Upload detector2.yaml

configs/detector2.yaml

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+TASK: custom_laanet
+PRECISION: float32
+METRICS_BASE: binary
+SEED: 317
+DATA_RELOAD: False
+Resume: True
+begin_epoch: 100
+
+MODEL:
+  # PRETRAINED_PATH: '' 
+  type: PoseEfficientNet
+  model_name: efficientnet-b4
+  num_layers: B4
+  include_top: False
+  include_hm_decoder: True
+  head_conv: 64
+  use_c2: False
+  use_c3: True
+  use_c4: True
+  use_c51: True
+  efpn: True
+  tfpn: False
+  se_layer: False
+  heads:
+    hm: 1
+    cls: 1
+    cstency: 256
+  INIT_WEIGHTS:
+    pretrained: True
+    advprop: True
+
+DATASET:
+  type: BinaryFaceForensic
+  FROM_FILE: False
+  PIN_MEMORY: True
+  NUM_WORKERS: 7
+  COLOR_NORM: 'simple'  
+  mean: [0.485, 0.456, 0.406]
+  std: [0.229, 0.224, 0.225]
+  IMAGE_SUFFIX: png
+  COMPRESSION: c0
+  IMAGE_SUFFIX: png
+  IMAGE_SIZE: [384, 384]
+  HEATMAP_SIZE: [96, 96] #[IMAGE_SIZE//4, IMAGE_SIZE//4]
+  SIGMA: 2
+  ADAPTIVE_SIGMA: True
+  HEATMAP_TYPE: gaussian
+  SPLIT_IMAGE: False
+  DATA:
+    TYPE: frames
+    SAMPLES_PER_VIDEO: 
+      ACTIVE: True
+      TRAIN: 8
+      VAL: 8
+      TEST: 32
+    TRAIN:
+      NAME: custom_dataset
+      ROOT: ./datasets/train/
+      FROM_FILE: False
+      FAKETYPE: [fake]
+      LABEL_FOLDER: [real, fake]
+      
+    VAL:
+      NAME: custom_dataset
+      ROOT: ./datasets/test/
+      FROM_FILE: False
+      FAKETYPE: [fake]
+      LABEL_FOLDER: [real, fake]
+    TEST:
+      NAME: custom_dataset
+      ROOT: ./datasets/test/
+      FROM_FILE: False
+      FAKETYPE: [fake]
+      LABEL_FOLDER: [real, fake]
+  TRANSFORM:
+    geometry:
+      type: GeometryTransform
+      resize: [384, 384, 0] #h, w, p=probability. If no affine transform, set p=1
+      normalize: 0
+      horizontal_flip: 0.5
+      cropping: [0.15, 0.5] #Format: [crop_limit, prob]
+      scale: [0.15, 0.5] #Format: [scale_limit, prob]
+      rand_erasing: [0.5, 1] #Format: [p, max_count]
+    color:
+      type: ColorJitterTransform
+      clahe: 0.0
+      colorjitter: 0.3
+      gaussianblur: 0.3
+      gaussnoise: 0.3
+      jpegcompression: [0.5, 40, 100] # prob, lower and upper quality respectively
+      rgbshift: 0.3
+      randomcontrast: 0.0
+      randomgamma: 0.5
+      randombrightness: 1
+      huesat: 1
+    normalize:
+      mean: [0.5, 0.5, 0.5]
+      std: [0.5, 0.5, 0.5]
+
+TRAIN:
+  resume: True
+  gpus: [0]
+  pretrained_model: './logs/27-03-2025/PoseEfficientNet_custom_laanet_model_final.pth'
+  batch_size: 32
+  lr: 0.00005
+  epochs: 150
+  begin_epoch: 100
+  warm_up: 6
+  every_val_epochs: 1
+  loss:
+    type: CombinedFocalLoss
+    use_target_weight: False
+    cls_lmda: 1
+    dst_hm_cls_lmda: 0
+    offset_lmda: 0
+    hm_lmda: 100
+    cstency_lmda: 100
+    mse_reduction: sum
+    ce_reduction: mean
+  optimizer: SAM
+  distributed: False
+  tensorboard: False
+  resume: True
+  lr_scheduler:
+    # type: MultiStepLR
+    milestones: [5, 15, 20, 25]
+    gamma: 0.5
+  freeze_backbone: True
+  debug:
+    active: False
+    save_hm_gt: True
+    save_hm_pred: True
+
+TEST:
+  gpus: [0]
+  subtask: 'eval'
+  test_file: ''
+  vis_hm: True
+  threshold: 0.5
+  flip_test: True
+  video_level: True
+  pretrained: './training/weights/final_model.pth'