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Baseline
- Model: se_resnext50_32x4d
- image_size: 512x512
- batch_size: 64
- grad_accum: 2
- augmentations:
def train_aug(image_size=512): return Compose([ Resize(image_size, image_size), RandomRotate90(), Flip(), Transpose(), ], p=1) def valid_aug(image_size=512): return Compose([ # CenterCrop(448, 448), Resize(image_size, image_size) # Normalize(), ], p=1)
- Optimizers:
criterion_params: criterion: CrossEntropyLoss optimizer_params: optimizer: Adam lr: 0.0003 weight_decay: 0.0001 scheduler_params: scheduler: MultiStepLR milestones: [25, 30, 40] gamma: 0.5 data_params: batch_size: 64 num_workers: 4 drop_last: False image_size: &image_size 512 train_csv: "./csv/train_0.csv" valid_csv: "./csv/valid_0.csv" root: "/raid/data/kaggle/recursion-cellular-image-classification/" site: 2 channels: [1, 2, 3]
Results: (fold 0)
Experiment CV LB c123_s1 42.9% 30.6% c123_s2 41% 23.6% Ensemble 0.7 * c123_s1 + 0.3 * c123_s2 - 32.5 c123_s1: using channels=[1,2,3] and site = 1