Introduction

This project focuses on enhancing defect detection and classification in printed circuit boards (PCBs) using advanced machine learning methodologies and computer vision techniques. Traditional manual inspection methods are both time-consuming and prone to errors, which inspired the development of this automated system. The project was completed using a dataset of over 10,000 annotated PCB images. Tools and libraries such as VGG16, RES-UNET, and YOLOv5 were employed. The project is hosted on https://pcb-vision.streamlit.app, showcasing the implemented solution.

Objectives

The primary objective was to improve the quality control process in PCB manufacturing by automating defect detection. The initial challenge was the inefficiency of manual inspection methods, which prompted the exploration of deep learning models for more accurate and efficient defect classification. Essential considerations included selecting appropriate machine learning models, preprocessing the dataset effectively, and fine-tuning the models to achieve optimal performance.

Results

The project successfully demonstrated that deep learning models can significantly enhance defect detection in PCBs. The RES-UNET model achieved high accuracy in defect segmentation and classification, while YOLOv5 showed efficient defect detection capabilities. The results indicated a robust system capable of detecting and classifying defects with high precision. Key learnings include the effectiveness of combining different models and the importance of extensive dataset preprocessing. The project's outcomes have the potential to impact future quality control processes in PCB manufacturing, offering a scalable solution for defect detection.

The "Magnetic-Tile-Surface-Defects" project aims to enhance the detection and analysis of surface defects in magnetic tiles. As magnetic tiles are commonly used in various applications, ensuring their quality is crucial. Traditional inspection methods can be inadequate, especially with increasing complexity and scale. This project was pursued to leverage advanced deep learning techniques to automate defect detection, making the process more accurate and efficient. The project was conducted independently, with credits to the open-source community for the tools and libraries used.

Objectives

The primary objectives of this project were:

• To develop a robust system for detecting and classifying six different types of surface defects in magnetic tiles.

• To utilize the SegFormer model for defect detection, employing PyTorch for model implementation.

• To process data efficiently using data loaders and to figure out which loss parameter to use for fine tuning. I chose to apply BCE (Binary Cross-Entropy) loss for model training.

• To improve the accuracy and efficiency of defect detection compared to traditional methods.

Results

The project successfully implemented a defect detection system using the SegFormer model, achieving notable results:

• The SegFormer model, combined with PyTorch, effectively detected and classified the six types of surface defects in magnetic tiles.

• The use of data loaders facilitated efficient data handling and processing, while BCE loss contributed to the model’s accurate performance.

• The system demonstrated significant improvements in defect detection accuracy and efficiency, compared to traditional inspection methods.

• Insights gained include the effectiveness of the SegFormer model in handling complex defect patterns and the benefits of using BCE loss for binary classification tasks.

Overall, this project contributes to enhancing the quality control processes in industries using magnetic tiles, potentially influencing future advancements in automated defect detection technologies.

The "NLI-2-Sentence-Relation" project focuses on Natural Language Inference (NLI) to determine the relationships between pairs of sentences. Understanding the relationships between sentences is crucial for various natural language processing tasks, such as text comprehension and sentiment analysis. This project was initiated to explore the capabilities of modern NLP techniques in inferring sentence relationships. The project was carried out independently, with significant contributions from Hugging Face Transformers and other NLP libraries. 

Objectives

The primary objectives of this project were:

• To develop a Natural Language Inference model capable of determining the relationships between pairs of sentences.

• To leverage Hugging Face Transformers and other NLP libraries for model training and evaluation.

• To improve the accuracy and reliability of sentence relationship inference in natural language processing tasks.

Results

The project achieved the following outcomes:

• The Natural Language Inference model was successfully developed and demonstrated effective determination of sentence pair relationships.

• Utilized Hugging Face Transformers and NLP libraries contributed to a robust and accurate model performance.

• The model showed promising results in evaluating sentence relationships, with insights into the strengths and limitations of different NLP approaches.

Overall, this project enhances the understanding of sentence relationships in natural language processing, providing a foundation for further advancements in text comprehension and analysis.