---
library_name: transformers
tags:
- electronics
- sciences
- components
license: apache-2.0
datasets:
- qipchip31/electronic_components
language:
- en
metrics:
- accuracy
---

# Model Card for Model ID

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## Model Details

### Model Description

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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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## Training Details

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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

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