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.)

|—— 📁 your_own_dataset
	|—— 📁 train
		|—— 📁 class_1
			|—— 🖼️ 1.jpg
			|—— ...
		|—— 📁 class_2 
			|—— 🖼️ ...
	|—— 📁 valid
		|—— 📁 class_1
		|—— 📁 ... 
	|—— 📁 test
		|—— 📁 class_1
		|—— 📁 ... 
  1. Check __init__.py. You might need to modify variables and add somethings (transformation, optimizer, lr_schduler …).
    💁
    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.
    💁
    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

🐛
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. (Confusion Matrix)
  3. https://pytorch.org/ignite/generated/ignite.handlers.param_scheduler.create_lr_scheduler_with_warmup.html

GitHub

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