AbdallahOmarAhmed / cifar100-model

my first deep learning model, its a classification model to classify a photo throw 100 class with the famous dataset cifar100

cifar100-Model

My first deep learning model, it's a classification model to classify a photo throw 100 class with the famous dataset cifar100.

Requirements

Ubuntu " It's only tested on Ubuntu, so it may not work on Windows "

Python3 : https://www.python.org/downloads/

numpy : https://numpy.org/

PyTorch : https://pytorch.org/

torchvision : https://pypi.org/project/torchvision/

GPU : Any GPU that is work with PyTorch

DataSet : You should download the cifar100 dataset : https://www.cs.toronto.edu/~kriz/cifar.html

Model

• Hayper parametars

  starting learning rate = 0.01
  
  number of epochs = 200
  
  batch size = 1000
  

• Optimaizer

  adam optimaizer
  

• Schedular

  step size = 20
  
  gamma = 0.8
  

• Loss function

  cross entropy loss
  

• Model structure

  1.conv layer ( input_channels = 3 , output_channels = 20 , kernal_size = 3)
  
  activation function (' ReLU ')
  
  max pooling ( kernal_size = 2 , stride = 2 )
  
  batch norm ( input_channels = 20 )
  
  drop out ( probability = 0.25 )
  
  
  2. conv layer ( 20 , 50 , 4 )
  
  activation function (' ReLU ')
  
  max pooling ( 2 , 2 )
  
  batch norm ( 50 )
  
  drop out ( 0.25 )
  
  
  3. conv layer ( 50 , 100 , 3 )
  
  activation function (' ReLU ')
  
  max pooling ( 2 , 2 )
  
  batch norm ( 100 )
  
  drop out ( 0.25 )
  
  
  4. fully connected layer ( input_features = 400 , output_features = 200 )
  
  5. fully connected layer ( 200 , 150 )
  
  6. fully connected layer ( 150 , 100 )
  

Accuracy

last epoch accuracy : 60.75

best accuracy : 61.26

my model : https://drive.google.com/drive/folders/1LYjmtvBkNjfSTB71pFc1WCU2XL1uKwCv?usp=sharing

Usage

• Read the data set

  1. download the data set from this link : https://www.cs.toronto.edu/~kriz/cifar.html
  2. extract the data set file : https://stackoverflow.com/questions/48454111/how-to-extract-tar-files
  3. download the data_loader.py, in data_loader file you will find 2 classes ( cifar100DataSet , cifar100TestSet )
  4. in both of this classes you will find a variable called path, change it to match your data set path

• Train the model

  1. download the cifar100.py file
  2. " if you want ", you can change some parametars
  3. check that you have a GPU
  4. and now just run this file :)

• Load your model

  1. first run the cifar100.py file
  2. you will find a file in the home directory called " model.pth "
  3. download the test_model.py file
  4. change the path variable to match the model.pth path
  5. choose how do you want to use this model