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AWL_detector_utils/configs/bases/Base-RCNN-FPN.yaml

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+MODEL:
+  META_ARCHITECTURE: "GeneralizedRCNN"
+  BACKBONE:
+    NAME: "build_resnet_fpn_backbone"
+  RESNETS:
+    OUT_FEATURES: ["res2", "res3", "res4", "res5"]
+  FPN:
+    IN_FEATURES: ["res2", "res3", "res4", "res5"]
+  ANCHOR_GENERATOR:
+    SIZES: [[32], [64], [128], [256], [512]] # One size for each in feature map
+    ASPECT_RATIOS: [[0.5, 1.0, 2.0]] # Three aspect ratios (same for all in feature maps)
+  RPN:
+    IN_FEATURES: ["p2", "p3", "p4", "p5", "p6"]
+    PRE_NMS_TOPK_TRAIN: 2000 # Per FPN level
+    PRE_NMS_TOPK_TEST: 1000 # Per FPN level
+    # Detectron1 uses 2000 proposals per-batch,
+    # (See "modeling/rpn/rpn_outputs.py" for details of this legacy issue)
+    # which is approximately 1000 proposals per-image since the default batch size for FPN is 2.
+    POST_NMS_TOPK_TRAIN: 1000
+    POST_NMS_TOPK_TEST: 1000
+  ROI_HEADS:
+    NAME: "StandardROIHeads"
+    IN_FEATURES: ["p2", "p3", "p4", "p5"]
+  ROI_BOX_HEAD:
+    NAME: "FastRCNNConvFCHead"
+    NUM_FC: 2
+    POOLER_RESOLUTION: 7
+  ROI_MASK_HEAD:
+    NAME: "MaskRCNNConvUpsampleHead"
+    NUM_CONV: 4
+    POOLER_RESOLUTION: 14
+DATASETS:
+  TRAIN: ("coco_2017_train",)
+  TEST: ("coco_2017_val",)
+SOLVER:
+  IMS_PER_BATCH: 16
+  BASE_LR: 0.02
+  STEPS: (60000, 80000)
+  MAX_ITER: 90000
+INPUT:
+  MIN_SIZE_TRAIN: (640, 672, 704, 736, 768, 800)
+VERSION: 2

AWL_detector_utils/configs/bases/Base-RetinaNet.yaml

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+MODEL:
+  META_ARCHITECTURE: "RetinaNet"
+  BACKBONE:
+    NAME: "build_retinanet_resnet_fpn_backbone"
+  RESNETS:
+    OUT_FEATURES: ["res3", "res4", "res5"]
+  ANCHOR_GENERATOR:
+    SIZES:
+      !!python/object/apply:eval [
+        "[[x, x * 2**(1.0/3), x * 2**(2.0/3) ] for x in [32, 64, 128, 256, 512 ]]",
+      ]
+  FPN:
+    IN_FEATURES: ["res3", "res4", "res5"]
+  RETINANET:
+    IOU_THRESHOLDS: [0.4, 0.5]
+    IOU_LABELS: [0, -1, 1]
+    SMOOTH_L1_LOSS_BETA: 0.0
+DATASETS:
+  TRAIN: ("coco_2017_train",)
+  TEST: ("coco_2017_val",)
+SOLVER:
+  IMS_PER_BATCH: 16
+  BASE_LR: 0.01 # Note that RetinaNet uses a different default learning rate
+  STEPS: (60000, 80000)
+  MAX_ITER: 90000
+INPUT:
+  MIN_SIZE_TRAIN: (640, 672, 704, 736, 768, 800)
+VERSION: 2

AWL_detector_utils/configs/faster_rcnn_web.yaml

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+_BASE_: "./bases/Base-RCNN-FPN.yaml"
+MODEL:
+  # COCO ResNet50 weights
+  WEIGHTS: "https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/faster_rcnn_R_50_FPN_3x/137849458/model_final_280758.pkl"
+  MASK_ON: False # Not doing segmentation
+  RESNETS:
+    DEPTH: 50 # ResNet50
+  ROI_HEADS:
+    NUM_CLASSES: 5 # Change to suit own task
+    # Can reduce this for lower memory/faster training; Default 512
+    BATCH_SIZE_PER_IMAGE: 512
+  BACKBONE:
+    FREEZE_AT: 2 # Default 2
+DATASETS:
+  TRAIN: ("web_train",)
+  TEST: ("web_test",)
+DATALOADER:
+  NUM_WORKERS: 0
+SOLVER:
+  IMS_PER_BATCH: 8 # Batch size; Default 16
+  BASE_LR: 0.00001
+  # (2/3, 8/9)
+  STEPS: (16341, 21788) # The iteration number to decrease learning rate by GAMMA.
+  MAX_ITER: 24512 # Number of training iterations
+  CHECKPOINT_PERIOD: 4000 # Saves checkpoint every number of steps
+INPUT:
+  MIN_SIZE_TRAIN: (640, 672, 704, 736, 768, 800) # Image input sizes
+TEST:
+  # The period (in terms of steps) to evaluate the model during training.
+  # Set to 0 to disable.
+  EVAL_PERIOD: 1000
+OUTPUT_DIR: "./output/website" # Specify output directory
+

AWL_detector_utils/configs/faster_rcnn_web_lr0.001.yaml

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+_BASE_: "./bases/Base-RCNN-FPN.yaml"
+MODEL:
+  # COCO ResNet50 weights
+  WEIGHTS: "https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/faster_rcnn_R_50_FPN_3x/137849458/model_final_280758.pkl"
+  MASK_ON: False # Not doing segmentation
+  RESNETS:
+    DEPTH: 50 # ResNet50
+  ROI_HEADS:
+    NUM_CLASSES: 5 # Change to suit own task
+    # Can reduce this for lower memory/faster training; Default 512
+    BATCH_SIZE_PER_IMAGE: 512
+  BACKBONE:
+    FREEZE_AT: 2 # Default 2
+DATASETS:
+  TRAIN: ("web_train",)
+  TEST: ("web_test",)
+DATALOADER:
+  NUM_WORKERS: 0
+SOLVER:
+  IMS_PER_BATCH: 8 # Batch size; Default 16
+  BASE_LR: 0.001
+  # (2/3, 8/9)
+  STEPS: (16341, 21788) # The iteration number to decrease learning rate by GAMMA.
+  MAX_ITER: 24512 # Number of training iterations
+  CHECKPOINT_PERIOD: 4000 # Saves checkpoint every number of steps
+INPUT:
+  MIN_SIZE_TRAIN: (640, 672, 704, 736, 768, 800) # Image input sizes
+TEST:
+  # The period (in terms of steps) to evaluate the model during training.
+  # Set to 0 to disable.
+  EVAL_PERIOD: 0
+OUTPUT_DIR: "./output/website_lr0.001" # Specify output directory
+
+
+

AWL_detector_utils/configs/faster_rcnn_web_lr0.01.yaml

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+_BASE_: "./bases/Base-RCNN-FPN.yaml"
+MODEL:
+  # COCO ResNet50 weights
+  WEIGHTS: "https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/faster_rcnn_R_50_FPN_3x/137849458/model_final_280758.pkl"
+  MASK_ON: False # Not doing segmentation
+  RESNETS:
+    DEPTH: 50 # ResNet50
+  ROI_HEADS:
+    NUM_CLASSES: 5 # Change to suit own task
+    # Can reduce this for lower memory/faster training; Default 512
+    BATCH_SIZE_PER_IMAGE: 512
+  BACKBONE:
+    FREEZE_AT: 2 # Default 2
+DATASETS:
+  TRAIN: ("web_train",)
+  TEST: ("web_test",)
+DATALOADER:
+  NUM_WORKERS: 0
+SOLVER:
+  IMS_PER_BATCH: 8 # Batch size; Default 16
+  BASE_LR: 0.01
+  # (2/3, 8/9)
+  STEPS: (16341, 21788) # The iteration number to decrease learning rate by GAMMA.
+  MAX_ITER: 24512 # Number of training iterations
+  CHECKPOINT_PERIOD: 4000 # Saves checkpoint every number of steps
+INPUT:
+  MIN_SIZE_TRAIN: (640, 672, 704, 736, 768, 800) # Image input sizes
+TEST:
+  # The period (in terms of steps) to evaluate the model during training.
+  # Set to 0 to disable.
+  EVAL_PERIOD: 0
+OUTPUT_DIR: "./output/website_lr0.01" # Specify output directory
+
+
+
+

AWL_detector_utils/output/website_lr0.001/model_final.pth

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+oid sha256:11b2b2360ca1865e3a3f07b7837232ceac4ebceca32ad1d63c9c643ea6454d92
+size 330145096

crp_classifier_utils/output/Increase_resolution_lr0.005/BiT-M-R50x1V2_0.005.pth.tar

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+version https://git-lfs.github.com/spec/v1
+oid sha256:5c364a56229bb518d34c9b6a07f4a5d253fc42c3cb0e87734d79da8f54f8d778
+size 188221125

crp_locator_utils/login_finder/configs/bases/Base-RCNN-FPN.yaml

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+MODEL:
+  META_ARCHITECTURE: "GeneralizedRCNN"
+  BACKBONE:
+    NAME: "build_resnet_fpn_backbone"
+  RESNETS:
+    OUT_FEATURES: ["res2", "res3", "res4", "res5"]
+  FPN:
+    IN_FEATURES: ["res2", "res3", "res4", "res5"]
+  ANCHOR_GENERATOR:
+    SIZES: [[32], [64], [128], [256], [512]] # One size for each in feature map
+    ASPECT_RATIOS: [[0.5, 1.0, 2.0]] # Three aspect ratios (same for all in feature maps)
+  RPN:
+    IN_FEATURES: ["p2", "p3", "p4", "p5", "p6"]
+    PRE_NMS_TOPK_TRAIN: 2000 # Per FPN level
+    PRE_NMS_TOPK_TEST: 1000 # Per FPN level
+    # Detectron1 uses 2000 proposals per-batch,
+    # (See "modeling/rpn/rpn_outputs.py" for details of this legacy issue)
+    # which is approximately 1000 proposals per-image since the default batch size for FPN is 2.
+    POST_NMS_TOPK_TRAIN: 1000
+    POST_NMS_TOPK_TEST: 1000
+  ROI_HEADS:
+    NAME: "StandardROIHeads"
+    IN_FEATURES: ["p2", "p3", "p4", "p5"]
+  ROI_BOX_HEAD:
+    NAME: "FastRCNNConvFCHead"
+    NUM_FC: 2
+    POOLER_RESOLUTION: 7
+  ROI_MASK_HEAD:
+    NAME: "MaskRCNNConvUpsampleHead"
+    NUM_CONV: 4
+    POOLER_RESOLUTION: 14
+DATASETS:
+  TRAIN: ("coco_2017_train",)
+  TEST: ("coco_2017_val",)
+SOLVER:
+  IMS_PER_BATCH: 16
+  BASE_LR: 0.02
+  STEPS: (60000, 80000)
+  MAX_ITER: 90000
+INPUT:
+  MIN_SIZE_TRAIN: (640, 672, 704, 736, 768, 800)
+VERSION: 2

crp_locator_utils/login_finder/configs/bases/Base-RetinaNet.yaml

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+MODEL:
+  META_ARCHITECTURE: "RetinaNet"
+  BACKBONE:
+    NAME: "build_retinanet_resnet_fpn_backbone"
+  RESNETS:
+    OUT_FEATURES: ["res3", "res4", "res5"]
+  ANCHOR_GENERATOR:
+    SIZES:
+      !!python/object/apply:eval [
+        "[[x, x * 2**(1.0/3), x * 2**(2.0/3) ] for x in [32, 64, 128, 256, 512 ]]",
+      ]
+  FPN:
+    IN_FEATURES: ["res3", "res4", "res5"]
+  RETINANET:
+    IOU_THRESHOLDS: [0.4, 0.5]
+    IOU_LABELS: [0, -1, 1]
+    SMOOTH_L1_LOSS_BETA: 0.0
+DATASETS:
+  TRAIN: ("coco_2017_train",)
+  TEST: ("coco_2017_val",)
+SOLVER:
+  IMS_PER_BATCH: 16
+  BASE_LR: 0.01 # Note that RetinaNet uses a different default learning rate
+  STEPS: (60000, 80000)
+  MAX_ITER: 90000
+INPUT:
+  MIN_SIZE_TRAIN: (640, 672, 704, 736, 768, 800)
+VERSION: 2

crp_locator_utils/login_finder/configs/faster_rcnn_login_RPN.yaml

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+_BASE_: "./bases/Base-RCNN-FPN.yaml"
+MODEL:
+  # COCO ResNet50 weights
+  WEIGHTS: "https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/faster_rcnn_R_50_FPN_3x/137849458/model_final_280758.pkl"
+  MASK_ON: False # Not doing segmentation
+  RESNETS:
+    DEPTH: 50 # ResNet50
+  ROI_HEADS:
+    NUM_CLASSES: 1 # Change to suit own task
+    # Can reduce this for lower memory/faster training; Default 512
+    BATCH_SIZE_PER_IMAGE: 512
+  BACKBONE:
+    FREEZE_AT: 2 # Default 2
+  RPN:
+    POST_NMS_TOPK_TEST: 5000 # increase number of regions, default is 1000
+    
+DATASETS:
+  TRAIN: ("login_train",)
+  TEST: ("login_test",)
+  
+DATALOADER:
+  NUM_WORKERS: 0
+  
+SOLVER:
+  IMS_PER_BATCH: 8 # Batch size; Default 16
+  BASE_LR: 0.00001
+  # (2/3, 8/9)
+  STEPS: (20000, 26666) # The iteration number to decrease learning rate by GAMMA.
+  MAX_ITER: 30000 # Number of training iterations
+  CHECKPOINT_PERIOD: 4000 # Saves checkpoint every number of steps
+  
+INPUT:
+  MIN_SIZE_TRAIN: (640, 672, 704, 736, 768, 800) # Image input sizes
+  
+TEST:
+  # The period (in terms of steps) to evaluate the model during training.
+  # Set to 0 to disable.
+  EVAL_PERIOD: 2000
+  
+OUTPUT_DIR: "./output/lr0.00001_nocheat" # Specify output directory
+

crp_locator_utils/login_finder/configs/faster_rcnn_login_lr0.00001.yaml

@@ -0,0 +1,33 @@
+_BASE_: "./bases/Base-RCNN-FPN.yaml"
+MODEL:
+  # COCO ResNet50 weights
+  WEIGHTS: "https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/faster_rcnn_R_50_FPN_3x/137849458/model_final_280758.pkl"
+  MASK_ON: False # Not doing segmentation
+  RESNETS:
+    DEPTH: 50 # ResNet50
+  ROI_HEADS:
+    NUM_CLASSES: 1 # Change to suit own task
+    # Can reduce this for lower memory/faster training; Default 512
+    BATCH_SIZE_PER_IMAGE: 512
+  BACKBONE:
+    FREEZE_AT: 2 # Default 2
+DATASETS:
+  TRAIN: ("login_train",)
+  TEST: ("login_test",)
+DATALOADER:
+  NUM_WORKERS: 0
+SOLVER:
+  IMS_PER_BATCH: 8 # Batch size; Default 16
+  BASE_LR: 0.00001
+  # (2/3, 8/9)
+  STEPS: (20000, 26666) # The iteration number to decrease learning rate by GAMMA.
+  MAX_ITER: 30000 # Number of training iterations
+  CHECKPOINT_PERIOD: 4000 # Saves checkpoint every number of steps
+INPUT:
+  MIN_SIZE_TRAIN: (640, 672, 704, 736, 768, 800) # Image input sizes
+TEST:
+  # The period (in terms of steps) to evaluate the model during training.
+  # Set to 0 to disable.
+  EVAL_PERIOD: 2000
+OUTPUT_DIR: "./output/lr0.00001_nocheat" # Specify output directory
+

crp_locator_utils/login_finder/configs/faster_rcnn_login_lr0.00001_aug.yaml

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+_BASE_: "./bases/Base-RCNN-FPN.yaml"
+MODEL:
+  # COCO ResNet50 weights
+  WEIGHTS: "https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/faster_rcnn_R_50_FPN_3x/137849458/model_final_280758.pkl"
+  MASK_ON: False # Not doing segmentation
+  RESNETS:
+    DEPTH: 50 # ResNet50
+  ROI_HEADS:
+    NUM_CLASSES: 1 # Change to suit own task
+    # Can reduce this for lower memory/faster training; Default 512
+    BATCH_SIZE_PER_IMAGE: 512
+  BACKBONE:
+    FREEZE_AT: 2 # Default 2
+DATASETS:
+  TRAIN: ("login_train_aug",)
+  TEST: ("login_test",)
+DATALOADER:
+  NUM_WORKERS: 0
+SOLVER:
+  IMS_PER_BATCH: 8 # Batch size; Default 16
+  BASE_LR: 0.00001
+  # (2/3, 8/9)
+  STEPS: (26666, 35555) # The iteration number to decrease learning rate by GAMMA.
+  MAX_ITER: 40000 # Number of training iterations
+  CHECKPOINT_PERIOD: 4000 # Saves checkpoint every number of steps
+INPUT:
+  MIN_SIZE_TRAIN: (640, 672, 704, 736, 768, 800) # Image input sizes
+TEST:
+  # The period (in terms of steps) to evaluate the model during training.
+  # Set to 0 to disable.
+  EVAL_PERIOD: 2000
+OUTPUT_DIR: "./output/lr0.00001_aug" # Specify output directory
+

crp_locator_utils/login_finder/configs/faster_rcnn_login_lr0.00001_finetune.yaml

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+_BASE_: "./bases/Base-RCNN-FPN.yaml"
+MODEL:
+  # COCO ResNet50 weights
+  WEIGHTS: "./output/lr0.001_aug/model_final.pth"
+  MASK_ON: False # Not doing segmentation
+  RESNETS:
+    DEPTH: 50 # ResNet50
+  ROI_HEADS:
+    NUM_CLASSES: 1 # Change to suit own task
+    # Can reduce this for lower memory/faster training; Default 512
+    BATCH_SIZE_PER_IMAGE: 512
+  BACKBONE:
+    FREEZE_AT: 2 # Default 2
+DATASETS:
+  TRAIN: ("login_train",)
+  TEST: ("login_test",)
+DATALOADER:
+  NUM_WORKERS: 0
+SOLVER:
+  IMS_PER_BATCH: 8 # Batch size; Default 16
+  BASE_LR: 0.00001
+  # (2/3, 8/9)
+  STEPS: (12000, 16000) # The iteration number to decrease learning rate by GAMMA.
+  MAX_ITER: 18000 # Number of training iterations
+  CHECKPOINT_PERIOD: 4000 # Saves checkpoint every number of steps
+INPUT:
+  MIN_SIZE_TRAIN: (640, 672, 704, 736, 768, 800) # Image input sizes
+TEST:
+  # The period (in terms of steps) to evaluate the model during training.
+  # Set to 0 to disable.
+  EVAL_PERIOD: 2000
+OUTPUT_DIR: "./output/lr0.00001_finetune" # Specify output directory
+

crp_locator_utils/login_finder/configs/faster_rcnn_login_lr0.0001.yaml

@@ -0,0 +1,34 @@
+_BASE_: "./bases/Base-RCNN-FPN.yaml"
+MODEL:
+  # COCO ResNet50 weights
+  WEIGHTS: "https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/faster_rcnn_R_50_FPN_3x/137849458/model_final_280758.pkl"
+  MASK_ON: False # Not doing segmentation
+  RESNETS:
+    DEPTH: 50 # ResNet50
+  ROI_HEADS:
+    NUM_CLASSES: 1 # Change to suit own task
+    # Can reduce this for lower memory/faster training; Default 512
+    BATCH_SIZE_PER_IMAGE: 512
+  BACKBONE:
+    FREEZE_AT: 2 # Default 2
+DATASETS:
+  TRAIN: ("login_train",)
+  TEST: ("login_test",)
+DATALOADER:
+  NUM_WORKERS: 0
+SOLVER:
+  IMS_PER_BATCH: 8 # Batch size; Default 16
+  BASE_LR: 0.0001
+  # (2/3, 8/9)
+  STEPS: (20000, 26666) # The iteration number to decrease learning rate by GAMMA.
+  MAX_ITER: 30000 # Number of training iterations
+  CHECKPOINT_PERIOD: 4000 # Saves checkpoint every number of steps
+INPUT:
+  MIN_SIZE_TRAIN: (640, 672, 704, 736, 768, 800) # Image input sizes
+TEST:
+  # The period (in terms of steps) to evaluate the model during training.
+  # Set to 0 to disable.
+  EVAL_PERIOD: 2000
+OUTPUT_DIR: "./output/lr0.0001_nocheat" # Specify output directory
+
+

crp_locator_utils/login_finder/configs/faster_rcnn_login_lr0.0001_aug.yaml

@@ -0,0 +1,34 @@
+_BASE_: "./bases/Base-RCNN-FPN.yaml"
+MODEL:
+  # COCO ResNet50 weights
+  WEIGHTS: "https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/faster_rcnn_R_50_FPN_3x/137849458/model_final_280758.pkl"
+  MASK_ON: False # Not doing segmentation
+  RESNETS:
+    DEPTH: 50 # ResNet50
+  ROI_HEADS:
+    NUM_CLASSES: 1 # Change to suit own task
+    # Can reduce this for lower memory/faster training; Default 512
+    BATCH_SIZE_PER_IMAGE: 512
+  BACKBONE:
+    FREEZE_AT: 2 # Default 2
+DATASETS:
+  TRAIN: ("login_train_aug",)
+  TEST: ("login_test",)
+DATALOADER:
+  NUM_WORKERS: 0
+SOLVER:
+  IMS_PER_BATCH: 8 # Batch size; Default 16
+  BASE_LR: 0.0001
+  # (2/3, 8/9)
+  STEPS: (26666, 35555) # The iteration number to decrease learning rate by GAMMA.
+  MAX_ITER: 40000 # Number of training iterations
+  CHECKPOINT_PERIOD: 4000 # Saves checkpoint every number of steps
+INPUT:
+  MIN_SIZE_TRAIN: (640, 672, 704, 736, 768, 800) # Image input sizes
+TEST:
+  # The period (in terms of steps) to evaluate the model during training.
+  # Set to 0 to disable.
+  EVAL_PERIOD: 2000
+OUTPUT_DIR: "./output/lr0.0001_aug" # Specify output directory
+
+

crp_locator_utils/login_finder/configs/faster_rcnn_login_lr0.0001_finetune.yaml

@@ -0,0 +1,34 @@
+_BASE_: "./bases/Base-RCNN-FPN.yaml"
+MODEL:
+  # COCO ResNet50 weights
+  WEIGHTS: "./output/lr0.001_aug/model_final.pth"
+  MASK_ON: False # Not doing segmentation
+  RESNETS:
+    DEPTH: 50 # ResNet50
+  ROI_HEADS:
+    NUM_CLASSES: 1 # Change to suit own task
+    # Can reduce this for lower memory/faster training; Default 512
+    BATCH_SIZE_PER_IMAGE: 512
+  BACKBONE:
+    FREEZE_AT: 2 # Default 2
+DATASETS:
+  TRAIN: ("login_train",)
+  TEST: ("login_test",)
+DATALOADER:
+  NUM_WORKERS: 0
+SOLVER:
+  IMS_PER_BATCH: 8 # Batch size; Default 16
+  BASE_LR: 0.0001
+  # (2/3, 8/9)
+  STEPS: (12000, 16000) # The iteration number to decrease learning rate by GAMMA.
+  MAX_ITER: 18000 # Number of training iterations
+  CHECKPOINT_PERIOD: 4000 # Saves checkpoint every number of steps
+INPUT:
+  MIN_SIZE_TRAIN: (640, 672, 704, 736, 768, 800) # Image input sizes
+TEST:
+  # The period (in terms of steps) to evaluate the model during training.
+  # Set to 0 to disable.
+  EVAL_PERIOD: 2000
+OUTPUT_DIR: "./output/lr0.0001_finetune" # Specify output directory
+
+

crp_locator_utils/login_finder/configs/faster_rcnn_login_lr0.001.yaml

@@ -0,0 +1,34 @@
+_BASE_: "./bases/Base-RCNN-FPN.yaml"
+MODEL:
+  # COCO ResNet50 weights
+  WEIGHTS: "https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/faster_rcnn_R_50_FPN_3x/137849458/model_final_280758.pkl"
+  MASK_ON: False # Not doing segmentation
+  RESNETS:
+    DEPTH: 50 # ResNet50
+  ROI_HEADS:
+    NUM_CLASSES: 1 # Change to suit own task
+    # Can reduce this for lower memory/faster training; Default 512
+    BATCH_SIZE_PER_IMAGE: 512
+  BACKBONE:
+    FREEZE_AT: 2 # Default 2
+DATASETS:
+  TRAIN: ("login_train",)
+  TEST: ("login_test",)
+DATALOADER:
+  NUM_WORKERS: 0
+SOLVER:
+  IMS_PER_BATCH: 8 # Batch size; Default 16
+  BASE_LR: 0.001
+  # (2/3, 8/9)
+  STEPS: (20000, 26666) # The iteration number to decrease learning rate by GAMMA.
+  MAX_ITER: 30000 # Number of training iterations
+  CHECKPOINT_PERIOD: 4000 # Saves checkpoint every number of steps
+INPUT:
+  MIN_SIZE_TRAIN: (640, 672, 704, 736, 768, 800) # Image input sizes
+TEST:
+  # The period (in terms of steps) to evaluate the model during training.
+  # Set to 0 to disable.
+  EVAL_PERIOD: 2000
+OUTPUT_DIR: "./output/lr0.001_nocheat" # Specify output directory
+
+

crp_locator_utils/login_finder/configs/faster_rcnn_login_lr0.001_aug.yaml

@@ -0,0 +1,34 @@
+_BASE_: "./bases/Base-RCNN-FPN.yaml"
+MODEL:
+  # COCO ResNet50 weights
+  WEIGHTS: "https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/faster_rcnn_R_50_FPN_3x/137849458/model_final_280758.pkl"
+  MASK_ON: False # Not doing segmentation
+  RESNETS:
+    DEPTH: 50 # ResNet50
+  ROI_HEADS:
+    NUM_CLASSES: 1 # Change to suit own task
+    # Can reduce this for lower memory/faster training; Default 512
+    BATCH_SIZE_PER_IMAGE: 512
+  BACKBONE:
+    FREEZE_AT: 2 # Default 2
+DATASETS:
+  TRAIN: ("login_train_aug",)
+  TEST: ("login_test",)
+DATALOADER:
+  NUM_WORKERS: 0
+SOLVER:
+  IMS_PER_BATCH: 8 # Batch size; Default 16
+  BASE_LR: 0.001
+  # (2/3, 8/9)
+  STEPS: (26666, 35555) # The iteration number to decrease learning rate by GAMMA.
+  MAX_ITER: 40000 # Number of training iterations
+  CHECKPOINT_PERIOD: 4000 # Saves checkpoint every number of steps
+INPUT:
+  MIN_SIZE_TRAIN: (640, 672, 704, 736, 768, 800) # Image input sizes
+TEST:
+  # The period (in terms of steps) to evaluate the model during training.
+  # Set to 0 to disable.
+  EVAL_PERIOD: 2000
+OUTPUT_DIR: "./output/lr0.001_aug" # Specify output directory
+
+

crp_locator_utils/login_finder/configs/faster_rcnn_login_lr0.001_finetune.yaml

@@ -0,0 +1,34 @@
+_BASE_: "./bases/Base-RCNN-FPN.yaml"
+MODEL:
+  # COCO ResNet50 weights
+  WEIGHTS: "./output/lr0.001_aug/model_final.pth"
+  MASK_ON: False # Not doing segmentation
+  RESNETS:
+    DEPTH: 50 # ResNet50
+  ROI_HEADS:
+    NUM_CLASSES: 1 # Change to suit own task
+    # Can reduce this for lower memory/faster training; Default 512
+    BATCH_SIZE_PER_IMAGE: 512
+  BACKBONE:
+    FREEZE_AT: 2 # Default 2
+DATASETS:
+  TRAIN: ("login_train",)
+  TEST: ("login_test",)
+DATALOADER:
+  NUM_WORKERS: 0
+SOLVER:
+  IMS_PER_BATCH: 8 # Batch size; Default 16
+  BASE_LR: 0.001
+  # (2/3, 8/9)
+  STEPS: (12000, 16000) # The iteration number to decrease learning rate by GAMMA.
+  MAX_ITER: 18000 # Number of training iterations
+  CHECKPOINT_PERIOD: 4000 # Saves checkpoint every number of steps
+INPUT:
+  MIN_SIZE_TRAIN: (640, 672, 704, 736, 768, 800) # Image input sizes
+TEST:
+  # The period (in terms of steps) to evaluate the model during training.
+  # Set to 0 to disable.
+  EVAL_PERIOD: 2000
+OUTPUT_DIR: "./output/lr0.001_finetune" # Specify output directory
+
+

crp_locator_utils/login_finder/configs/faster_rcnn_login_lr0.01.yaml

@@ -0,0 +1,34 @@
+_BASE_: "./bases/Base-RCNN-FPN.yaml"
+MODEL:
+  # COCO ResNet50 weights
+  WEIGHTS: "https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/faster_rcnn_R_50_FPN_3x/137849458/model_final_280758.pkl"
+  MASK_ON: False # Not doing segmentation
+  RESNETS:
+    DEPTH: 50 # ResNet50
+  ROI_HEADS:
+    NUM_CLASSES: 1 # Change to suit own task
+    # Can reduce this for lower memory/faster training; Default 512
+    BATCH_SIZE_PER_IMAGE: 512
+  BACKBONE:
+    FREEZE_AT: 2 # Default 2
+DATASETS:
+  TRAIN: ("login_train",)
+  TEST: ("login_test",)
+DATALOADER:
+  NUM_WORKERS: 0
+SOLVER:
+  IMS_PER_BATCH: 8 # Batch size; Default 16
+  BASE_LR: 0.01
+  # (2/3, 8/9)
+  STEPS: (20000, 26666) # The iteration number to decrease learning rate by GAMMA.
+  MAX_ITER: 30000 # Number of training iterations
+  CHECKPOINT_PERIOD: 4000 # Saves checkpoint every number of steps
+INPUT:
+  MIN_SIZE_TRAIN: (640, 672, 704, 736, 768, 800) # Image input sizes
+TEST:
+  # The period (in terms of steps) to evaluate the model during training.
+  # Set to 0 to disable.
+  EVAL_PERIOD: 2000
+OUTPUT_DIR: "./output/lr0.01_nocheat" # Specify output directory
+
+

crp_locator_utils/login_finder/configs/faster_rcnn_login_lr0.01_aug.yaml

@@ -0,0 +1,34 @@
+_BASE_: "./bases/Base-RCNN-FPN.yaml"
+MODEL:
+  # COCO ResNet50 weights
+  WEIGHTS: "https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/faster_rcnn_R_50_FPN_3x/137849458/model_final_280758.pkl"
+  MASK_ON: False # Not doing segmentation
+  RESNETS:
+    DEPTH: 50 # ResNet50
+  ROI_HEADS:
+    NUM_CLASSES: 1 # Change to suit own task
+    # Can reduce this for lower memory/faster training; Default 512
+    BATCH_SIZE_PER_IMAGE: 512
+  BACKBONE:
+    FREEZE_AT: 2 # Default 2
+DATASETS:
+  TRAIN: ("login_train_aug",)
+  TEST: ("login_test",)
+DATALOADER:
+  NUM_WORKERS: 0
+SOLVER:
+  IMS_PER_BATCH: 8 # Batch size; Default 16
+  BASE_LR: 0.01
+  # (2/3, 8/9)
+  STEPS: (26666, 35555) # The iteration number to decrease learning rate by GAMMA.
+  MAX_ITER: 40000 # Number of training iterations
+  CHECKPOINT_PERIOD: 4000 # Saves checkpoint every number of steps
+INPUT:
+  MIN_SIZE_TRAIN: (640, 672, 704, 736, 768, 800) # Image input sizes
+TEST:
+  # The period (in terms of steps) to evaluate the model during training.
+  # Set to 0 to disable.
+  EVAL_PERIOD: 2000
+OUTPUT_DIR: "./output/lr0.01_aug" # Specify output directory
+
+

crp_locator_utils/login_finder/configs/faster_rcnn_login_lr0.01_finetune.yaml

@@ -0,0 +1,34 @@
+_BASE_: "./bases/Base-RCNN-FPN.yaml"
+MODEL:
+  # COCO ResNet50 weights
+  WEIGHTS: "https://dl.fbaipublicfiles.com/detectron2/COCO-Detection/faster_rcnn_R_50_FPN_3x/137849458/model_final_280758.pkl"
+  MASK_ON: False # Not doing segmentation
+  RESNETS:
+    DEPTH: 50 # ResNet50
+  ROI_HEADS:
+    NUM_CLASSES: 1 # Change to suit own task
+    # Can reduce this for lower memory/faster training; Default 512
+    BATCH_SIZE_PER_IMAGE: 512
+  BACKBONE:
+    FREEZE_AT: 2 # Default 2
+DATASETS:
+  TRAIN: ("login_train",)
+  TEST: ("login_test",)
+DATALOADER:
+  NUM_WORKERS: 0
+SOLVER:
+  IMS_PER_BATCH: 8 # Batch size; Default 16
+  BASE_LR: 0.01
+  # (2/3, 8/9)
+  STEPS: (4000, 5333) # The iteration number to decrease learning rate by GAMMA.
+  MAX_ITER: 6000 # Number of training iterations
+  CHECKPOINT_PERIOD: 4000 # Saves checkpoint every number of steps
+INPUT:
+  MIN_SIZE_TRAIN: (640, 672, 704, 736, 768, 800) # Image input sizes
+TEST:
+  # The period (in terms of steps) to evaluate the model during training.
+  # Set to 0 to disable.
+  EVAL_PERIOD: 2000
+OUTPUT_DIR: "./output/lr0.01_finetune" # Specify output directory
+
+

crp_locator_utils/login_finder/output/lr0.001_finetune/model_final.pth

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+size 329981096

phishpedia_siamese/domain_map.pkl

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+size 220978

phishpedia_siamese/expand_targetlist.zip

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+size 212573017