Added configs for rancrop models trained with wce loss

peiva-git · Dec 14, 2023 · feb96d4 · feb96d4
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diff --git a/README.md b/README.md
@@ -111,10 +111,10 @@ In the following table you can find the summarized results of the obtained model
 | Model        | Backbone | Random Crops | Train  Resolution | Test  Resolution | Training Iters | Ball class IoU | Ball class Precision | Ball class Recall | Kappa  | Dice   | Links                                                          |
 |--------------|----------|--------------|-------------------|------------------|----------------|----------------|----------------------|-------------------|--------|--------|----------------------------------------------------------------|
 | PP-LiteSeg-T | STDC1    | 0            | 1024x512          | 2048x1024        | 50000          | 0.6542         | 0.909                | 0.7               | 0.7909 | 0.8954 | [config](configs/pp_liteseg_base_stdc1_ohem_1024x512.yml)      |
-| PP-LiteSeg-T | STDC1    | 1            | 1024x512          | 2048x1024        | 50000          | 0.5561         | 0.9035               | 0.5913            | 0.7147 | 0.8574 | [config](configs/pp_liteseg_rancrop1_stdc1_ohem_1024x512.yml)  |
-| PP-LiteSeg-T | STDC1    | 2            | 1024x512          | 2048x1024        | 50000          | 0.5459         | 0.8999               | 0.5811            | 0.7062 | 0.8531 | [config](configs/pp_liteseg_rancrop2_stdc1_ohem_1024x512.yml)  |
-| PP-LiteSeg-T | STDC1    | 5            | 1024x512          | 2048x1024        | 50000          | 0.5627         | 0.9053               | 0.5979            | 0.7201 | 0.8600 | [config](configs/pp_liteseg_rancrop5_stdc1_ohem_1024x512.yml)  | 
-| PP-LiteSeg-T | STDC1    | 10           | 1024x512          | 2048x1024        | 50000          | 0.5539         | 0.9042               | 0.5884            | 0.7128 | 0.8564 | [config](configs/pp_liteseg_rancrop10_stdc1_ohem_1024x512.yml) |
+| PP-LiteSeg-T | STDC1    | 1            | 1024x512          | 2048x1024        | 50000          | 0.5561         | 0.9035               | 0.5913            | 0.7147 | 0.8574 | [config](configs/rancrop_ohem_10000/pp_liteseg_rancrop1_stdc1_ohem_1024x512.yml)  |
+| PP-LiteSeg-T | STDC1    | 2            | 1024x512          | 2048x1024        | 50000          | 0.5459         | 0.8999               | 0.5811            | 0.7062 | 0.8531 | [config](configs/rancrop_ohem_10000/pp_liteseg_rancrop2_stdc1_ohem_1024x512.yml)  |
+| PP-LiteSeg-T | STDC1    | 5            | 1024x512          | 2048x1024        | 50000          | 0.5627         | 0.9053               | 0.5979            | 0.7201 | 0.8600 | [config](configs/rancrop_ohem_10000/pp_liteseg_rancrop5_stdc1_ohem_1024x512.yml)  | 
+| PP-LiteSeg-T | STDC1    | 10           | 1024x512          | 2048x1024        | 50000          | 0.5539         | 0.9042               | 0.5884            | 0.7128 | 0.8564 | [config](configs/rancrop_ohem_10000/pp_liteseg_rancrop10_stdc1_ohem_1024x512.yml) |
 
 ## Credits
 

diff --git a/...liteseg_rancrop10_stdc1_ohem_1024x512.yml → ...liteseg_rancrop10_stdc1_ohem_1024x512.yml b/...liteseg_rancrop10_stdc1_ohem_1024x512.yml → ...liteseg_rancrop10_stdc1_ohem_1024x512.yml
diff --git a/..._liteseg_rancrop1_stdc1_ohem_1024x512.yml → ..._liteseg_rancrop1_stdc1_ohem_1024x512.yml b/..._liteseg_rancrop1_stdc1_ohem_1024x512.yml → ..._liteseg_rancrop1_stdc1_ohem_1024x512.yml
diff --git a/..._liteseg_rancrop2_stdc1_ohem_1024x512.yml → ..._liteseg_rancrop2_stdc1_ohem_1024x512.yml b/..._liteseg_rancrop2_stdc1_ohem_1024x512.yml → ..._liteseg_rancrop2_stdc1_ohem_1024x512.yml
diff --git a/..._liteseg_rancrop5_stdc1_ohem_1024x512.yml → ..._liteseg_rancrop5_stdc1_ohem_1024x512.yml b/..._liteseg_rancrop5_stdc1_ohem_1024x512.yml → ..._liteseg_rancrop5_stdc1_ohem_1024x512.yml
diff --git a/configs/rancrop_wce_0.01/pp_liteseg_rancrop10_stdc1_wce_1024x512.yml b/configs/rancrop_wce_0.01/pp_liteseg_rancrop10_stdc1_wce_1024x512.yml
@@ -0,0 +1,72 @@
+batch_size: 4
+iters: 50000 # ~ 200 epochs for 1002 training images
+
+train_dataset:
+  type: Dataset
+  dataset_root: /home/ubuntu/dataset_paddleseg/
+  train_path: /home/ubuntu/dataset_paddleseg/train.txt
+  num_classes: 2
+  mode: train
+  transforms:
+    - type: Resize
+      target_size: [1024, 512]
+    - type: ResizeStepScaling
+      min_scale_factor: 0.5
+      max_scale_factor: 2.0
+      scale_step_size: 0.25
+    - type: RandomPaddingCrop
+      crop_size: [1024, 512]
+    - type: RandomHorizontalFlip
+    - type: RandomDistort
+      brightness_range: 0.5
+      contrast_range: 0.5
+      saturation_range: 0.5
+    - type: Normalize
+
+val_dataset:
+  type: Dataset
+  dataset_root: /home/ubuntu/dataset_paddleseg/
+  val_path: /home/ubuntu/dataset_paddleseg/val.txt
+  num_classes: 2
+  mode: val
+  transforms:
+    - type: Resize
+      target_size: [2048, 1024]
+    - type: Normalize
+
+test_config:
+  aug_eval: True
+  scales: 1.0
+
+loss:
+  types:
+    - type: CrossEntropyLoss
+      weight: [0.01, 0.99] # the ball pixels represent ~ 0.004 of the pixels on a 1024x512 image
+    - type: CrossEntropyLoss
+      weight: [0.01, 0.99]
+    - type: CrossEntropyLoss
+      weight: [0.01, 0.99]
+  coef: [1, 1, 1]
+
+optimizer:
+  type: SGD
+  momentum: 0.9
+  weight_decay: 5.0e-4
+
+lr_scheduler:
+  type: PolynomialDecay
+  warmup_iters: 1000
+  warmup_start_lr: 1.0e-5
+  learning_rate: 0.005
+  end_lr: 0
+  power: 0.9
+
+model:
+  type: PPLiteSegRandomCrops
+  num_classes: 2
+  backbone:
+    type: STDC1
+    pretrained: https://bj.bcebos.com/paddleseg/dygraph/PP_STDCNet1.tar.gz
+  arm_out_chs: [32, 64, 128]
+  seg_head_inter_chs: [32, 64, 128]
+  random_crops: 10
diff --git a/configs/rancrop_wce_0.01/pp_liteseg_rancrop1_stdc1_wce_1024x512.yml b/configs/rancrop_wce_0.01/pp_liteseg_rancrop1_stdc1_wce_1024x512.yml
@@ -0,0 +1,72 @@
+batch_size: 4
+iters: 50000 # ~ 200 epochs for 1002 training images
+
+train_dataset:
+  type: Dataset
+  dataset_root: /home/ubuntu/dataset_paddleseg/
+  train_path: /home/ubuntu/dataset_paddleseg/train.txt
+  num_classes: 2
+  mode: train
+  transforms:
+    - type: Resize
+      target_size: [1024, 512]
+    - type: ResizeStepScaling
+      min_scale_factor: 0.5
+      max_scale_factor: 2.0
+      scale_step_size: 0.25
+    - type: RandomPaddingCrop
+      crop_size: [1024, 512]
+    - type: RandomHorizontalFlip
+    - type: RandomDistort
+      brightness_range: 0.5
+      contrast_range: 0.5
+      saturation_range: 0.5
+    - type: Normalize
+
+val_dataset:
+  type: Dataset
+  dataset_root: /home/ubuntu/dataset_paddleseg/
+  val_path: /home/ubuntu/dataset_paddleseg/val.txt
+  num_classes: 2
+  mode: val
+  transforms:
+    - type: Resize
+      target_size: [2048, 1024]
+    - type: Normalize
+
+test_config:
+  aug_eval: True
+  scales: 1.0
+
+loss:
+  types:
+    - type: CrossEntropyLoss
+      weight: [0.01, 0.99] # the ball pixels represent ~ 0.004 of the pixels on a 1024x512 image
+    - type: CrossEntropyLoss
+      weight: [0.01, 0.99]
+    - type: CrossEntropyLoss
+      weight: [0.01, 0.99]
+  coef: [1, 1, 1]
+
+optimizer:
+  type: SGD
+  momentum: 0.9
+  weight_decay: 5.0e-4
+
+lr_scheduler:
+  type: PolynomialDecay
+  warmup_iters: 1000
+  warmup_start_lr: 1.0e-5
+  learning_rate: 0.005
+  end_lr: 0
+  power: 0.9
+
+model:
+  type: PPLiteSegRandomCrops
+  num_classes: 2
+  backbone:
+    type: STDC1
+    pretrained: https://bj.bcebos.com/paddleseg/dygraph/PP_STDCNet1.tar.gz
+  arm_out_chs: [32, 64, 128]
+  seg_head_inter_chs: [32, 64, 128]
+  random_crops: 1
diff --git a/configs/rancrop_wce_0.01/pp_liteseg_rancrop2_stdc1_wce_1024x512.yml b/configs/rancrop_wce_0.01/pp_liteseg_rancrop2_stdc1_wce_1024x512.yml
@@ -0,0 +1,72 @@
+batch_size: 4
+iters: 50000 # ~ 200 epochs for 1002 training images
+
+train_dataset:
+  type: Dataset
+  dataset_root: /home/ubuntu/dataset_paddleseg/
+  train_path: /home/ubuntu/dataset_paddleseg/train.txt
+  num_classes: 2
+  mode: train
+  transforms:
+    - type: Resize
+      target_size: [1024, 512]
+    - type: ResizeStepScaling
+      min_scale_factor: 0.5
+      max_scale_factor: 2.0
+      scale_step_size: 0.25
+    - type: RandomPaddingCrop
+      crop_size: [1024, 512]
+    - type: RandomHorizontalFlip
+    - type: RandomDistort
+      brightness_range: 0.5
+      contrast_range: 0.5
+      saturation_range: 0.5
+    - type: Normalize
+
+val_dataset:
+  type: Dataset
+  dataset_root: /home/ubuntu/dataset_paddleseg/
+  val_path: /home/ubuntu/dataset_paddleseg/val.txt
+  num_classes: 2
+  mode: val
+  transforms:
+    - type: Resize
+      target_size: [2048, 1024]
+    - type: Normalize
+
+test_config:
+  aug_eval: True
+  scales: 1.0
+
+loss:
+  types:
+    - type: CrossEntropyLoss
+      weight: [0.01, 0.99] # the ball pixels represent ~ 0.004 of the pixels on a 1024x512 image
+    - type: CrossEntropyLoss
+      weight: [0.01, 0.99]
+    - type: CrossEntropyLoss
+      weight: [0.01, 0.99]
+  coef: [1, 1, 1]
+
+optimizer:
+  type: SGD
+  momentum: 0.9
+  weight_decay: 5.0e-4
+
+lr_scheduler:
+  type: PolynomialDecay
+  warmup_iters: 1000
+  warmup_start_lr: 1.0e-5
+  learning_rate: 0.005
+  end_lr: 0
+  power: 0.9
+
+model:
+  type: PPLiteSegRandomCrops
+  num_classes: 2
+  backbone:
+    type: STDC1
+    pretrained: https://bj.bcebos.com/paddleseg/dygraph/PP_STDCNet1.tar.gz
+  arm_out_chs: [32, 64, 128]
+  seg_head_inter_chs: [32, 64, 128]
+  random_crops: 2
diff --git a/configs/rancrop_wce_0.01/pp_liteseg_rancrop5_stdc1_wce_1024x512.yml b/configs/rancrop_wce_0.01/pp_liteseg_rancrop5_stdc1_wce_1024x512.yml
@@ -0,0 +1,72 @@
+batch_size: 4
+iters: 50000 # ~ 200 epochs for 1002 training images
+
+train_dataset:
+  type: Dataset
+  dataset_root: /home/ubuntu/dataset_paddleseg/
+  train_path: /home/ubuntu/dataset_paddleseg/train.txt
+  num_classes: 2
+  mode: train
+  transforms:
+    - type: Resize
+      target_size: [1024, 512]
+    - type: ResizeStepScaling
+      min_scale_factor: 0.5
+      max_scale_factor: 2.0
+      scale_step_size: 0.25
+    - type: RandomPaddingCrop
+      crop_size: [1024, 512]
+    - type: RandomHorizontalFlip
+    - type: RandomDistort
+      brightness_range: 0.5
+      contrast_range: 0.5
+      saturation_range: 0.5
+    - type: Normalize
+
+val_dataset:
+  type: Dataset
+  dataset_root: /home/ubuntu/dataset_paddleseg/
+  val_path: /home/ubuntu/dataset_paddleseg/val.txt
+  num_classes: 2
+  mode: val
+  transforms:
+    - type: Resize
+      target_size: [2048, 1024]
+    - type: Normalize
+
+test_config:
+  aug_eval: True
+  scales: 1.0
+
+loss:
+  types:
+    - type: CrossEntropyLoss
+      weight: [0.01, 0.99] # the ball pixels represent ~ 0.004 of the pixels on a 1024x512 image
+    - type: CrossEntropyLoss
+      weight: [0.01, 0.99]
+    - type: CrossEntropyLoss
+      weight: [0.01, 0.99]
+  coef: [1, 1, 1]
+
+optimizer:
+  type: SGD
+  momentum: 0.9
+  weight_decay: 5.0e-4
+
+lr_scheduler:
+  type: PolynomialDecay
+  warmup_iters: 1000
+  warmup_start_lr: 1.0e-5
+  learning_rate: 0.005
+  end_lr: 0
+  power: 0.9
+
+model:
+  type: PPLiteSegRandomCrops
+  num_classes: 2
+  backbone:
+    type: STDC1
+    pretrained: https://bj.bcebos.com/paddleseg/dygraph/PP_STDCNet1.tar.gz
+  arm_out_chs: [32, 64, 128]
+  seg_head_inter_chs: [32, 64, 128]
+  random_crops: 5