Setting parameters and configurations

Please set the following parameters in the config.yaml file:

num_workers: number of sub-processes or threads to use for data loading. Setting the argument num_workers as a positive integer will turn on multi-process data loading. (Default=32). It can be up to total number of threads available for processing.
precision: precision of data type in which model to be fine-tuned. Choices are [float32, bfloat16]
fine_tune: Not applicable when running the workflow independently from ref kit. Please refer to ref kit README for details.
output_path: path to save the checkpoints or final model
tlt_wf_path: Not applicable when running the workflow independently from ref kit. Please refer to ref kit README for details.
dataset:
- root_dir: path to the root directory of MVTEC dataset
- category_type: category type within MVTEC dataset, e.g.: hazelnut or all (for running all categories in MVTEC)
- batch_size: batch size for inference (Default=32)
- image_size: each image resized to this size (Default=224x224)
model: Options to select when running with a pre-trained backbone, no fine-tuning on custom dataset
- name: pretrained backbone model E.g.: resnet50, resnet18
- layer: intermediate layer from which features will be extracted
- pool: pooling kernel size for average pooling
- feature_extractor: select the type of modelling and subsequent feature extractor. Options are:
  - pretrained - No fine-tuning on custom dataset, features will be extracted from pretrained model which is set in model/name
  - simsiam - SimSiam self-supervised training on custom dataset
  - cutpaste - CutPaste self-supervised training on custom dataset
simsiam: Set when 'feature_extractor' is set to simsiam. For details about simsiam method, please refer to https://arxiv.org/abs/2011.10566
- batch_size: batch size for fine-tuning (Default=64)
- epochs: number of epochs to fine-tune the model
- optim: optimization algorithm E.g.: sgd, adam
- model_path: path to save the checkpoints or final model
- ckpt: flag to specify whether intermediate checkpoints should be saved or not
cutpaste: Set when 'feature_extractor' is set to cutpaste. For details about cutpaste method, please refer to https://arxiv.org/abs/2104.04015
- cutpaste_type: type of image augmentation for cutpaste fine-tuning, choices are ['normal', 'scar', '3way', 'union'].
- head_layer: number of fully-connected layers on top of average pooling layer followed by the last linear layer of backbone network
- freeze_resnet: number of epochs till only head layers will be trained. After this, complete network will be trained.
- batch_size: batch size for fine-tuning (Default=64)
- epochs: number of epochs to fine-tune the model
- optim: optimization algorithm E.g.: sgd, adam
- model_path: path to save the checkpoints or final model
- ckpt: flag to specify whether intermediate checkpoints should be saved or not
pca_thresholds: percentage of variance ratio to be retained. Number of PCA components are selected according to it