Instructions to use dkrak737/battery-ct-defect-models with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ultralytics
How to use dkrak737/battery-ct-defect-models with ultralytics:
# Couldn't find a valid YOLO version tag. # Replace XX with the correct version. from ultralytics import YOLOvXX model = YOLOvXX.from_pretrained("dkrak737/battery-ct-defect-models") source = 'http://images.cocodataset.org/val2017/000000039769.jpg' model.predict(source=source, save=True) - Notebooks
- Google Colab
- Kaggle
metadata
license: mit
library_name: ultralytics
tags:
- yolo
- ultralytics
- battery
- ct
- defect-detection
- segmentation
- classification
배터리 CT 내부결함 탐지 — 가중치
기내 화재(내부 단락) 예방용 배터리 CT 결함 스크리닝 모델 묶음. recall 우선.
| 파일 | task | 결함 | 비고 |
|---|---|---|---|
module_r01c.pt |
detect | porosity, resin overflow | module pouch, imgsz 512, conf 0.05 |
cell_r06.pt |
detect | porosity | cell pouch, imgsz 640, 검토 큐 |
swell_kf0~4.pt |
classify | swelling | 5-fold 앙상블, normal=0/swelling=1, imgsz 224 |
porosity_best.pt |
segment | porosity | 정밀 마스크, cell 4x 타일 |
데모/코드: https://github.com/dkrak737-svg/battery-ct-defect-screening 데이터: AI-Hub 103.배터리 불량 이미지(71687), CT만. 원본 미배포(약관).