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Pytorch Image Classification

General image classification framework implemented by Pytorch for almost all image classification task.

structure

/checkpoints

This directory will store all models you trained.

/data

Please put all your training or test data in this directory and follow the original directory structure. I provide two kinds of dataset format: Custom and ImageFolder. Users can choose one of them for your convinence. For example, if you want to use ImageFolder, you can put all your training images in /data/ImageFolder/train and your test images in /data/ImageFolder/test. The further child directory in /data/ImageFolder/train is the classes folder of your images, such as /data/ImageFolder/train/ClassA...

/log

All log record file will be stored in this directory.

/models

You can put all the network model you design in this directory. I already provide three classic networks: VGG16, ResNet18,ResNet50, GoogLeNet.

env

You could use following command to install all dependencies:

pip -r requirements.txt

PS: for the pytorch version, early version may still be available.

data

I provide two kinds of dataloader in dataset.py: ImageFolder dataloader and Custom dataloader. Users can choose any one of them. All you need is change following part in train.py and test.py:

train_loader = get_imagefolder_train_loader()
#train_loader = get_custom_train_loader()
print('get train loader done')
val_loader = get_imagefoler_val_loader()
#val_loader = get_custom_val_loader()
print('get val loader done')

And do not forget put your images in /data directory following original structure I provide.

config

Users can change the config setting in conf.py as they need, such as IMAGE_SIZE, EPOCH et. al.

train

Users can use following command to start training model:

python train.py -model=vgg16 -gpu
  • -model: choose one model in /models.
  • -gpu: use gpu to train model.

PS: Users can also use following command to get usage:

python train.py -h

test

Users can use following command to evaluate model:

python -model=vgg16 -weights=YOUR_WEIGHT_PATH -gpu -data_path=YOUR_TEST_DATA_PATH
  • -model: choose the model structure of your trained model.
  • -weights: the model weights path.
  • -gpu: use gpu to test model.
  • -data_path: the test data path. Users can modify the my_eval() function in utils.py to better test your own test data.

PS: Users can also use following command to get usage:

python test.py -h

others

utils.py: some utils function used in train.py and test.py. Users can modify this file for their convinence.

If this repo do you a favor, a star is my pleasure :)

And if you find any problem, please contact me or open an issue.