/est_wassup_03

EST Soft AI Bootcamp - WASSUP Final project

Primary LanguagePythonMIT LicenseMIT

middle

Language

한국어 Readme

Project

We used 500,000 Asian amounts.
A total of 7 emotions were used: happy, angry, anxious, peaceful, pain, and sad.
We used YOLOv8-face as a preprocessing method to cut out only the face to save manpower and learning time.
Repvgg, VIT (Vision Transformer), and YOLO were used as 2-stage models, and YOLO was used as 1-stage model.
Streamlit adds various new features.
image

Use

GPU server : 4GPU A-100 (AWS) OS : Linux Language : Python

Team

Experiment Report

Experiment Report Presentation Materials
결과보고서.pdf 감정AI발표자료.pdf
결과보고서.docx

How to Install

git clone https://github.com/JamieSKinard/est_wassup_03.git
cd est_wassup_03
pip install -r requirements.txt
pip install -e .

or

# using conda
conda env create -f env.yaml

How to Pretreatment

# check default path
python preprocess/preprocess.py --data-dir {your_data_path}

Create a cropped photo after face recognition with the yolo8n-face model

How to Learn(2Stage Model)

python main.py --data-dir {your_data_path}
### Check defult
### Change model -> choice(defalut = RepVGG, VIT)
python main.py -mn VIT --data-dir {your_data_path} -mp {your_model} -mn {Reppvgg or VIT}

History and model aved in Models folder/{your_choice_model}

How to Learn(1Stage Model)

Preprocessing is possible with box_labeling_yolov8.ipynb in the folder called preprocess. and Go to the file named YOLO.ipynb and Just Shift + F5

How to evaluation

python eval.py --data-dir {your_test_folder_path} -mp {your_model_path} -mn {Repvgg, VIT}

We used f1, R2, Precision, and recall as metrics.

result

image

YOLO(1Stage) YOLO(2Stage) ReppVgg VIT
val_loss 0.233 0.533 1.470 1.251
train_acc 78.3% 72.2%
val_acc 73.9% 68.7% 62.5%

Example(Mask)

image image