# Steel defect detection
URL: https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/model-eval-metrics/steel-plate.html

> Train a highly accurate and robust machine learning model capable of detecting and classifying any-sized scratch present in steel plates.

# Steel defect detection

[Access this AI accelerator on GitHub](https://github.com/datarobot-community/ai-accelerators/blob/main/advanced_ml_and_api_approaches/faster-rcnn-custom-model/FasterR-CNN_training.ipynb)

Modern machine learning techniques are now capable of assisting manufacturers streamline their product development in numerous ways. In this notebook, you are going to focus on detecting and classifying product defects using state of the art computer vision systems. Utilizing machine learning brings immense value to manufacturers, transforming their production processes and giving them an overall competitive edge. By leveraging these advanced methods, manufacturers can streamline product development, enhance defect detection accuracy, optimize operational efficiency, reduce costs, and ultimately deliver higher-quality products to meet the ever-growing demands of the market.

In this accelerator, you will leverage computer vision to tackle the task of identifying product defects in hot-rolled steel plates, which are used extensively in construction and agriculture due to their superior strength and high formability. By leveraging an object detection model powered by machine learning, we can achieve precise and efficient detection and classification of one of the most prevalent product defects that steel manufacturers encounter: scratches.

In practical applications, the inspection of steel plates is performed visually by an in-factory human examiner, which is time consuming and potentially unreliable. The approach will stand out from traditional techniques that do not utilize machine learning, as it offers the ability to automate the detection process, enhance accuracy, and reduce human effort and error.

- Download the data
- Perform the necessary data preprocessing
- Split the data into training and validation datasets
- Create our model
- Write custom training and validation loops
- Create a visualizer to evaluate model performance and take a look at the model's predictions

At the end, you will have successfully trained a highly accurate and robust machine learning model capable of detecting and classifying any sized scratch present in steel plates.

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