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The fine-tuned Vision Transformer (ViT) model, initialized from google/vit-base-patch16-224 and named electronic-components-model, is specialized for classifying electronic components such as resistors, capacitors, inductors, and transistors. Initially pretrained on broader datasets, the fine-tuning process adjusts model parameters specifically for this custom dataset. This adaptation enhances the electronic-components-model's ability to accurately identify and classify intricate visual features unique to electronic components, improving its efficacy in practical applications requiring automated component recognition based on visual inputs.

  • Developed by: Chirag Pradhan
  • Funded by [optional]: Fatima Al-Fihri Predoctoral Fellowship
  • Shared by [optional]: Chirag Pradhan
  • Model type: Vision Transformer (ViT) for image classification
  • Language(s) (NLP): Not applicable (Image classification)
  • License: Apache License 2.0
  • Finetuned from model [optional]: google/vit-base-patch16-224

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Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

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Dataset used to train qipchip31/electronic-components-model