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