negi3961/factory-defect-guard

🏭 Factory Defect Guard — YOLOv8 Industrial Defect Detection

Multi-domain industrial defect detection model trained on 29,000+ images across steel surfaces, PCBs, and industrial components. Detects 17 defect classes in a single forward pass.

Metric	Value
mAP@0.5	83.0% (V6_MC)
mAP@0.5:0.95	56.4%
Precision	78.8%
Recall	72.2%
Model size	22.5 MB
Input size	640×640

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🔍 Defect Classes (17)

Steel Surface (NEU Dataset) crazing · inclusion · patches · pitted_surface · rolled_in_scale · scratches

PCB Defects pcb_missing_hole · pcb_mouse_bite · pcb_open_circuit · pcb_short · pcb_spur · pcb_spurious_copper

Industrial Components (MVTec-derived) metal_nut_defect · screw_defect · transistor_defect · tile_defect · cable_defect

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🚀 Quick Start

from huggingface_hub import hf_hub_download
from ultralytics import YOLO

# Load model
model_path = hf_hub_download(
    repo_id  = "negi3961/factory-defect-guard",
    filename = "best_v6_mc.pt"   # MC Dropout version — best accuracy
)
model = YOLO(model_path)

# Run inference
results = model.predict("your_image.jpg", conf=0.25)
results[0].show()

# Get detections
for box in results[0].boxes:
    cls  = int(box.cls)
    conf = float(box.conf)
    name = model.names[cls]
    print(f"{name}: {conf:.2f}")

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📦 Model Files

File	Description	mAP@0.5
best_v6_mc.pt	Recommended — V6 fine-tuned with MC Dropout	0.830
best.pt	V6 base model	0.796

Use best_v6_mc.pt for production. best.pt is kept for reproducibility.

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🗂️ Training Details

Datasets Used

Dataset	Domain	Images
NEU Surface Defect Database	Steel surface	~1,800
PCB Defect (akhatova)	PCB original	~1,600
PCB Dataset (nakul8820)	PCB augmented	~2,000
PCB Defect (norbertelter)	PCB YOLO format	~10,668
MVTec AD subset	Industrial objects	~428
Magnetic Tile Defects	Tile surface	~2,688
Surface Defect (yidazhang07)	Mixed	~4,194
Total		~29,354

Training Config (V6)

model:     YOLOv8s
epochs:    60
imgsz:     640
batch:     16
optimizer: AdamW
lr0:       0.0001
mosaic:    1.0
mixup:     0.2
patience:  20
platform:  Kaggle GPU (Tesla T4)

Training Progression

Run	Epochs	mAP@0.5	Notes
V5	43	0.7477	Initial training
V6	60	0.7960	Full run, AdamW
V6_MC	+fine-tune	0.8300	MC Dropout added

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📊 Per-Class mAP@0.5

Class	mAP@0.5
tile_defect	99.5%
pcb_missing_hole	99.3%
pcb_short	95.5%
pcb_open_circuit	90.7%
patches	91.6%
pcb_spurious_copper	91.1%
pcb_mouse_bite	81.8%
metal_nut_defect	85.5%
inclusion	81.3%
scratches	80.7%
cable_defect	82.3%
rolled_in_scale	57.4%
screw_defect	56.8%
transistor_defect	54.0%
crazing	48.9%

Note: crazing is the hardest class — subtle surface texture variation makes it difficult to detect. tile_defect achieves near-perfect accuracy due to strong visual contrast.

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🛠️ Custom Inference Pipeline

from huggingface_hub import hf_hub_download
from ultralytics import YOLO
import cv2

CLASSES = [
    'crazing', 'inclusion', 'patches', 'pitted_surface',
    'rolled_in_scale', 'scratches', 'pcb_missing_hole',
    'pcb_mouse_bite', 'pcb_open_circuit', 'pcb_short',
    'pcb_spur', 'pcb_spurious_copper', 'metal_nut_defect',
    'screw_defect', 'transistor_defect', 'tile_defect', 'cable_defect'
]

model_path = hf_hub_download("negi3961/factory-defect-guard", "best_v6_mc.pt")
model = YOLO(model_path)

def inspect(image_path, conf_threshold=0.25):
    results = model.predict(image_path, conf=conf_threshold, verbose=False)
    detections = []
    for box in results[0].boxes:
        detections.append({
            "class": CLASSES[int(box.cls)],
            "confidence": round(float(box.conf), 3),
            "bbox": box.xyxy[0].tolist()
        })
    return detections

print(inspect("surface_sample.jpg"))

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📋 Requirements

ultralytics>=8.0.0
huggingface_hub
torch>=2.0.0
Pillow
opencv-python

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⚠️ Limitations

• Model trained on specific public benchmark datasets — real factory images may need fine-tuning
• crazing and transistor_defect classes have lower accuracy (~49–54%) and may produce false negatives on ambiguous textures
• Optimized for 640×640 input; very small defects on high-resolution industrial cameras may need tiling

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🔗 Links

• GitHub: github.com/chandanNegi39671/factory-defect-guard
• Training Notebook: Kaggle (YOLOv8s, Tesla T4) (https://www.kaggle.com/code/negi1586/notebookce295c43f7)

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👤 Author

Negi — ML Engineer
HuggingFace: @negi3961