Instructions to use qipchip31/electronic-components-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use qipchip31/electronic-components-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="qipchip31/electronic-components-model") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("qipchip31/electronic-components-model") model = AutoModelForImageClassification.from_pretrained("qipchip31/electronic-components-model") - Notebooks
- Google Colab
- Kaggle
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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.
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- **Developed by:** Chirag Pradhan
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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.
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- **Developed by:** Chirag Pradhan
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