Image_Classification_Model_with_Machine_Learning

An image classification model is trained to recognize various classes of images. For example, a model might be trained to recognize photos representing three different types of animals and objects like: Horses Frogs Dogs Aeroplanes Ships etc.

Inputs:

The input to the model is the .jpg or .jpeg format image.

Processing:

The model processes the input image and convert it into small pixeled image which is further compared with the characterstics of the previous images (that were used for training of model) and then the result is drawn.

Output:

The model output is the probability. The model predicts the output on the basis of comaparisions with the trained data.

Dataset:

Data set of CIFAR-10 has been used for training the model.

Click below to download data set:

https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz