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A comparitive study of CNN vs ViT architectures for the purpose of anomaly detection and defect classification in low-resolution leather surface images.

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Leather-Defect-Detection

Description: A comparative study of CNN vs ViT architectures for the purpose of anomaly detection and defect classification in low-resolution leather surface images.

Models include:

  • EfficientNet-B0 - Model modified for required input and output tensors/dimensions.
  • Inception-V3 - Model modified for required input and output tensors/dimensions.
  • ResNet-50 - Model modified for required input and output tensors/dimensions.
  • Vision Transformer 01 - Dataset and preprocessing adapted to the leather dataset, original model structure adhered to.
  • Vision Transformer 02 - ViT Model structure modified for optimised results based on the same modified leather dataset.

Datasets:

  • MVTec AD - Small but high-res defect benchmark dataset with annotated ground truths (Leather module only)
  • Kaggle - Low-resolution open-source Leather Defect Dataset

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A comparitive study of CNN vs ViT architectures for the purpose of anomaly detection and defect classification in low-resolution leather surface images.

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