metadata

license: cc-by-nc-sa-4.0
extra_gated_fields:
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  email:
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tags:
  - objectdetection
  - anomaly
  - ai

Model Card for `rebotnix/rb_productInspection`

🏭 Industrial Product Anomaly Detection – Trained by KINEVA, Built by REBOTNIX, Germany Current State: in production and re-training.

This object detection model is specialized in identifying anomalies in manufactured industrial products, such as gears, valves, and machine parts. It has been trained on a curated dataset of common and uncommon industrial defects, the model aids in automated quality control and visual inspection workflows in manufacturing environments.

Developed and maintained by REBOTNIX, Germany, https://rebotnix.com

About KINEVA

KINEVA® is an automated training platform based on the MCP Agent system. It regularly delivers new visual computing models, all developed entirely from scratch. This approach enables the creation of customized models tailored to specific client requirements, which can be retrained and re-released as needed. The platform is particularly suited for applications that demand flexibility, adaptability, and technological precision—such as industrial image processing, smart city analytics, or automated object detection.

KINEVA is continuously evolving to meet the growing demands in the fields of artificial intelligence and machine vision. https://rebotnix.com/en/kineva

✈️ Example Predictions

Input Image	Detection Result


(More example visualizations coming soon)

Model Details

Architecture: RF-DETR (custom training head with optimized anchor boxes)
Task: Object Detection (Anomalies in manufactured products)
Trained on: REBOTNIX Industrial Defect Dataset (proprietary)
Format: PyTorch .pth + ONNX and trt export available on request
Backbone: EfficientNet B3 (adapted)
Training Framework: PyTorch + RF-DETR + custom augmentation

Chart

Dataset

The training dataset consists of high-resolution inspection images collected from:

Partner manufacturing lines
In-house simulated defect generation
Custom annotated bounding boxes by REBOTNIX team

The model was trained to be robust across:

Varying lighting and surface finishes
Small-scale defects and irregular shapes
Complex product geometries

Intended Use

✅ Intended Use	❌ Not Intended Use
Industrial visual inspection	Consumer photography analysis
Quality control automation	Medical imaging
Anomaly detection on gears and parts	Non-industrial object detection

Limitations

May produce false positives in visually noisy backgrounds
Performance may degrade on unseen product types not in the training data
Not trained for thermal or infrared imagery

Usage Example

import supervision as sv
from PIL import Image
from rfdetr import RFDETRBase

model_path= "./rb_productInspection.pth"
CLASS_NAMES = ["anomaly"]
model = RFDETRBase(pretrain_weights=model_path,num_classes=len(CLASS_NAMES))

image_path = "./example_productInspection1.jpg"
image = Image.open(image_path)

detections = model.predict(image, threshold=0.35)

labels = [
    f"{CLASS_NAMES[class_id]} {confidence:.2f}"
    for class_id, confidence
    in zip(detections.class_id, detections.confidence)
]

print(labels)

annotated_image = image.copy()
annotated_image = sv.BoxAnnotator().annotate(annotated_image, detections)
annotated_image = sv.LabelAnnotator().annotate(annotated_image, detections, labels)

annotated_image.save("./output_1.jpg")

Contact

📫 For commercial use or re-training this model support, or dataset access, contact:

REBOTNIX
✉️ Email: [email protected]
🌐 Website: https://rebotnix.com

License

This model is released under CC-BY-NC-SA unless otherwise noted. For commercial licensing, please reach out to the contact email.

Model Card for rebotnix/rb_productInspection