🌟CV-08조🌟 MakeZenerator 팀 김태양, 김혜지, 신호준, 성주희, 임서현, 정소윤

${PROJECT}
 ┃  
 ┣ Data_EDA
 ┃ ┗ Train_data_EDA.ipynb  
 ┃ ┗ upsampling.ipynb  
 ┃ ┗ Val_data_EDA.ipynb  
 ┃
 ┣ mmdetection
 ┃
 ┣ operating_configs
 ┃ ┗ base_config.py
 ┃
 ┣ .github
 ┣ .gitignore  
 ┣ .gitmessage.txt  
 ┣ .pre-commit-config.yaml  
 ┣ README.md  
 ┗ cuda_test.py

• Data_EDA : This folder contains..
• mmdetection : This folder contains...
• operating_configs : This folder contains...
• README.md
• requirements.txt : contains the necessary packages to be installed
• train.py : This file used for training the model

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)

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
   

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

pip install -r requirements.txt

Description of all arguments

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

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 (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

• ConvNext Tiny
• EfficientNet v2 small
• ViT16

• oversampling with data augmentation