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Dataset assetOpen Source CommunityDeep LearningDefect Detection
NEU-DET
A dataset for defect detection, using YOLOv8 and its enhanced models (including Coordinate Attention and Swin Transformer) for defect detection.
Source
github
Created
Dec 7, 2023
Updated
Dec 7, 2023
Signals
1,058 views
Availability
Linked source ready
Overview
Dataset description and usage context
Dataset Overview
Dataset Name
- Name: NEU‑DET
Purpose
- Purpose: Defect detection
Structure
- Location:
/root/autodl-tmp/ultralytics-main/data/NEU-DET - Splits:
- Training:
/root/autodl-tmp/ultralytics-main/data/NEU-DET/train - Testing:
/root/autodl-tmp/ultralytics-main/data/NEU-DET/test
- Training:
- Labels:
- Number: 6
- Names: [crazing, inclusion, patches, pitted_surface, rolled‑in_scale, scratches]
Pre‑processing
- Original format: XML files
- Target format: TXT files
- Conversion tool:
xml_to_txt.py(already executed)
Model & Algorithm
- Base model: YOLOv8
- Enhanced models: Include Coordinate Attention and Swin Transformer
- Training script:
train_final.py- Pre‑trained weights:
yolov8n.pt - Parameters: config
/autodl-tmp/ultralytics-main/data/data.yaml, 400 epochs, image size 640, device 0
- Pre‑trained weights:
Results
- Detection output: Visual results are shown in the README via image links
Conclusion
The NEU‑DET dataset is used for defect detection, combined with YOLOv8 and its enhanced variants for training and testing, enabling efficient data handling and model training.
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