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vit_base_patch16_pc_parts_classifier

Image Classification
fastai
timm
English
vision-transformer
pc-parts
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vit_base_patch16_pc_parts_classifier / README.md
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metadata 
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) 

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: