https://github.com/jparedesds/fluorescent-penetrant-inspection

Detection of liquid penetrant test with AI (Yolo11)
https://github.com/jparedesds/fluorescent-penetrant-inspection

detection-model fpi huggingface inspection liquid lpi model opencv penetrant penetration-testing pt test torch torchvison yolo11

Last synced: 5 months ago
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Detection of liquid penetrant test with AI (Yolo11)

• Host: GitHub
• URL: https://github.com/jparedesds/fluorescent-penetrant-inspection
• Owner: jparedesDS
• License: mit
• Created: 2024-11-21T10:31:01.000Z (12 months ago)
• Default Branch: main
• Last Pushed: 2024-11-22T11:06:44.000Z (12 months ago)
• Last Synced: 2025-04-02T09:15:28.952Z (8 months ago)
• Topics: detection-model, fpi, huggingface, inspection, liquid, lpi, model, opencv, penetrant, penetration-testing, pt, test, torch, torchvison, yolo11
• Homepage: https://huggingface.co/jparedesDS/fluorescent-penetrant-inspection
• Size: 7.81 KB
• Stars: 2
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# Fluorescent penetrant inspection (FPI, DP, LPI, PT)

Detection of liquid penetrant test with AI (Yolo11)

### MODEL

- Yolo11l: https://huggingface.co/jparedesDS/liquid-penetrant-test-detection

#### Supported Labels

['defecto']

#### ALL my models YOLOv10 & YOLOv9

- Yolov9c: https://huggingface.co/jparedesDS/cs2-yolov9c

- Yolov10s: https://huggingface.co/jparedesDS/cs2-yolov10s

- Yolov10m: https://huggingface.co/jparedesDS/cs2-yolov10m

- Yolov10b: https://huggingface.co/jparedesDS/cs2-yolov10b

- Yolov10b: https://huggingface.co/jparedesDS/valorant-yolov10b

- Yolo11x: https://huggingface.co/jparedesDS/welding-defects-detection

#### How to use

```

from ultralytics import YOLO

# Load a pretrained YOLO model

model = YOLO(r'weights\yolo11l_LPI.pt')

# Run inference on 'image.png' with arguments

model.predict(

    'image.png',

    save=True,

    device=0

    )

```

#### Confusion matrix normalized

![confusion_matrix_normalized.png](https://cdn-uploads.huggingface.co/production/uploads/62e1c9b42e4cab6e39dafc97/cW-e-XzOwhyTbaNdCHEJc.png)

#### Labels

![labels.jpg](https://cdn-uploads.huggingface.co/production/uploads/62e1c9b42e4cab6e39dafc97/eVEQyEO6ddZVgPW9RdzOu.jpeg)

#### Results

![results.png](https://cdn-uploads.huggingface.co/production/uploads/62e1c9b42e4cab6e39dafc97/xgQm7K5mSHq9cDwDLW8Hz.png)

#### Predict

![val_batch1_labels.jpg](https://cdn-uploads.huggingface.co/production/uploads/62e1c9b42e4cab6e39dafc97/7n8JsgcalT6nAMgpAdB4V.jpeg)

![val_batch2_labels.jpg](https://cdn-uploads.huggingface.co/production/uploads/62e1c9b42e4cab6e39dafc97/81CEGAahhFqSZ7PaW-ai3.jpeg)

```

YOLO11l summary (fused): 464 layers, 25,280,083 parameters, 0 gradients, 86.6 GFLOPs

                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 12/12 [00:05<00:00,  2.32it/s]

                   all        836        752      0.794      0.771      0.793      0.379

```

#### Others models...

https://huggingface.co/jparedesDS/welding-defects-detection