README.md

metadata

title: Casting Defect Detection
emoji: 🔍
colorFrom: green
colorTo: gray
sdk: gradio
sdk_version: 5.33.0
app_file: app.py
pinned: false
license: mit
models:
  - gfichetdc/casting-defect-vit

Casting Defect Detection (ViT)

Fine-tuned ViT-B/16 on 6,633 casting surface images to classify parts as defective or ok.

Metric	Value
Macro F1	0.995
Accuracy	99.6%
Base model	google/vit-base-patch16-224
Training images	6,633
Test images	715
Epochs	3

Usage

from transformers import pipeline

classifier = pipeline("image-classification", model="gfichetdc/casting-defect-vit")
result = classifier("path/to/casting_image.jpeg")

Dataset

Kaggle — Casting Product Image Data for Quality Inspection: 7,348 grayscale images of submersible pump impellers.

Training

• Fine-tuned google/vit-base-patch16-224 with HuggingFace Trainer
• lr=2e-5, batch size 16, 3 epochs on RTX 3060 (~10 min)
• Experiment tracking with MLflow