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
Update README.md
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README.md
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@@ -30,8 +30,8 @@ The fine-tuned Vision Transformer (ViT) model, initialized from `google/vit-base
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- **Developed by:** Chirag Pradhan
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- **Funded by [optional]:** Fatima Al-Fihri Predoctoral Fellowship
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- **Shared by [optional]:** Chirag Pradhan
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- **Model type:**
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- **Language(s) (NLP):**
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- **License:** Apache License 2.0
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- **Finetuned from model [optional]:** google/vit-base-patch16-224
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- **Developed by:** Chirag Pradhan
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- **Funded by [optional]:** Fatima Al-Fihri Predoctoral Fellowship
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- **Shared by [optional]:** Chirag Pradhan
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- **Model type:** Vision Transformer (ViT) for image classification
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- **Language(s) (NLP):** Not applicable (Image classification)
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- **License:** Apache License 2.0
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- **Finetuned from model [optional]:** google/vit-base-patch16-224
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