1. auto-encoder + anomaly detection
2. Ensemble
3. Use domain knowledage
4. Long tail classification
5. metric learning
6. self labeling
7. Self-supervise learning
8. Early Stop

1. construct a binary classifier for (1) 11 & 13 classes and (2) others (V)
2. construct a binary classifier for 11 and 13 classes (V)
3. train the rest of the classes

1. CNNClassifier1113_trial_6 :::info resnet152 epoch 10 82.10 % 204.86 sec ::::

   CNNClassifier1113_trial_7 :::info resnet152 epoch 15 87.04 % 314.89 sec ::::

   CNNClassifier1113_trial_8 :::info resnet152 epoch 15 optim Adam 80.86 % 312.98 sec
   ::::

   CNNClassifier1113_trial_9 :::info resnet152 epoch 20 87.96 % 415.64 ::::

1. CNNClassifier1113_trial_1 :::info 1 epoch resnet18 optim SGD StepLR 89.51 % (low minor class accuracy) 20.16 sec :::

   CNNClassifier1113_trial_2 :::info 8 epoch resnet18 optim SGD StepLR 80.56 % 141.13 sec :::

   CNNClassifier1113_trial_4 :::info 8 epoch resnet18 optim SGD ReduceLROnPlateau 79.01 % :::

1. CNNClassifier_trial_17 (V) :::info 8 epoch resnet18 optim SGD StepLR 97.53 % 952.68 sec :::

2. using freeze-unfreeze (this method does not work very well in small model)

   CNNClassifier_trial_18 freeze :::info 5 epoch resnet18 optim SGD StepLR 92.44 % 597.40 sec :::

   CNNClassifier_trial_19 unfreeze :::info 8 epoch resnet18 optim SGD StepLR 97.45 % 949.98 sec :::

ratio of 11 & 13 vs other: 1:4 -> ~80 % baseline

1. Use kmean on feature maps, to seperate 11 & 13 from other. :::info 74.94 % :::

2. using CNN model

   CNNClassifier_trial_3 :::info 1 epoch resnet18 85.51 % 120.92 sec :::

   CNNClassifier_trial_4 :::info 4 epoch resnet18 93.03 % 471.69 sec :::

3. Using weightloss

   CNNClassifier_trial_6 :::info 1 epoch resnet18 91.66 % 119.36 sec :::

   CNNClassifier_trial_10 :::info 2 epoch resnet152 85.12 % 741.58 sec
   :::

   CNNClassifier_trial_12 :::info 5 epoch resnet18 optim SGD 96.00 % 593.48 sec :::

add in model.train() and model.eval() in the train.py

use trial 68 as freeze

trial 69
unfreeze
:::info
epoch 20
91.34 %
2621.9 sec
:::

re-tain trial 64 (73.56 %) with fine-tuning

trial 68
freeze
:::info
epoch 6
--lr_name 'StepLR' --optim Adam
73.87 %
:::

re-train trial 65 (88.84 %) with fine-tuning trial 66 :::info epoch 6 --lr_name 'StepLR' --optim SGD 89.15 % 787.5 sec :::

trial 67
:::info
epoch 6
--lr_name 'StepLR' --optim Adam
89.89 %
790.0 sec
:::

• using the cluster label

• file oneDfea_combine_True is class 11 and 13 merge feature maps

• file oneDfea_lab_combine_True.txt is corresponded image names

• file oneDfea_newlab_1113merge is class 11 and 13 merge cluster new labels

• file oneDfea_imgname_1113merge.txt is corresponded image names

• file oneDfea_train_label36 is the continuous labels

  trial 64 freeze :::info epoch 10 73.56 % 1189.4 sec :::

  trial 65 unfreeze :::info epoch 15 88.84 % 1949.2 sec :::

• train freeze model (trial 56) more

  trial 59 freeze :::info epoch 5 76.18 % overfiting at the last epoch :::

  trial 60 freeze :::info StepLR epoch 5 76.18 % ::: train more have minor enhance

cluster image will improve the performace

• It is not an good idea using fc in the middle of auto-encoder (auto_encoder_trial_15)
• Changing the feature number in default auto-encoder model (trial 16 5 epochs)
  • check the loss threshold of class 10 (cannot get with this model)
• Refine model: add batch normalization (trial 17)
• TODO: Postone for now

• let class 11 and 13 to be the same class.

  • use class label with range (0~13), original 11 & 13 merge to class 11.
  • class 14 change to class 13

• Discard cluster class

  • model output change
  • dataset label change
  • train.py val loop change

  trial 56 freeze :::info epoch 10 75.01 % 1177.3 sec :::

  trial 57 unfreeze :::info epoch 15 89.31 % 1939.0 sec :::

  trial 58 re-train trial 57 :::info StepLR, SGD 5 epoch 89.54 % :::

label 13 class is the worst in trial 47 try to add cluster method to label13 -> guess not, cluster seperation is very bad.

auto-encoder

1. Default auto-encoder model (v)

1. Use se_resnext101_32x4d model trial 45 :::info epoch 1 59.42 % 690.6 sec :::

2. Using freeze-unfreeze trial 46 freeze :::info epoch 10 69.68 % 1182.7 sec epoch 7 of val loss shows the sign of overfitting :::

   trial 47 unfreeze :::info epoch 15 90.21 % 1968.7 sec while lr 0.0001 performance increase :::

   trial 48 unfreeze and re-train trial 47 with small lr and using SGD optim :::info epoch 5 90.80 % 659.4 sec :::

   trial 49 unfreeze and re-train trial 47 with small lr (StepLR, step_size=2, gamma=0.1) and using SGD optim :::info epoch 5 90.80 % 651.5 sec :::

----------- Correct Accuracy Updated until Here-----------

1. use the SGD+momentum re-run trial 43 trial 43 :::info epoch 5 82.8 % 635.6 sec the overfitting sign gone :::
2. use the SGD+momentum re-run trial 43 trial 44 :::info epoch 10 81.32 % no improvement :::

!!!!!!!!!! IMPORTANT !!!!!!!!!!! YOU have not use AUG in train set (V)

trial 42
freeze
:::info
epoch 15, lr fixed
60 %
1770.4 sec
:::

trial 43 (file get washed @@)
unfreeze
:::info
epoch 20
82.6 %
sign of overfitting around epoch 13
:::

Using freeze-unfreeze method

trial 39
freeze
:::info
epoch 15
66.27 %
1670.6 sec
:::

trial 40
unfreeze and ReduceLROnPlateau with patience=4
:::info
epoch 20
81.0 %
2508.8 sec
sign of overfitting
:::

• cluster2target fixed
  • val loss bug fixed

SGD -> Adam, default parameters trial 37 :::info epoch 5 67.02 % 406.9 sec :::

trial 38
:::info
epoch 15
75 %
1897.9 sec
at epoch 6, sign of overfitting
:::

k_numbers = [3,3,2,4,4,4,2,2,3,2,0,4,0,0,0]

1. 37 subclasses :::info 3 epochs 26.87 % :::

1. SGD -> Adam, default parameters :::info 3 epochs 27.50 % ::: :::info 15 epochs train loss is underfitting val loss is overfitting sth is wrong @@ check the label!! :::

1. features from trial 23
   1. Feature separation is not good.
   2. kmean does not seperate the class well.
2. features from imagenet (V)
   1. well seperate!
   2. kmean can have good seperations

• oneDfea_train_metal_trained_False is feature maps
• oneDfea_lab_train_metal_trained_False their corresponded labels

model_name change to se_resnet152 for default batch_size 16 as default

1. Using the cluster dataset
   1. Directly use the 66 subclass :::info --epoch 10 -model_name 'se_resnet152' --batch_size 16 50.08 % :::

1. Using class distribution train/val/test set

2. Pretrain trial: Freeze :::info --epoch 15 --model_name 'se_resnet152' --batch_size 16 64.1 % 1645.0 sec :::

   Unfreeze :::info --epoch 20 --model_name 'se_resnet152' --batch_size 16 81.36 % 3100.4 sec :::

1. Pretrainmodel parameter freeze

   1. freeze pretrain model parameter, using default lr :::info trial 18: lr = 0.01 --epoch 10 --model_name 'se_resnet152' --batch_size 16 59.07 % 1120.5 sec :::

   2. Loading the previous model path, then unfreeze the model, and change the lr to smaller one. :::info trial 19: lr = 0.005 --epoch 10 --model_name 'se_resnet152' --batch_size 16 87.5 % 1258.5 sec :::

1. CrossEntropy -> WeightFocalLoss :::info 'se_resnet50' batch_size 32 epoch 12 30.1 % :::

1. StepLR(step_size=3, gamma=0.1)
2. model_name 'se_resnet50' :::info batch_size 32 epoch 12 72.2 % 1374.3 sec :::

1. StepLR -> ReduceLROnPlateau
2. solving batch_size issue
3. model_name 'efficientnet-b7' :::info batch_size = 5 epoch 1 12.14 % 246.9 sec :::

1. StepLR -> ReduceLROnPlateau
2. solving batch_size issue
3. model_name 'se_resnet152' :::info batch_size = 16 epoch 10 77.67 % 1276.7 sec :::

Note that below is add up modifications

1. optimizer: add momentum=0.9, nesterov=True :::info 1 epoch: 45.4 % :::

1. learning rate: LambdaLR -> StepLR(step_size=1, gamma=0.1)
2. optimizer: add weight_decay=0.01 :::info Trial 7 1 epoch: 42.1 % 2 epoch: 47.4 % 3011.4 sec :::

1. rewrite dataset
2. num_workers
3. pin_memory :::info 1/8 time saved ::::

1. learning rate: LambdaLR -> StepLR(step_size=1, gamma=0.1)
2. optimizer: add weight_decay=0.01 :::info Trial 7 20 epoch: 48 % 2375.6 sec :::

In T2H folder image: T1H_Q1558-1090326175042092.jpg T1H_Q1558-1090326175049966.jpg T1H_Q1558-1090326175101066.jpg

I move them to T1H folder. :::info accuracy baseline: 56.7% (trial_3) :::

file is saved as trial_3