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https://github.com/saranshmanu/cifar-image-classification

This project was to create a model that can classify and predict the object in the image from the open source dataset CIFAR 10. The dataset is divided into five training batches and one test batch, each with 10000 images.
https://github.com/saranshmanu/cifar-image-classification

cifar-10 cnn convolutional-neural-networks deep-learning keras keras-neural-networks machine-learning python

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This project was to create a model that can classify and predict the object in the image from the open source dataset CIFAR 10. The dataset is divided into five training batches and one test batch, each with 10000 images.

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README 

        
# CIFAR-Image-Classification

## CIFAR 10 Dataset

The CIFAR-10 dataset consists of 60000 32x32 colour images in 10 classes, with 6000 images per class. There are 50000 training images and 10000 test images. 



The 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. 



Here are the classes in the dataset, as well as 10 random images from each:

airplane										

automobile										

bird										

cat										

deer										

dog										

frog										

horse										

ship										

truck										



The 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.



## CIFAR 100 Dataset

This 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).

Here is the list of classes in the CIFAR-100:



Superclass	Classes

aquatic - mammals	beaver, dolphin, otter, seal, whale 


fish - aquarium fish, flatfish, ray, shark, trout 


flowers	- orchids, poppies, roses, sunflowers, tulips 


food containers	- bottles, bowls, cans, cups, plates 


fruit and vegetables	- apples, mushrooms, oranges, pears, sweet peppers 


household electrical devices - clock, computer keyboard, lamp, telephone, television 


household furniture	- bed, chair, couch, table, wardrobe 


insects	- bee, beetle, butterfly, caterpillar, cockroach 


large carnivores	- bear, leopard, lion, tiger, wolf 


large man-made outdoor things	- bridge, castle, house, road, skyscraper 


large natural outdoor scenes	- cloud, forest, mountain, plain, sea 


large omnivores and herbivores	- camel, cattle, chimpanzee, elephant, kangaroo 


medium-sized mammals	- fox, porcupine, possum, raccoon, skunk 


non-insect invertebrates	- crab, lobster, snail, spider, worm 


people	- baby, boy, girl, man, woman 


reptiles	- crocodile, dinosaur, lizard, snake, turtle 


small mammals	- hamster, mouse, rabbit, shrew, squirrel 


trees	- maple, oak, palm, pine, willow 


vehicles 1	- bicycle, bus, motorcycle, pickup truck, train 


vehicles 2	- lawn-mower, rocket, streetcar, tank, tractor 



Yes, I know mushrooms aren't really fruit or vegetables and bears aren't really carnivores.