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Level1-Classification Competetion

🌟CV-01조🌟 Supershy 성주희, 한주희, 정재웅, 김혜지, 류경엽, 임서현

Project Structure

${PROJECT}
├── eda
│   ├── data_eda.ipynb
├── main
│   ├── README.md
│   ├── accuracy_loss_print.py
│   ├── dataset.py
│   ├── inference.py
│   ├── loss.py
│   ├── model.py
│   ├── requiremets.txt
│   ├── train.py
│   ├── train_single_multiple.py
│   ├── hard_voting.py
│   └── soft_voting.py
├── README.md
└── requiremets.txt
  • dataset.py : This file contains dataset class for model training and validation
  • inference.py : This file used for predict the model
  • loss.py : This file defines the loss functions used during training
  • model.py : This file defines the model
  • README.md
  • requirements.txt : contains the necessary packages to be installed
  • train.py : This file used for training the model

Code Structure Description

Data EDA

  • Use MaskSplitByProfileDataset
  • Downsampling
  • Stratified Kfold

Model

  • Ensemble Soft Voting
  • Learn additional Fine Tuning based on the public pretrained model EfficientNet + ConvNext + ConvNext(Stratified Kfold)

Getting Started

Setting up Vitual Enviornment

  1. Initialize and update the server

    su -
source .bashrc

  2. Create and Activate a virtual environment in the project directory

    conda create -n env python=3.8
conda activate env

  3. To deactivate and exit the virtual environment, simply run:

    deactivate

Install Requirements

To Insall the necessary packages liksted in requirements.txt, run the following command while your virtual environment is activated:

pip install -r requirements.txt

Usage

Description of all arguments

Training

To train the model with your custom dataset, set the appropriate directories for the training images and model saving, then run the training script.

  • single model
python train.py --data_dir /path/to/images --model_dir /path/to/model --model MODEL_NAME
  • single multiple model
python train_single_multiple.py --data_dir /path/to/images --model_dir /path/to/model --model MODEL_NAME

Inference

For generating predictions with a trained model, provide directories for evaluation data, the trained model, and output, then run the inference script.

  • single model
python inference.py --data_dir /path/to/images --model_dir /path/to/model --output_dir /path/to/model --model MODEL_NAME
  • single multiple model
python inference.py --data_dir /path/to/images --model_dir /path/to/model --output_dir /path/to/model --model_mode single_multiple --model MODEL_NAME

Ensemble

  • ensemble (hard voting)
python hard_voting.py --file_dir ./csv --csv1 file1.csv --csv2 file2.csv --csv3 file3.csv
  • ensemble (soft voting)
python soft_voting.py --models MODEL_NAME1 MODEL_NAME2 MODEL_NAME3 --model_dir ./checkpoint --model_files file1.pth file2.pth file3.pth --data_dir ./data/eval

Arguments

Model

Dataloader