image_classification_pytorch

This repository contains practice of image classification tasks in PyTorch tutorials, with implementation of some of the convolutional networks referred by the Dive Into Deep Learning book (中文版:动手学深度学习).

Requirements

• python 3.6
• pytorch 1.7.0
• torchvision 0.8.1
• numpy
• tqdm

Usage

dataset

• Cifar10 download

Instead of using cifar10 dataset from torchvision, I write code for collecting data from raw files in dataset folder.

train

train the model using train.py

$ python train.py --data_dir=<cifar10-directory> --checkpoint_dir=<where-to-save-checkpoint> --model_name=ResNet18

Instead of invoking model from torchvision.models, I write codes to implement several convolutional neural networks in models folders.

The supported model_name args are:

LeNet
VGG11
VGG13
VGG16
VGG19
ResNet18
ResNet34
ResNet50
ResNet101
ResNet152
DenseNet121
DenseNet161
DenseNet169
DenseNet201

test

test the model using evaluate.py

$ python evaluate.py --data_dir=<cifar10-directory> --checkpoint_dir=<path-to-particular-saved-model-dir>

Reference

https://github.com/ShusenTang/Dive-into-DL-PyTorch

https://github.com/yunjey/pytorch-tutorial

https://github.com/pytorch/vision/tree/d5096a7f9944fde2619649f2374d866c86e66c32/torchvision/models