{"id":22406505,"url":"https://github.com/saranshmanu/cifar-image-classification","last_synced_at":"2026-04-27T23:39:49.655Z","repository":{"id":76673056,"uuid":"135454838","full_name":"saranshmanu/CIFAR-Image-Classification","owner":"saranshmanu","description":"This project was to create a model that can classify and predict the object in the image from the open source dataset CIFAR 10. 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There are 50000 training images and 10000 test images. \n\nThe dataset is divided into five training batches and one test batch, each with 10000 images. The test batch contains exactly 1000 randomly-selected 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. Between them, the training batches contain exactly 5000 images from each class. \n\nHere are the classes in the dataset, as well as 10 random images from each:\nairplane\t\t\t\t\t\t\t\t\t\t\nautomobile\t\t\t\t\t\t\t\t\t\t\nbird\t\t\t\t\t\t\t\t\t\t\ncat\t\t\t\t\t\t\t\t\t\t\ndeer\t\t\t\t\t\t\t\t\t\t\ndog\t\t\t\t\t\t\t\t\t\t\nfrog\t\t\t\t\t\t\t\t\t\t\nhorse\t\t\t\t\t\t\t\t\t\t\nship\t\t\t\t\t\t\t\t\t\t\ntruck\t\t\t\t\t\t\t\t\t\t\n\nThe classes are completely mutually exclusive. There is no overlap between automobiles and trucks. \"Automobile\" includes sedans, SUVs, things of that sort. \"Truck\" includes only big trucks. Neither includes pickup trucks.\n\n## CIFAR 100 Dataset\nThis dataset is just like the CIFAR-10, except it has 100 classes containing 600 images each. There are 500 training images and 100 testing images per class. The 100 classes in the CIFAR-100 are grouped into 20 superclasses. Each image comes with a \"fine\" label (the class to which it belongs) and a \"coarse\" label (the superclass to which it belongs).\nHere is the list of classes in the CIFAR-100:\n\nSuperclass\tClasses\naquatic - mammals\tbeaver, dolphin, otter, seal, whale \u003cbr\u003e\nfish - aquarium fish, flatfish, ray, shark, trout \u003cbr\u003e\nflowers\t- orchids, poppies, roses, sunflowers, tulips \u003cbr\u003e\nfood containers\t- bottles, bowls, cans, cups, plates \u003cbr\u003e\nfruit and vegetables\t- apples, mushrooms, oranges, pears, sweet peppers \u003cbr\u003e\nhousehold electrical devices - clock, computer keyboard, lamp, telephone, television \u003cbr\u003e\nhousehold furniture\t- bed, chair, couch, table, wardrobe \u003cbr\u003e\ninsects\t- bee, beetle, butterfly, caterpillar, cockroach \u003cbr\u003e\nlarge carnivores\t- bear, leopard, lion, tiger, wolf \u003cbr\u003e\nlarge man-made outdoor things\t- bridge, castle, house, road, skyscraper \u003cbr\u003e\nlarge natural outdoor scenes\t- cloud, forest, mountain, plain, sea \u003cbr\u003e\nlarge omnivores and herbivores\t- camel, cattle, chimpanzee, elephant, kangaroo \u003cbr\u003e\nmedium-sized mammals\t- fox, porcupine, possum, raccoon, skunk \u003cbr\u003e\nnon-insect invertebrates\t- crab, lobster, snail, spider, worm \u003cbr\u003e\npeople\t- baby, boy, girl, man, woman \u003cbr\u003e\nreptiles\t- crocodile, dinosaur, lizard, snake, turtle \u003cbr\u003e\nsmall mammals\t- hamster, mouse, rabbit, shrew, squirrel \u003cbr\u003e\ntrees\t- maple, oak, palm, pine, willow \u003cbr\u003e\nvehicles 1\t- bicycle, bus, motorcycle, pickup truck, train \u003cbr\u003e\nvehicles 2\t- lawn-mower, rocket, streetcar, tank, tractor \u003cbr\u003e\n\nYes, I know mushrooms aren't really fruit or vegetables and bears aren't really carnivores. \n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsaranshmanu%2Fcifar-image-classification","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsaranshmanu%2Fcifar-image-classification","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsaranshmanu%2Fcifar-image-classification/lists"}