Skip to content

vnk8071/fpt-ai-data-competition

 
 

Repository files navigation

FPT-AI-DATA-COMPETITION

This repository clone from FPT-AI

Update:

  • src/: contains all utils files for EDA.
  • eda/: contains all files for EDA (NOTE: don't commit notebook file)
  • download_data.sh: file bash for downloading the dataset.

1. Create virtual environment:

conda create -n fptai python=3.7
conda activate fptai

2. Clone this repository:

git clone https://github.com/DatacollectorVN/fpt-ai-data-competition.git

3. Install required packages:

pip install -r requirements.txt

4. Download the standard and additional data after processing:

Run to download raw dataset:

bash download_data.sh

Check annotation:

We use Streamlit to display and check annotations of image.

streamlit run eda/streamlit_annotations.py

Data pre-processing:

  • Increase brightness
python eda/increase_brightness.py
  • Enhence face of people
python eda/enhence_face.py

NOTE: Remember to change config correctly

Data augmentation:

  • Mosaic | Flip | Rotate | Mixup
python src/{augmentation_name}_augmentation.py

NOTE: Change path of dataset and number images to generate

  • Auto augmentation based on Yolov5 source code
python auto_augmentation.py

5. Baseline:

Val:

Baselineval

Public_test:

Baselinetest

For more details DRIVE-CHUNG

6. Train:

  • On Google Colab: (Note: Make a copy in drive)
Open In Colab
  • On server:
python train.py --batch-size 32 --device 0 --name <version_name> 

Note: Change the number of epochs to 70 in config/train_cfg.yaml

7. Evaluation:

python val.py --weights results/train/<version_name>/weights/best.pt  --task test --name <version_name> --batch-size 64 --device 0
                                                                             val
                                                                             train
  • Results are saved at results/evaluate/<task>/<version_name>.

8. Prediction:

  • Results are saved at <save_dir>.
python detect.py --weights results/train/<version_name>/weights/best.pt --source <path_to_folder> --dir <save_dir> --device 0

Note:

  • <path_to_folder>: folder contain images to predict (Usually ./dataset/public_test)
  • <save_dir>: path to save images predict

9. Result on leaderboard:

LEADERBOARD

10. Result official:

In the final result, our team finished 15th out of 394 participating teams. We are very happy with this result and will try to do better in the upcoming competitions.

LEADERBOARD_OFFICIAL

Try your best 🔥

About

No description, website, or topics provided.

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages

  • Python 98.0%
  • Jupyter Notebook 2.0%