Comments (1)
config
==== Dataset & Transform ====
dataset: MaskBaseDataset
augmentation: AutoAugmentation
resize: [
256,
192
]
valid_ratio: 0.2
balanced_split: true
sampler: ImbalancedSampler
age_drop: True
==== Training ====
seed: 42
max_epochs: 30
batch_size: 48
valid_batch_size: 64
optimizer: AdamW
lr: 0.001
criterion: focal
scheduler: cosine
lr_decay_step: 20
log_interval: 50
inference score
f1-score | accuracy |
---|---|
0.6637 | 75.2540 |
from level1-imageclassification-cv-10.
Related Issues (20)
- [REFACT] datasets 내부에 있는 augmentation code 관련된 코드 분리
- [FEAT] multi-head 모델 drop_age, mislabel data 적용
- [FEAT] RMSPROP optimizer 추가
- [FIX] multi_train.py augmentation class 경로 수정 필요 HOT 1
- [FEAT] 소규모 모델들 실험 HOT 1
- [FEAT] RandAugment 이용해 성능이 좋은 Augmentation 찾기 HOT 1
- [FEAT] Loss(Cross Entropy, Focal Loss) 실험 HOT 1
- [FEAT] ConvNeXt 모델 추가 및 실험
- [FEAT] train & validation folder split
- [FEAT] Add Confusion Matrix in wandb
- [FEAT] yolo, rembg 관련 기능 추가 및 실험 HOT 3
- [FEAT] vit, swin transformer 추가 HOT 1
- [FEAT] Add cutmix and mixup code HOT 1
- [FEAT] multi-head 추가 실험
- [FEAT] Cutmix/Mixup smooth label 구현 HOT 1
- [FIX] 코드 모듈화 및 가독성 위한 문서화
- [FEAT] hard-voting / soft-voting 앙상블 실험
- [FEAT] pretrained models 를 기반으로 ensemble 하는 코드
- [FEAT] Stacking / Soft Voting / Hard Voting Ensemble 실험
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from level1-imageclassification-cv-10.