![]() 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): As compare to the result of average method, this image is more brighter.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', ![]() New grayscale image = ( (0.3 * R) + (0.59 * G) + (0.11 * B) ).Īccording to this equation, Red has contribute 30%, Green has contributed 59% which is greater in all three colors and Blue has contributed 11%.Īpplying this equation to the image, we get thisĪs you can see here, that the image has now been properly converted to grayscale using weighted method. It means that we have to decrease the contribution of red color, and increase the contribution of the green color, and put blue color contribution in between these two. Since red color has more wavelength of all the three colors, and green is the color that has not only less wavelength then red color but also green is the color that gives more soothing effect to the eyes. Weighted method has a solution to that problem. You have seen the problem that occur in the average method. The solution to this has been given by luminosity method. We are taking 33% of each, that means, each of the portion has same contribution in the image. Since the three different colors have three different wavelength and have their own contribution in the formation of image, so we have to take average according to their contribution, not done it averagely using average method. This problem arise due to the fact, that we take average of the three colors. We wanted to convert the image into a grayscale, but this turned out to be a rather black image. There is one thing to be sure, that something happens to the original works. If you have an color image like the image shown above and you want to convert it into grayscale using average method. Since its an RGB image, so it means that you have add r with g with b and then divide it by 3 to get your desired grayscale image. You just have to take the average of three colors. The methods are:Īverage method is the most simple one. Now we will convert an color image into a grayscale image. We have already define the RGB color model and gray scale format in our tutorial of Image types.
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