![]() “Automobile” includes sedans, SUVs, things of that sort. There is no overlap between automobiles and trucks. The classes are completely mutually exclusive. Between them, the training batches contain exactly 5000 images from each class. The training batches contain the remaining images in random order, but some training batches may contain more images from one class than another. The test batch contains exactly 1000 randomly-selected images from each class. ![]() The dataset is divided into five training batches and one test batch, each with 10000 images. There are 50000 training images and 10000 test images. The CIFAR-10 dataset consists of 60000 32x32 colour images in 10 classes, with 6000 images per class. ![]() What are our model’s weaknesses and how might they be improved?.Building CNN on CIFAR-10 dataset using PyTorch: 1
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