You can deploy this model and start earning money today!

aijadugar/
cifar-10-vgg16

Views

No views yet

VGG16 on CIFAR-10 using PyTorch

This project implements the VGG16 architecture from scratch using PyTorch and trains it on the CIFAR-10 image classification dataset.

Features

VGG16 architecture implemented from scratch
CIFAR-10 training pipeline
Data normalization
SGD optimizer with Momentum
Learning Rate Scheduler
Model evaluation
Inference on test images
Save and load trained model

Dataset

CIFAR-10 consists of 60,000 RGB images of size 32×32 belonging to 10 classes.

Classes:

Airplane
Automobile
Bird
Cat
Deer
Dog
Frog
Horse
Ship
Truck

Dataset path:

/kaggle/input/datasets/bariankitvinod/cifar-10/data

Model Architecture

Input
│
├── Conv(64)
├── Conv(64)
├── MaxPool
│
├── Conv(128)
├── Conv(128)
├── MaxPool
│
├── Conv(256)
├── Conv(256)
├── Conv(256)
├── MaxPool
│
├── Conv(512)
├── Conv(512)
├── Conv(512)
├── MaxPool
│
├── Conv(512)
├── Conv(512)
├── Conv(512)
├── MaxPool
│
├── Flatten
├── Linear(512)
├── ReLU
├── Dropout
├── Linear(512)
├── ReLU
├── Dropout
└── Linear(10)

Training Configuration

Loss Function

CrossEntropyLoss

Optimizer

SGD
Learning Rate = 0.01
Momentum = 0.9
Weight Decay = 5e-4

Learning Rate Scheduler

StepLR
Step Size = 10
Gamma = 0.1

Epochs

Expected Performance

Typical Test Accuracy

Test Accuracy : 10.00%

Run

Execute all notebook cells sequentially.

Project Structure

VGG16/
│
├── vgg16.ipynb
├── README.md
├── requirements.txt
├── config.py
└── vgg16_cifar10.pth

Technologies Used

Python
PyTorch
Torchvision
NumPy
Matplotlib
tqdm

aijadugar/cifar-10-vgg16