Train data augmentation scripts and config files closes #26

config/m_color_jitter.yaml

@@ -0,0 +1,81 @@
+trainer: "BasicTrainer"
+random_seed: 43
+
+model:
+  type: "efficientnet_b0"
+  parameters:
+    num_classes: 34
+    pretrained: true
+
+training:
+  batch_size: 64
+  epochs:
+    initial: 200
+    fine_tuning: 200
+  loss_function:
+    type: "CrossEntropyLoss"
+  optimizer:
+    type: "SGD"
+    parameters:
+      lr: 0.05
+  learning_rates:
+    initial: 0.01
+    fine_tuning: 0.001
+    final_fine_tuning: 0.0001
+  freeze_until_layer: "classifier.1.weight"
+
+metrics:
+  - type: "Accuracy"
+  - type: "Precision"
+  - type: "Recall"
+  - type: "F1Score"
+
+callbacks:
+  CSVLogging:
+    parameters:
+      csv_path: "dinamically/set/by/date.csv"
+  Checkpoint:
+    parameters:
+      save_freq: 5
+  EarlyStopping:
+    parameters:
+      monitor: "val_loss"
+      patience: 5
+      delta: 0
+      verbose: true
+
+data:
+  name: "CarDataset"
+  dataset_path: "./data/processed/DB_Marca"
+  test_size: 0.1
+  val_size: 0.1
+  transforms:
+    - type: "Resize"
+      parameters:
+        size: [224, 224]
+    - type: "ColorJitter"
+      parameters:
+        brightness: 0.3
+        contrast: 0.3
+        saturation: 0.3
+        hue: 0.1
+    - type: "ToTensor"
+    - type: "Normalize"
+      parameters:
+        mean: [0.485, 0.456, 0.406]
+        std: [0.229, 0.224, 0.225]
+  eval_transforms:
+    - type: "Resize"
+      parameters:
+        size: [224, 224]
+    - type: "ToTensor"
+    - type: "Normalize"
+      parameters:
+        mean: [0.485, 0.456, 0.406]
+        std: [0.229, 0.224, 0.225]
+
+paths:
+  model_path: "./outputs/models/"
+  log_path: "./logs/"
+  plot_path: "./outputs/figures/"
+  checkpoint_path: "./outputs/checkpoints/"

config/m_gaussian_blur.yaml

@@ -0,0 +1,79 @@
+trainer: "BasicTrainer"
+random_seed: 43
+
+model:
+  type: "efficientnet_b0"
+  parameters:
+    num_classes: 34
+    pretrained: true
+
+training:
+  batch_size: 64
+  epochs:
+    initial: 200
+    fine_tuning: 200
+  loss_function:
+    type: "CrossEntropyLoss"
+  optimizer:
+    type: "SGD"
+    parameters:
+      lr: 0.05
+  learning_rates:
+    initial: 0.01
+    fine_tuning: 0.001
+    final_fine_tuning: 0.0001
+  freeze_until_layer: "classifier.1.weight"
+
+metrics:
+  - type: "Accuracy"
+  - type: "Precision"
+  - type: "Recall"
+  - type: "F1Score"
+
+callbacks:
+  CSVLogging:
+    parameters:
+      csv_path: "dinamically/set/by/date.csv"
+  Checkpoint:
+    parameters:
+      save_freq: 5
+  EarlyStopping:
+    parameters:
+      monitor: "val_loss"
+      patience: 5
+      delta: 0
+      verbose: true
+
+data:
+  name: "CarDataset"
+  dataset_path: "./data/processed/DB_Marca"
+  test_size: 0.1
+  val_size: 0.1
+  transforms:
+    - type: "Resize"
+      parameters:
+        size: [224, 224]
+    - type: "GaussianBlur"
+      parameters:
+        kernel_size: [5, 5]
+        sigma: [0.1, 2.0]
+    - type: "ToTensor"
+    - type: "Normalize"
+      parameters:
+        mean: [0.485, 0.456, 0.406]
+        std: [0.229, 0.224, 0.225]
+  eval_transforms:
+    - type: "Resize"
+      parameters:
+        size: [224, 224]
+    - type: "ToTensor"
+    - type: "Normalize"
+      parameters:
+        mean: [0.485, 0.456, 0.406]
+        std: [0.229, 0.224, 0.225]
+
+paths:
+  model_path: "./outputs/models/"
+  log_path: "./logs/"
+  plot_path: "./outputs/figures/"
+  checkpoint_path: "./outputs/checkpoints/"

config/m_random_horizontal_flip.yaml

@@ -0,0 +1,78 @@
+trainer: "BasicTrainer"
+random_seed: 43
+
+model:
+  type: "efficientnet_b0"
+  parameters:
+    num_classes: 34
+    pretrained: true
+
+training:
+  batch_size: 64
+  epochs:
+    initial: 200
+    fine_tuning: 200
+  loss_function:
+    type: "CrossEntropyLoss"
+  optimizer:
+    type: "SGD"
+    parameters:
+      lr: 0.05
+  learning_rates:
+    initial: 0.01
+    fine_tuning: 0.001
+    final_fine_tuning: 0.0001
+  freeze_until_layer: "classifier.1.weight"
+
+metrics:
+  - type: "Accuracy"
+  - type: "Precision"
+  - type: "Recall"
+  - type: "F1Score"
+
+callbacks:
+  CSVLogging:
+    parameters:
+      csv_path: "dinamically/set/by/date.csv"
+  Checkpoint:
+    parameters:
+      save_freq: 5
+  EarlyStopping:
+    parameters:
+      monitor: "val_loss"
+      patience: 5
+      delta: 0
+      verbose: true
+
+data:
+  name: "CarDataset"
+  dataset_path: "./data/processed/DB_Marca"
+  test_size: 0.1
+  val_size: 0.1
+  transforms:
+    - type: "Resize"
+      parameters:
+        size: [224, 224]
+    - type: "RandomHorizontalFlip"
+      parameters:
+        p: 0.5
+    - type: "ToTensor"
+    - type: "Normalize"
+      parameters:
+        mean: [0.485, 0.456, 0.406]
+        std: [0.229, 0.224, 0.225]
+  eval_transforms:
+    - type: "Resize"
+      parameters:
+        size: [224, 224]
+    - type: "ToTensor"
+    - type: "Normalize"
+      parameters:
+        mean: [0.485, 0.456, 0.406]
+        std: [0.229, 0.224, 0.225]
+
+paths:
+  model_path: "./outputs/models/"
+  log_path: "./logs/"
+  plot_path: "./outputs/figures/"
+  checkpoint_path: "./outputs/checkpoints/"

config/m_random_resized_crop.yaml

@@ -0,0 +1,80 @@
+trainer: "BasicTrainer"
+random_seed: 43
+
+model:
+  type: "efficientnet_b0"
+  parameters:
+    num_classes: 34
+    pretrained: true
+
+training:
+  batch_size: 64
+  epochs:
+    initial: 200
+    fine_tuning: 200
+  loss_function:
+    type: "CrossEntropyLoss"
+  optimizer:
+    type: "SGD"
+    parameters:
+      lr: 0.05
+  learning_rates:
+    initial: 0.01
+    fine_tuning: 0.001
+    final_fine_tuning: 0.0001
+  freeze_until_layer: "classifier.1.weight"
+
+metrics:
+  - type: "Accuracy"
+  - type: "Precision"
+  - type: "Recall"
+  - type: "F1Score"
+
+callbacks:
+  CSVLogging:
+    parameters:
+      csv_path: "dinamically/set/by/date.csv"
+  Checkpoint:
+    parameters:
+      save_freq: 5
+  EarlyStopping:
+    parameters:
+      monitor: "val_loss"
+      patience: 5
+      delta: 0
+      verbose: true
+
+data:
+  name: "CarDataset"
+  dataset_path: "./data/processed/DB_Marca"
+  test_size: 0.1
+  val_size: 0.1
+  transforms:
+    - type: "Resize"
+      parameters:
+        size: [224, 224]
+    - type: "RandomResizedCrop"
+      parameters:
+        size: [224, 224]
+        scale: [0.8, 1.0]
+        ratio: [0.75, 1.33]
+    - type: "ToTensor"
+    - type: "Normalize"
+      parameters:
+        mean: [0.485, 0.456, 0.406]
+        std: [0.229, 0.224, 0.225]
+  eval_transforms:
+    - type: "Resize"
+      parameters:
+        size: [240, 240]
+    - type: "ToTensor"
+    - type: "Normalize"
+      parameters:
+        mean: [0.485, 0.456, 0.406]
+        std: [0.229, 0.224, 0.225]
+
+paths:
+  model_path: "./outputs/models/"
+  log_path: "./logs/"
+  plot_path: "./outputs/figures/"
+  checkpoint_path: "./outputs/checkpoints/"