https://github.com/debugger404/cifar-pytorch_model

Train Basic Model on CIFAR10 Dataset - 🎨🖥️ Utilizes CIFAR-10 dataset with 60000 32x32 color images in 10 classes. Demonstrates loading using torchvision and training with pretrained models like ResNet18, AlexNet, VGG16, DenseNet161, and Inception. Notebook available for experimentation.
https://github.com/debugger404/cifar-pytorch_model

cifar10 cifar10-classification dataset image-classification python pytorch training

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Train Basic Model on CIFAR10 Dataset - 🎨🖥️ Utilizes CIFAR-10 dataset with 60000 32x32 color images in 10 classes. Demonstrates loading using torchvision and training with pretrained models like ResNet18, AlexNet, VGG16, DenseNet161, and Inception. Notebook available for experimentation.

• Host: GitHub
• URL: https://github.com/debugger404/cifar-pytorch_model
• Owner: deBUGger404
• Created: 2021-05-08T16:06:27.000Z (over 3 years ago)
• Default Branch: main
• Last Pushed: 2021-05-09T05:39:14.000Z (over 3 years ago)
• Last Synced: 2024-11-05T09:48:38.259Z (about 2 months ago)
• Topics: cifar10, cifar10-classification, dataset, image-classification, python, pytorch, training
• Language: Jupyter Notebook
• Homepage:
• Size: 163 KB
• Stars: 1
• Watchers: 1
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Train Basic Model on CIFAR10-Dataset



     



  

## Contents

- [Introduction](#introduction)

- [Prerequisites](#prerequisites)

- [Training](#training)

## Introduction

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.

below is the  6 random images with their respective label:



There is a package of python called `torchvision`, that has data loaders for `CIFAR10` and data transformers for images using `torch.utils.data.DataLoader`.

Below an example of how to load `CIFAR10` dataset using `torchvision`:

```python

import torch

import torchvision

## load data CIFAR10

train_dataset = torchvision.datasets.CIFAR10(root='./train_data', train=True, download=True)

train_dataloader = torch.utils.data.DataLoader(train_dataset, batch_size=128, shuffle=True, num_workers=2)

```

## Prerequisites

- Python>=3.6

- PyTorch >=1.4

- Library are mentioned in `requirenments.txt`

## Training

I used pretrained `resnet18` for model training. you can use any other pretrained model according to you problem.

```python

import torchvision.models as models

alexnet = models.alexnet()

vgg16 = models.vgg16()

densenet = models.densenet161()

inception = models.inception_v3()

```

There are two things for pytorch model training:

1. Notebook - you can just download and play with it

2. python scripts:

    ```

    # Start training with: 

    python main.py

    

    # You can manually pass the attributes for the training: 

    python main.py --lr=0.01 --epoch 20 --model_path './cifar_model.pth'

    

    # Start infrence with:

     python3.6 prediction.py --model_path './cifar_model.pth'

    ```

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