Visionds

configs/env.yml

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-root_dir: /path/where/to/store/results/
+root_dir: /content/data/

configs/pretext/simclr_birds.yml

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+# Setup
+setup: simclr
+
+# Model
+backbone: resnet50
+model_kwargs:
+   head: mlp
+   features_dim: 2048
+
+# Dataset
+train_db_name: birds
+val_db_name: birds
+num_classes: 200
+
+# Loss
+criterion: simclr
+criterion_kwargs:
+   temperature: 0.07
+
+# Hyperparameters
+epochs: 100
+optimizer: sgd
+optimizer_kwargs:
+   nesterov: False
+   weight_decay: 0.0001
+   momentum: 0.9
+   lr: 0.005625
+scheduler: cosine
+scheduler_kwargs:
+   lr_decay_rate: 0.1
+batch_size: 48
+num_workers: 8
+
+# Transformations
+augmentation_strategy: simclr
+augmentation_kwargs:
+   random_resized_crop:
+      size: 224
+      scale: [0.2, 1.0]
+   color_jitter_random_apply:
+      p: 0.8
+   color_jitter:
+      brightness: 0.4
+      contrast: 0.4
+      saturation: 0.4
+      hue: 0.1
+   random_grayscale:
+      p: 0.2
+   normalize:
+      mean: [0.485, 0.456, 0.406]
+      std: [0.229, 0.224, 0.225]
+
+transformation_kwargs:
+   crop_size: 224
+   normalize:
+      mean: [0.485, 0.456, 0.406]
+      std: [0.229, 0.224, 0.225]

configs/pretext/simclr_mnist.yml

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+# Setup
+setup: simclr
+
+# Model
+backbone: resnet18
+model_kwargs:
+   head: mlp
+   features_dim: 128
+
+# Dataset
+train_db_name: mnist
+val_db_name: mnist
+num_classes: 10
+
+# Loss
+criterion: simclr
+criterion_kwargs:
+   temperature: 0.1
+
+# Hyperparameters
+epochs: 5
+optimizer: sgd
+optimizer_kwargs:
+   nesterov: False
+   weight_decay: 0.0001
+   momentum: 0.9
+   lr: 0.4
+scheduler: cosine
+scheduler_kwargs:
+   lr_decay_rate: 0.1
+batch_size: 48
+num_workers: 8
+
+# Transformations
+augmentation_strategy: simclr
+augmentation_kwargs:
+   random_resized_crop:
+      size: 32
+      scale: [0.2, 1.0]
+   color_jitter_random_apply:
+      p: 0.8
+   color_jitter:
+      brightness: 0.4
+      contrast: 0.4
+      saturation: 0.4
+      hue: 0.1
+   random_grayscale:
+      p: 0.2
+   normalize:
+      mean: [0.5]
+      std: [0.5]
+
+transformation_kwargs:
+   crop_size: 32
+   normalize:
+      mean: [0.5]
+      std: [0.5]

configs/scan/scan_birds.yml

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+# setup
+setup: scan
+
+# Loss
+criterion: scan
+criterion_kwargs:
+   entropy_weight: 5.0
+
+# Model
+backbone: resnet50
+
+# Weight update
+update_cluster_head_only: True # Train only linear layer during SCAN
+num_heads: 10 # Use multiple heads
+
+# Dataset
+train_db_name: birds
+val_db_name: birds
+num_classes: 200
+num_neighbors: 50
+
+# Transformations
+augmentation_strategy: simclr
+augmentation_kwargs:
+   random_resized_crop:
+      size: 224
+      scale: [0.2, 1.0]
+   color_jitter_random_apply:
+      p: 0.8
+   color_jitter:
+      brightness: 0.4
+      contrast: 0.4
+      saturation: 0.4
+      hue: 0.1
+   random_grayscale:
+      p: 0.2
+   normalize:
+      mean: [0.485, 0.456, 0.406]
+      std: [0.229, 0.224, 0.225]
+
+transformation_kwargs:
+   crop_size: 224
+   normalize:
+      mean: [0.485, 0.456, 0.406]
+      std: [0.229, 0.224, 0.225]
+
+# Hyperparameters
+optimizer: sgd
+optimizer_kwargs:
+   lr: 5.0
+   weight_decay: 0.0000
+   nesterov: False
+   momentum: 0.9
+epochs: 10
+batch_size: 32
+num_workers: 8
+
+# Scheduler
+scheduler: constant

configs/scan/scan_mnist.yml

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+# setup
+setup: scan
+
+# Loss
+criterion: scan
+criterion_kwargs:
+   entropy_weight: 5.0
+
+# Weight update
+update_cluster_head_only: False # Update full network in SCAN
+num_heads: 1 # Only use one head
+
+# Model
+backbone: resnet18
+
+# Dataset
+train_db_name: mnist
+val_db_name: mnist
+num_classes: 10
+num_neighbors: 20
+
+# Transformations
+augmentation_strategy: ours
+augmentation_kwargs:
+   crop_size: 32
+   normalize:
+      mean: [0.5]
+      std: [0.5]
+   num_strong_augs: 4
+   cutout_kwargs:
+     n_holes: 1
+     length: 16
+     random: True
+
+transformation_kwargs:
+   crop_size: 32
+   normalize:
+      mean: [0.5]
+      std: [0.5]
+
+# Hyperparameters
+optimizer: adam
+optimizer_kwargs:
+   lr: 0.0001
+   weight_decay: 0.0001
+epochs: 5
+batch_size: 48
+num_workers: 8
+
+# Scheduler
+scheduler: constant

configs/selflabel/selflabel_birds.yml

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+# setup
+setup: selflabel
+
+# Threshold
+confidence_threshold: 0.99
+
+# EMA
+use_ema: True
+ema_alpha: 0.999
+
+# Loss
+criterion: confidence-cross-entropy
+criterion_kwargs:
+   apply_class_balancing: False
+
+# Model
+backbone: resnet50
+num_heads: 1
+
+# Dataset
+train_db_name: birds
+val_db_name: birds
+num_classes: 200
+
+# Transformations
+augmentation_strategy: ours
+augmentation_kwargs:
+   crop_size: 224
+   normalize:
+      mean: [0.485, 0.456, 0.406]
+      std: [0.229, 0.224, 0.225]
+   num_strong_augs: 4
+   cutout_kwargs:
+     n_holes: 1
+     length: 75
+     random: True
+
+transformation_kwargs:
+   crop_size: 224
+   normalize:
+      mean: [0.485, 0.456, 0.406]
+      std: [0.229, 0.224, 0.225]
+
+# Hyperparameters
+optimizer: sgd
+optimizer_kwargs:
+   lr: 0.03
+   weight_decay: 0.0
+   nesterov: False
+   momentum: 0.9
+epochs: 10
+batch_size: 32
+num_workers: 8
+
+# Scheduler
+scheduler: constant

configs/selflabel/selflabel_mnist.yml

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+# setup
+setup: selflabel
+
+# ema
+use_ema: False
+
+# Threshold
+confidence_threshold: 0.99
+
+# Criterion
+criterion: confidence-cross-entropy
+criterion_kwargs:
+   apply_class_balancing: True
+
+# Model
+backbone: resnet18
+num_heads: 1
+
+# Dataset
+train_db_name: mnist
+val_db_name: mnist
+num_classes: 10
+
+# Transformations
+augmentation_strategy: ours
+augmentation_kwargs:
+   crop_size: 32
+   normalize:
+      mean: [0.5]
+      std: [0.5]
+   num_strong_augs: 4
+   cutout_kwargs:
+     n_holes: 1
+     length: 16
+     random: True
+
+transformation_kwargs:
+   crop_size: 32
+   normalize:
+      mean: [0.5]
+      std: [0.5]
+
+# Hyperparameters
+epochs: 5
+optimizer: adam
+optimizer_kwargs:
+   lr: 0.0001
+   weight_decay: 0.0001
+batch_size: 48
+num_workers: 8
+
+# Scheduler
+scheduler: constant