Simple-Image-Classification - Simple Image Classification Code (PyTorch)

Overview

Simple-Image-Classification

Simple Image Classification Code (PyTorch)

Yechan Kim

This repository contains:

  • Python3 / Pytorch code for multi-class image classification

Prerequisites

  • See requirements.txt for details.
torch
torchvision
matplotlib
scikit-learn
tqdm            # not mandatory but recommended
tensorboard     # not mandatory but recommended

How to use

  1. The directory structure of your dataset should be as follows. (You can use our toy-examples: unzip cifar10_dummy.zip.)
|β€”β€” πŸ“ your_own_dataset
	|β€”β€” πŸ“ train
		|β€”β€” πŸ“ class_1
			|β€”β€” πŸ–ΌοΈ 1.jpg
			|β€”β€” ...
		|β€”β€” πŸ“ class_2 
			|β€”β€” πŸ–ΌοΈ ...
	|β€”β€” πŸ“ valid
		|β€”β€” πŸ“ class_1
		|β€”β€” πŸ“ ... 
	|β€”β€” πŸ“ test
		|β€”β€” πŸ“ class_1
		|β€”β€” πŸ“ ... 
  1. Check __init__.py. You might need to modify variables and add somethings (transformation, optimizer, lr_schduler ...). πŸ’ Tip You can add your own loss function as follows:
...
def get_loss_function(loss_function_name, device):
    ... 
    elif loss_function_name == 'your_own_function_name':  # add +
        return your_own_function()
    ...
...
  1. Run train.py for training. The below is an example. See src/my_utils/parser.py for details. πŸ’ Tip --loss_function='CE' means that you choose softmax-cross-entropy (default) for your loss.
python train.py --network_name='resnet34_for_tiny' --dataset_dir='./cifar10_dummy' \
--batch_size=256 --epochs=5  \
--lr=0.1 --lr_step='[60, 120, 160]' --lr_step_gamma=0.5 --lr_warmup_epochs=5 \
--auto_mean_std --store_weights --store_loss_acc_log --store_logits --store_confusion_matrix \
--loss_function='your_own_function_name' --transform_list_name='CIFAR' --tag='train-001'
  1. Run test.py for test. The below is an example. See src/my_utils/parser.py for details.
python test.py --network_name='resnet34_for_tiny' --dataset_dir='./cifar10_dummy' \
--auto_mean_std --store_logits --store_confusion_matrix \
--checkpoint='pretrained_model_weights.pt'

Trailer

  1. If you install tqdm, you can check the progress of training. readme1

  2. If you install tensorboard, you can check the acc/loss changes and confusion matrices during training. readme1

Contribution

πŸ› If you find any bugs or have opinions for further improvements, feel free to contact me ([email protected]). All contributions are welcome.

Reference

  1. https://github.com/weiaicunzai/pytorch-cifar100
  2. https://medium.com/@djin31/how-to-plot-wholesome-confusion-matrix-40134fd402a8 (Confusion Matrix)
  3. https://pytorch.org/ignite/generated/ignite.handlers.param_scheduler.create_lr_scheduler_with_warmup.html
Owner
Yechan Kim
GIST, Machine Learning and Vision Lab.
Yechan Kim
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