finetuning.yaml

num_workers: 32 # number of parallel threads to process the data
precision: float32 # supported options are [float32, bfloat16]
fine_tune : true
output_path: ./output/  # path to save the checkpoints or final model
tlt_wf_path: ./transfer-learning/workflows/vision_anomaly_detection
dataset:
  root_dir: ./data/  # full path of root directory of MVTEC dataset
  category_type: hazelnut # category type within MVTEC dataset, i.e. hazelnut or all (for running all categories in MVTEC)
  batch_size: 32 # inference batch size
  image_size: 224 # each image resized to this size (224x224)
model: 
  name: resnet50 # pretrained backbone model ..choices are [resnet50, resnet18]
  layer: layer3  # intermediate layer from which features will be extracted
  pool: 2 # pooling kernel size for average pooling
  feature_extractor: cutpaste # choices are [pretrained, cutpaste, simsiam]
#pretrained -  No fine-tuning on custom dataset, features will be extracted from pretrained ResNet model
#simsiam - fine-tune resnet model on custom dataset using simsiam self-supervised technique
#cutpaste - fine-tune resnet model on custom datset using cutpaste self-supervised technique
simsiam:
  batch_size: 64 # fine-tuning batch size
  epochs: 2 # number of epochs to fine-tune the model
  optim: 'sgd' # optimizer
  model_path: './output' # path to save the checkpoints or final model
  ckpt: true  # flag for whether intermediate checkpoints would be saved or not
  initial_ckpt:
cutpaste:
  cutpaste_type: '3way' # choices are ['normal', 'scar', '3way', 'union'] for image augmentation
  head_layer: 2 # number of perceptron layers appended towards the end of ResNet layers
  freeze_resnet: 20 # number of epochs till resnet layers will be frozen and only head layers will be trained
  batch_size: 64 # fine-tuning batch size
  epochs: 1 # number of epochs to fine-tune the model
  optim: 'sgd' # optimizer
  model_path: './output' # path to save the checkpoints or final model
  ckpt: true # flag for whether intermediate checkpoints would be saved or not
pca:
  pca_thresholds: 0.99 # PCA select number of components such that it ensures to retain the variance ratio specified