add new parameters in config files

configs/paper_resnet18_softmax_only.yml

@@ -0,0 +1,48 @@
+input_shape :  [48, 48, 3]
+encodings_len: 256
+margin: 0.5
+mode : 'softmax_only'
+distance_type : 'l1'
+backbone : 'resnet18'
+backbone_weights : 'imagenet'
+freeze_backbone : False
+augmentation_type : 'default'
+min_n_obj_per_class : 30 
+select_max_n_obj_per_class : 30
+max_n_obj_per_class : 10000
+embeddings_normalization: True
+
+# optimizer parameters
+optimizer : 'radam'
+learning_rate : 0.0001
+decay_factor : 0.99
+step_size : 1
+
+# embeddings learning training parameters
+n_epochs : 1000
+n_steps_per_epoch : 200
+val_batch_size : 8
+val_steps : 500
+negatives_selection_mode : 'semihard'
+mining_n_classes: 5
+mining_n_samples: 3
+
+# softmax pretraining parameters
+softmax_pretraining : True
+softmax_batch_size : 8
+softmax_val_steps : 200
+softmax_steps_per_epoch : 800
+softmax_epochs : 200
+
+#paths
+work_dir : 'work_dirs/paper_resnet18_softmax_only/'
+dataset_path : '/home/rauf/datasets/road_signs/road_signs_separated/'
+plot_history : True
+model_save_name : 'best_model_paper_resnet18_softmax_only.h5'
+encodings_save_name: 'encodings_paper_resnet18_softmax_only.pkl'
+
+# encodings parameters
+save_encodings : True
+centers_only: False
+max_num_samples_of_each_class : 30
+knn_k : 1

configs/paper_resnet18_test_remaining.yml

@@ -0,0 +1,48 @@
+input_shape :  [48, 48, 3]
+encodings_len: 512
+margin: 0.5
+mode : 'triplet'
+distance_type : 'l1'
+backbone : 'resnet18'
+backbone_weights : 'imagenet'
+freeze_backbone : False
+augmentation_type : 'default'
+min_n_obj_per_class : 0
+select_max_n_obj_per_class : 30
+max_n_obj_per_class : 30
+embeddings_normalization: True
+
+# optimizer parameters
+optimizer : 'radam'
+learning_rate : 0.0001
+decay_factor : 0.99
+step_size : 1
+
+# embeddings learning training parameters
+n_epochs : 1000
+n_steps_per_epoch : 200
+val_batch_size : 8
+val_steps : 500
+negatives_selection_mode : 'semihard'
+mining_n_classes: 5
+mining_n_samples: 3
+
+# softmax pretraining parameters
+softmax_pretraining : True
+softmax_batch_size : 8
+softmax_val_steps : 200
+softmax_steps_per_epoch : 800
+softmax_epochs : 2
+
+#paths
+work_dir : 'work_dirs/paper_resnet18_remaining/'
+dataset_path : '/home/rauf/datasets/road_signs/road_signs_separated/'
+plot_history : True
+model_save_name : 'best_model_paper_resnet18_remaining.h5'
+encodings_save_name: 'encodings_paper_resnet18_remaining.pkl'
+
+# encodings parameters
+save_encodings : True
+centers_only: False
+max_num_samples_of_each_class : 30
+knn_k : 1

configs/paper_resnet18_with_softmax.yml

@@ -0,0 +1,48 @@
+input_shape :  [48, 48, 3]
+encodings_len: 256
+margin: 0.5
+mode : 'triplet'
+distance_type : 'l1'
+backbone : 'resnet18'
+backbone_weights : 'imagenet'
+freeze_backbone : False
+augmentation_type : 'default'
+min_n_obj_per_class : 30 
+select_max_n_obj_per_class : 30
+max_n_obj_per_class : 10000
+embeddings_normalization: True
+
+# optimizer parameters
+optimizer : 'radam'
+learning_rate : 0.0001
+decay_factor : 0.99
+step_size : 1
+
+# embeddings learning training parameters
+n_epochs : 1000
+n_steps_per_epoch : 200
+val_batch_size : 8
+val_steps : 500
+negatives_selection_mode : 'semihard'
+mining_n_classes: 5
+mining_n_samples: 3
+
+# softmax pretraining parameters
+softmax_pretraining : True
+softmax_batch_size : 8
+softmax_val_steps : 200
+softmax_steps_per_epoch : 800
+softmax_epochs : 10
+
+#paths
+work_dir : 'work_dirs/paper_resnet18_with_softmax/'
+dataset_path : '/home/rauf/datasets/road_signs/road_signs_separated/'
+plot_history : True
+model_save_name : 'best_model_paper_resnet18_with_softmax.h5'
+encodings_save_name: 'encodings_paper_resnet18_with_softmax.pkl'
+
+# encodings parameters
+save_encodings : True
+centers_only: False
+max_num_samples_of_each_class : 30
+knn_k : 1

configs/paper_resnet18_without_softmax.yml

@@ -0,0 +1,48 @@
+input_shape :  [48, 48, 3]
+encodings_len: 256
+margin: 0.5
+mode : 'triplet'
+distance_type : 'l1'
+backbone : 'resnet18'
+backbone_weights : 'imagenet'
+freeze_backbone : False
+augmentation_type : 'default'
+min_n_obj_per_class : 30 
+select_max_n_obj_per_class : 30
+max_n_obj_per_class : 10000
+embeddings_normalization: True
+
+# optimizer parameters
+optimizer : 'radam'
+learning_rate : 0.0001
+decay_factor : 0.99
+step_size : 1
+
+# embeddings learning training parameters
+n_epochs : 1000
+n_steps_per_epoch : 200
+val_batch_size : 8
+val_steps : 500
+negatives_selection_mode : 'semihard'
+mining_n_classes: 5
+mining_n_samples: 3
+
+# softmax pretraining parameters
+softmax_pretraining : False
+softmax_batch_size : 8
+softmax_val_steps : 200
+softmax_steps_per_epoch : 800
+softmax_epochs : 20
+
+#paths
+work_dir : 'work_dirs/paper_resnet18_without_softmax/'
+dataset_path : '/home/rauf/datasets/road_signs/road_signs_separated/'
+plot_history : True
+model_save_name : 'best_model_paper_resnet18_without_softmax.h5'
+encodings_save_name: 'encodings_paper_resnet18_without_softmax.pkl'
+
+# encodings parameters
+save_encodings : True
+centers_only: False
+max_num_samples_of_each_class : 30
+knn_k : 1

configs/plates.yml

@@ -1,23 +1,26 @@
 input_shape :  [128, 128, 3]
-encodings_len: 512
-margin: 0.4
+encodings_len: 128
+margin: 0.5
 mode : 'triplet'
 distance_type : 'l2'
-backbone : 'resnet18'
+backbone : 'simple2'
 backbone_weights : 'imagenet'
-freeze_backbone : True
-augmentation_type : None
+freeze_backbone : False
+augmentation_type : 'plates'
+min_n_obj_per_class : 0 
+select_max_n_obj_per_class : 30
+max_n_obj_per_class : 10000
 embeddings_normalization: True
 
 # optimizer parameters
 optimizer : 'radam'
 learning_rate : 0.0001
 decay_factor : 0.99
-step_size : 10
+step_size : 1
 
 # embeddings learning training parameters
 n_epochs : 1000
-n_steps_per_epoch : 10
+n_steps_per_epoch : 200
 val_batch_size : 8
 val_steps : 10
 negatives_selection_mode : 'semihard'
@@ -27,16 +30,17 @@ mining_n_samples: 3
 # softmax pretraining parameters
 softmax_pretraining : True
 softmax_batch_size : 8
-softmax_val_steps : 2
-softmax_steps_per_epoch : 2
-softmax_epochs : 1000
+softmax_val_steps : 1
+softmax_steps_per_epoch : 500
+softmax_epochs : 5
 
+#paths
 # paths
 work_dir : 'work_dirs/plates/'
-dataset_path : '/home/rauf/plates_competition/dataset/to_train/'
+dataset_path : '/home/rauf/datasets/plates/plates_splitted/'
 plot_history : True
-model_save_name : 'best_model_resnet18_plates.h5'
-encodings_save_name: 'encodings_resnet18_plates.pkl'
+model_save_name : 'best_model_plates_simple2.h5'
+encodings_save_name: 'encodings_plates_simple2.pkl'
 
 # encodings parameters
 save_encodings : True

configs/plates_resnet18.yml

@@ -0,0 +1,51 @@
+input_shape :  [128, 128, 3]
+encodings_len: 128
+margin: 0.4
+mode : 'triplet'
+distance_type : 'l1'
+backbone : 'resnet18'
+backbone_weights : 'imagenet'
+freeze_backbone : True
+augmentation_type : 'plates2'
+min_n_obj_per_class : 0 
+select_max_n_obj_per_class : 30
+max_n_obj_per_class : 10000
+embeddings_normalization: True
+
+# optimizer parameters
+optimizer : 'radam'
+learning_rate : 0.0001
+decay_factor : 0.99999
+step_size : 1
+
+# embeddings learning training parameters
+n_epochs : 1000
+n_steps_per_epoch : 200
+val_batch_size : 16
+val_steps : 10
+negatives_selection_mode : 'semihard'
+mining_n_classes: 2
+mining_n_samples: 5
+
+# softmax pretraining parameters
+softmax_pretraining : True
+softmax_batch_size_train : 32
+softmax_batch_size_val : 16
+softmax_val_steps : 50
+softmax_steps_per_epoch : 100
+softmax_epochs : 100
+softmax_is_binary: True
+
+#paths
+# paths
+work_dir : 'work_dirs/plates/'
+dataset_path : '/home/rauf/datasets/plates/plates_splitted/'
+plot_history : True
+model_save_name : 'best_model_plates_resnet18.h5'
+encodings_save_name: 'encodings_plates_resnet18.pkl'
+
+# encodings parameters
+save_encodings : True
+centers_only: False
+max_num_samples_of_each_class : 30
+knn_k : 1

configs/plates_resnext50.yml

@@ -0,0 +1,51 @@
+input_shape :  [128, 128, 3]
+encodings_len: 128
+margin: 0.3
+mode : 'triplet'
+distance_type : 'l1'
+backbone : 'resnext50'
+backbone_weights : 'imagenet'
+freeze_backbone : True
+augmentation_type : 'plates2'
+min_n_obj_per_class : 0 
+select_max_n_obj_per_class : 30
+max_n_obj_per_class : 10000
+embeddings_normalization: True
+
+# optimizer parameters
+optimizer : 'radam'
+learning_rate : 0.00001
+decay_factor : 0.99999
+step_size : 1
+
+# embeddings learning training parameters
+n_epochs : 1000
+n_steps_per_epoch : 200
+val_batch_size : 4
+val_steps : 10
+negatives_selection_mode : 'semihard'
+mining_n_classes: 2
+mining_n_samples: 3
+
+# softmax pretraining parameters
+softmax_pretraining : True
+softmax_batch_size_train : 16
+softmax_batch_size_val : 8
+softmax_val_steps : 1
+softmax_steps_per_epoch : 10
+softmax_epochs : 100
+softmax_is_binary: True
+
+#paths
+# paths
+work_dir : 'work_dirs/plates_resnext50/'
+dataset_path : '/home/rauf/datasets/plates/plates_splitted/'
+plot_history : True
+model_save_name : 'best_model_plates_resnext50.h5'
+encodings_save_name: 'encodings_plates_resnext50.pkl'
+
+# encodings parameters
+save_encodings : True
+centers_only: False
+max_num_samples_of_each_class : 30
+knn_k : 1