| --- |
| 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 |
|
|
