Dacon 3D Number Image Classification

🔗 Competition Link

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🏆 Result:🥇8/165🥇

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Environment

• Operating System: Ubuntu 18.04
• Package Management: Anaconda
• Deep Learning Framework: Pytorch 1.11

Description

• Initial attempts with VoxelNet for training struggled to surpass a score of 0.80.
• Inspired by a post on the code-sharing board, Principal Component Analysis (PCA) was applied to Point Cloud data to convert it into images for training.
• Chose EfficientNet B0 for its efficiency with simple color schemes and shapes.
• Applied data augmentations: Rotate, RandomGridShuffle, Mixup, and Backbone Freeze.
  • Rotate was consistently used during training and testing.
  • RandomGridShuffle enabled the model to learn high-level features, drawing inspiration from the JigSaw puzzle concept.
  • Mixup was used to enhance model performance on ambiguous images.
  • In the final 10 epochs, the model's backbone was frozen, and the classifier was trained without augmentations (except Rotate) to improve performance on non-augmented data.
• Training sequence: RandomGridShuffle for 35 epochs, Mixup for 25 epochs, followed by Backbone Freeze for 10 epochs.
• Used k-fold, Ensemble, and Soft Voting for robustness.
  • Trained on 5 folds.
  • Ensembled the outputs of models from different folds.
  • Compared Soft Voting and Hard Voting; Soft Voting yielded better results.

Getting Started

• Install Conda virtual environment:

  conda env create --file environment.yaml
  

• Convert Point Cloud Dataset to Image Dataset:

  python pc2img.py --mode train --data ./data/train.h5 --output ./data/img/train/
  

Exploratory Data Analysis (EDA)

• Refer to EDA.ipynb.
• Note: The documentation may be confusing due to incomplete organization.

Training

• Command to run:

  python main.py train --CTL_STEP 36 61
  

• CTL_STEP specifies epochs to apply RandomGridShuffle, Mixup, and Backbone freeze.
• For more details on arguments, see main.py.

Testing

• Command to run:

  python main.py test --ENSEMBLE soft --CHECKPOINT ./ckpt/model1.pth ./ckpt/model2.pth
  

• ENSEMBLE option: Use 'soft' for soft voting, 'hard' for hard voting. If not using ensemble, omit this option.
• For additional details on arguments, consult main.py.