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Validation and Leaderboard Progress Stage 2

Model (.scripts/ folder) Image Size LSTM Epochs Bag TTA Fold Val Stg1 Test LB Public / Private Comment
ResNeXt-101 32x8d retrained v12 fold 0 1 w/stg2 (v12&v13) with LSTM 480 5 LSTM 12X v12 0 1 2- hflip transpose; v13 0 - hflip 0 1 2 0 1 2 (v12 v13) 0.05622, 0.05775, 0.05604, 0.05648, 0.05775, 0.05534 --- 0.697 / 0.044 Incl stage 1 test, resnextv12/run_train1024lstmdeltattasum.sh resnextv12/run_train1024lstmdeltattasum.sh & eda/val_lstm_v22.py
ResNeXt-101 32x8d (v12&v13) with LSTM 480 5 LSTM 12X v12 0 1 2- hflip transpose; v13 0 - hflip 0 1 2 0 1 2 (v12 v13) 0.05654, 0.05807, 0.05604, 0.05648, 0.05775, 0.05534 0.4544 0.654 / 0.045 Incl stage 1 test, resnextv12/run_train1024lstmdeltattasum.sh resnextv12/run_train1024lstmdeltattasum.sh & eda/val_lstm_v22.py
ResNeXt-101 32x8d (v12&v13) with LSTM 480 5 LSTM 12X v12 0 1 2- hflip transpose; v13 0 - hflip 0 1 2 0 1 2 (v12 v13) 0.05699, 0.05866, 0.05642, 0.05696, 0.05844, 0.05588 0.5703 0.675 / 0.045 Excl stage 1 test, resnextv12/run_train1024lstmdeltattasum.sh resnextv12/run_train1024lstmdeltattasum.sh & eda/val_lstm_v21.py
ResNeXt-101 32x8d (v12&v13) with LSTM 480 5 LSTM 12X v12 0 1 2- hflip transpose 0 1 2 (v12) 0.05699, 0.05866, 0.05642 0.5706 0.713 / 0.046 Excl stage 1 test, resnextv12/run_train1024lstmdeltattasum.sh resnextv12/run_train1024lstmdeltattasum.sh & eda/val_lstm_v20.py

Validation and Leaderboard Progress Stage 1

Model (.scripts/ folder) Image Size Epochs Bag TTA Fold Val LB Comment
ResNeXt-101 32x8d (v12&v13) with LSTM 480 5, 5, 5, 5 LSTM 12X v12 0 1 2- hflip transpose; v13 0 - hflip 0 1 2 0 (v13) 0.05705 0.05866 0.05645 0.05690 0.057 Hidden 2048, bag12 epochs, resnextv12/run_train1024lstmdeltattasum.sh resnextv12/run_train1024lstmdeltattasum.sh & eda/val_lstm_v14.py
ResNeXt-101 32x8d (v12&v13) with LSTM 480 5, 5, 5, 5, 5 LSTM 12X v12 0 1 2- hflip transpose; v13 0 1 - hflip 0 1 2 0 (v13) 0.05687 0.05859 0.05651 0.05685 0.05839 0.057 Hidden 2048, bag12 epochs, resnextv12/run_train1024lstmdeltattasum.sh resnextv12/run_train1024lstmdeltattasum.sh & eda/val_lstm_v16.py
ResNeXt-101 32x8d (v12) with LSTM 480 5, 5, 5 LSTM 12X all folds 0-hflip, 1 2 - transpose 0 1 2 0.05705 0.05866 0.05645 0.057 Increase hidden units to 2048, bag12 epochs, scripts/resnextv12/run_train1024lstmdeltatta.sh & eda/val_lstm_v13.py , bsize 4 patients
ResNeXt-101 32x8d (v12) with LSTM 480 5 9X HFlip TTA on fold0 only 0 1 2 0.05730 0.05899 0.05681 0.057 Concat delta to prev and delta to next, bag9 epochs, scripts/resnextv12/trainlstmdelta.py & eda/val_lstm_v11.py , bsize 4 patients
ResNeXt-101 32x8d (v12) with LSTM 480 6 5X None 0 0.0574 0.059 Concat delta to prev and delta to next, bag4 epochs, scripts/resnextv12/trainlstmdelta.py, bsize 4 patients
ResNeXt-101 32x8d (v11) with LSTM 384 5, 5, 6 5X None 0, 1, 2 0.05780, 0.05914, 0.05666 0.059 2X LSTM 1024 hidden units, bag4 epochs, scripts/resnextv11/trainlstmdeep.py & eda/val_lstm_v9.py, bsize 4 patients
ResNeXt-101 32x8d (v12) with LSTM 480 5 5X None 0 0.05758 0.059 2X LSTM 1024 hidden units, bag4 epochs, scripts/resnextv12/trainlstmdeep.py, bsize 4 patients
ResNeXt-101 32x8d (v6) with LSTM 384 7, 5, 7 6X, 4X, 6X None 0, 1, 2 0.5836, 0.6060, 0.5728 0.060 2X LSTM 256 hidden units, bag4 epochs, scripts/resnextv11/trainlstmdeep.py, bsize 4 patients
ResNeXt-101 32x8d (v11) with LSTM 384 5 5X None 0 0.05780 0.060 2X LSTM 1024 hidden units, bag4 epochs, scripts/resnextv11/trainlstmdeep.py, bsize 4 patients
ResNeXt-101 32x8d (v6) with LSTM 384 7 5X None 0 0.05811 0.061 2X LSTM 256 hidden units, bag4 epochs, scripts/resnextv6/trainlstmdeep.py, bsize 4 patients
SEResNeXt-50 32x8d (v3) with LSTM(1024HU) 448 4 3X None 0, 1, 2, 3 0.05876, 0.06073, 0.05847, 0.06079 0.061 2X LSTM 1024 hidden units, bag8 epochs, scripts/resnextv6/trainlstmdeep.py, bsize 4 patients
ResNeXt-101 32x8d (v6) with LSTM 384 7 3X None 0 0.05844 0.061 2X LSTM 256 hidden units, bag4 epochs, scripts/resnextv6/trainlstmdeep.py, bsize 4 patients
ResNeXt-101 32x8d (v8) with LSTM 384 7 6X None 0 ----- 0.062 2X LSTM 256 hidden units, bag4 epochs, scripts/resnextv8/trainlstmdeep.py, bsize 4 patients
ResNeXt-101 32x8d (v4) with LSTM 256 7 5X None 0 0.06119 0.064 2X LSTM 256 hidden units, bag4 epochs, scripts/resnextv4/trainlstmdeep.py, bsize 4 patients
ResNeXt-101 32x8d (v4) with LSTM 256 7 5X None 0 0.06217 0.065 LSTM 64 hidden units, bag 5 epochs, scripts/resnextv4/trainlstm.py, bsize 4 patients
ResNeXt-101 32x8d (v8) 384 7 5X None 5 (all) ----- 0.066 Weighted [0.6, 1.8, 0.6] rolling mean win3, transpose, submission_v6.py, bsize 128
ResNeXt-101 32x8d (v8) 384 7 4X None 5 (all) ----- 0.067 Weighted [0.6, 1.8, 0.6] rolling mean win3, transpose, submission_v6.py, bsize 128
ResNeXt-101 32x8d (v6) 384 7 5X None 0 0.06336 0.068 Weighted [0.6, 1.8, 0.6] rolling mean win3, transpose, submission_v5.py, bsize 32
ResNeXt-101 32x8d (v4) 256 7 5X None 0 0.06489 0.070 Weighted [0.6, 1.8, 0.6] rolling mean win3, transpose, submission_v4.py, bsize 64
ResNeXt-101 32x8d (v4) 256 7 5X None 0 0.06582 0.070 Rolling mean window 3, transpose, submission_v3.py, bsize 64
ResNeXt-101 32x8d (v4) 256 4 3X None 0 0.06874 0.074 Rolling mean window 3, transpose, submission_v3.py, bsize 64
EfficientnetV0 (v8) 256 6 3X None 0 0.07416 0.081 Rolling mean window 3, no transpose, submission_v2.py, bsize 64
EfficientnetV0 (v8) 384 4 2X None 0 0.07661 0.085 With transpose augmentation
LSTM on logits from ResNeXt-101 32x8d (v4) 256 3 3X None 0 0.063 0.082 LSTM on sequence of patients logits, bsize 4 patients
EfficientnetV0 (v8) 384 2 1X None 0 0.07931 0.088 With transpose augmentation
EfficientnetV0 (v8) 384 11 2X None 0 0.08330 0.093 With transpose augmentation
EfficientnetV0 224 4 2X None 0 0.08047 ???? Without transpose augmentation
EfficientnetV0 224 4 2X None 0 0.08267 ???? With transpose augmentation
EfficientnetV0 224 2 1X None 0 0.08519 ???? With transpose augmentation
EfficientnetV0 224 11 2X None 0 0.08607 ???? With transpose augmentation