OpenDefectKit

Industrial Computer Vision Infrastructure for Python

OpenDefectKit is the missing infrastructure layer for industrial defect detection AI.

Every factory AI engineer rebuilds the same pipelines — annotation converters, augmentation scripts, dataset validators, edge exporters. OpenDefectKit ships all of it as a single pip install.

Install

pip install opendefectkit                    # core
pip install opendefectkit[benchmark]         # + model evaluation (requires PyTorch)
pip install opendefectkit[deploy]            # + ONNX edge export
pip install opendefectkit[all]               # everything

Quick Start

from opendefectkit.pipeline import DefectPipeline

pipeline = DefectPipeline(
    raw_data_dir="data/raw_annotations",
    output_dir="data/processed",
)
pipeline.run(steps=[
    "detect_format",
    "validate_annotations",
    "fix_annotations",
    "convert_to_yolo",
    "profile_dataset",
    "export_report",
])
# HTML report written to data/processed/dataset_report.html

Modules

Module	What it does	Key classes
opendefectkit.convert	Convert annotations between COCO, YOLO, VOC, LabelMe, CSV	detect_format, auto_detect_and_convert, convert_with_label_map, coco_to_yolo
opendefectkit.validate	Find and fix corrupt, OOB, duplicate, and missing annotations	AnnotationValidator, AnnotationFixer
opendefectkit.augment	Synthetic defect generation and industrial augmentation	SyntheticDefectGenerator, IndustrialAugPipeline
opendefectkit.analyze	Dataset profiling, health scoring, and HTML reports	DatasetProfiler, DatasetHealthScore, DatasetVisualizer, SeverityScorer
opendefectkit.taxonomy	35-type defect registry with label normalization	DefectTaxonomy, DefectType
opendefectkit.benchmark	Model evaluation, multi-model comparison, industry reports	DefectBenchmark, ModelComparison, IndustryReportGenerator
opendefectkit.deploy	ONNX export and edge device optimization	ONNXExporter, EdgeOptimizer, DEVICE_PROFILES
opendefectkit.pipeline	One-command orchestrator for the full workflow	DefectPipeline

CLI

# Detect annotation format
opendefectkit convert --input data/annotations/ --target yolo --output data/yolo/

# Profile a dataset
opendefectkit analyze --dataset data/yolo/ --output report.html

# Validate annotations
opendefectkit validate --dataset data/yolo/

# Generate synthetic defects
opendefectkit augment --images data/clean/ --defect crack --samples 200 --output data/augmented/

# Benchmark a model
opendefectkit benchmark --model runs/train/best.pt --dataset data/test/ --output benchmark.json

Defect Taxonomy

35 defect types across 6 categories: Surface Cracks, Corrosion, Surface Defects, Weld Defects, Coating Defects, Dimensional.

from opendefectkit.taxonomy import DefectTaxonomy

tax = DefectTaxonomy()

# Normalize raw labels from any source dataset
mapping = tax.map_labels(["rusty", "fracture", "pore", "undercutting"], method="fuzzy_match")
# {"rusty": "surface_rust", "fracture": "structural_crack", "pore": "porosity", "undercutting": "undercut"}

defect = tax.standardize("rust spot")
print(defect.id, defect.severity_class)  # CO-001  medium

weld_defects = tax.list_defects("Weld Defects")
# [DefectType(id='WD-001', name='porosity', ...), ...]

Synthetic Augmentation

from opendefectkit.augment import SyntheticDefectGenerator, IndustrialAugPipeline

# Generate crack images with paired YOLO labels
gen = SyntheticDefectGenerator(seed=42)
gen.add_cracks(
    clean_images_dir="data/clean/",
    output_dir="data/synthetic/cracks/",
    num_samples=500,
    crack_types=["hairline", "structural", "fatigue"],
    severity_range=(0.1, 0.9),
)

# Apply industrial augmentation transforms
pipeline = IndustrialAugPipeline(
    transforms=["random_lighting", "motion_blur", "industrial_noise", "jpeg_compression"]
)
pipeline.run(
    input_dir="data/synthetic/cracks/",
    output_dir="data/augmented/",
    multiplier=5,  # 5 augmented versions per image
)

Available transforms: random_lighting, motion_blur, jpeg_compression, perspective_warp, industrial_noise, surface_reflection.

Dataset Health Score

from opendefectkit.analyze import DatasetHealthScore

result = DatasetHealthScore("data/yolo/").run()
print(result)
# Dataset Health Score: 75/100
#
#   [PASS] (20pt) Min samples per class >= 100
#          Min samples: 312 check
#   [FAIL] (15pt) Validation split exists
#          No val/ or valid/ subdirectory
#   [PASS] (10pt) Duplicate images < 1%
#          Duplicates: 2/500 (0.4%)
#   [WARN] (20pt) Class imbalance ratio < 10:1
#          Imbalance ratio: 8.3:1 (need < 10:1)
#   ...
# Recommendation: To improve dataset quality: create a val/ subdirectory.

Edge Deployment

from opendefectkit.deploy import ONNXExporter, EdgeOptimizer, DEVICE_PROFILES

# Export YOLOv8 model to ONNX
exporter = ONNXExporter("runs/train/best.pt")
exporter.export(
    output_path="deploy/model.onnx",
    input_size=(640, 640),
    optimize_for="edge",
    quantize=True,
)

# Get device-specific recommendations
optimizer = EdgeOptimizer("deploy/model.onnx")
result = optimizer.profile_device("jetson_nano").optimize(target_latency_ms=33.0)
print(result.recommendations)
# ["Recommended input size: (416, 416)", "Target FPS: 30.0"]

optimizer.generate_deployment_package("deploy/package/")
# Creates deploy/package/deployment_package.zip with model + inference script + requirements.txt

Available device profiles: jetson_nano, jetson_orin, raspberry_pi_4, intel_nuc, generic_x86.

Who Uses This

Factory AI engineers who are tired of rebuilding:

• Annotation format converters for every new dataset
• Synthetic defect generators when real defect data is scarce
• Dataset validators that find the corrupt annotations before training
• Edge exporters that work with Jetson Nano and Raspberry Pi

Contributing

Fork the repo. To add defect types, extend the DEFECT_REGISTRY list in opendefectkit/taxonomy/defects.py following the existing DefectType dataclass pattern. To add format support, implement read_<format> in opendefectkit/convert/ and wire it into _converters.py. All contributions require tests in tests/. PR welcome.

License

MIT