ETRI SW-SoC CV Track Tutorials
테크닉
- Data preprocessing and EDA
- class distribution
- handle class imbalance
- over sampling, under sampling
- fill missing values
- normalization
- Data Augmentation
- Albumentations Albumentations
- soft augmentation
- Brightness,…
- Flip,…
- hard augmentation
- CutOut
- CutMix
- Mixup
- Random Erasing,…
- Good base model
- Papers with Code - SOTA Papers with Code
- OpenMMLab, PaddlePaddle, Detectron,…
- to improve model capacity…
- CNN vs ViT ViT Transformer
- Dig, Dig, Dig,…
- mixed-precision training (AMP)
- Data cleansing
- label smoothing
- Batch size
- K-fold cross validation
- weight initialization
simple random initailization- LeCun Initialization
- Xavier initialization ← Sigmoid, tanh
- He Initialization ← ReLU
- Bias 는 일반적으로 0으로 초기화
- learning rate scheduling and warmup
- early stopping
- Loss Function Optimization for “Class imbalanced label”
- focal loss
- dice loss
- SparseMax loss
- …
- Evaluation and Error Analysis
- MLFlow / WandB / Tensorboard,…
- Confusion matrix
- Grad CAM
- Model Ensemble
- Majority Voting
- Bagging
- Boosting
- Weighted Probability Averaging,...
- Test Time Augmentation