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README.md
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---
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license: mit
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---
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---
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language: en
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license: mit
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library_name: fastai
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pipeline_tag: image-classification
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tags:
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- fastai
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- timm
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- vision-transformer
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- image-classification
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- pc-parts
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widget:
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- src: https://images.unsplash.com/photo-1587202372775-a457f4ad61b9
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example_title: PC build example
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---
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# vit_base_patch16_pc_parts_classifier
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Vision Transformer image classifier for 11 PC component and cable-management classes.
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## Model Details
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- Architecture: ViT-Base Patch16 224 (`vit_base_patch16_224`)
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- Framework: FastAI + timm + PyTorch
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- Input size: 224x224 RGB
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- Classes: 11
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- Epochs: 15
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- Batch size: 16
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- Test accuracy: 0.7389
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- Training date: 2026-04-17
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## Labels
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1. AIO_Liquid_Cooler
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2. Air_Cooler
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3. Bad_Cable_Management
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4. CPU
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5. Good_Cable_Management
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6. Graphics_Card
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7. M2_NVMe_Drive
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8. Motherboard
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9. PC_Case
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10. Power_Supply
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11. RAM_Stick
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## Files
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- `best_model_export.pkl`: FastAI export for direct inference.
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- `best_model_state_dict.pth`: PyTorch state dict.
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- `best_model_metadata.json`: Training and class metadata.
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## Inference (FastAI)
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```python
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from fastai.learner import load_learner
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from pathlib import Path
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learn = load_learner("best_model_export.pkl")
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pred_class, pred_idx, probs = learn.predict(Path("sample.jpg"))
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print(pred_class)
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print({learn.dls.vocab[i]: float(probs[i]) for i in range(len(probs))})
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```
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## Live Demo
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A live inference demo is available on Hugging Face Spaces:
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- https://huggingface.co/spaces/shaanzeeeee/vit-base-pc-parts-inference
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