metadata
title: Wafer Defect Analysis
emoji: 🐠
colorFrom: red
colorTo: indigo
sdk: static
pinned: false
language: en
license: apache-2.0
tags:
- auto-scientist
- wafer-defect-detection
- VQA
- gemma-3
- semiconductor
metrics:
- quality-score: 8.4
- win-rate: 59%
- relative-improvement: 5.0%
Evaluation & Performance
Our fine-tuned adapter (uttarasawant/adaption-wafersage-wafermap-vqa) was benchmarked against the un-adapted base model on held-out test records.
https://huggingface.co/datasets/uttarasawant/adaption-wafersage-wafermap-vqa
Key Performance Metrics
- Quality Score: Improved from 8.0 to 8.4 (+5.0% relative improvement).
- Training Win Rates: Achieved a 59% win rate compared to the base model's 41% on domain-specific test sets.
- Percentile Ranking: Advanced from the 15.8th to the 19.2nd percentile.
- Evaluation Grade: Maintained a stable grade of B across multi-dimensional VQA rubrics.
Training Dynamics
The model underwent 44 global training steps featuring steady loss convergence and stable gradient norm stabilization.
Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference