping))įor idx, (data,image) in enumerate (train_dataset): Train_dataset = TransformedDataset(train_dataset, partial(map_targets_fn, target_mapping=labels_mapping)) Test_dataset=CustomDataset(csv_file='/home/tboonesifuentes/Databases/ImageNet/Test/test.csv',root_dir='/home/tboonesifuentes/Databases/ImageNet/Test/Crops',Ĭlass TransformedDataset():ĭef _init_(self, dataset, transform_fn): Hello, I am trying to classify ImageNet using vgg and I am using a custom dataset as follows train_dataset=CustomDataset(csv_file='/home/tboonesifuentes/Databases/ImageNet/Train/train.csv',root_dir='/home/tboonesifuentes/Databases/ImageNet/Train/Crops',
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