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This model card provides details about a fine - tuned Vision Transformer (ViT) model designed for classifying electronic components. It offers insights into the model's development, usage, training, and evaluation.
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Model Details
Model Description
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
Model Sources [optional]
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Uses
Direct Use
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Out - of - Scope Use
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Bias, Risks, and Limitations
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Recommendations
⚠️ Important Note
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
How to Get Started with the Model
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Training Details
Training Data
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Training Procedure
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Training Hyperparameters
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Evaluation
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Factors
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Results
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Summary
Model Examination [optional]
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Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
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Technical Specifications [optional]
Model Architecture and Objective
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Compute Infrastructure
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