Ashhadul Islam

Ashhadul Islam

Harnessing AI to bridge visionary innovation with real-world impact.

Detecting Road Damages From Image And Video

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In this post, I take you through the process of building an object detection model using YOLOv5, specifically for detecting road damages from images and videos. This project demonstrates how to leverage transfer learning by training a deep neural network on your own dataset, allowing the model to detect specific objects of interest, such as abnormalities in X-Ray images or road damages in surveillance footage.

The tutorial covers the entire workflow, from data preprocessing to model training and finally, deploying the model using a Streamlit web app. This web app allows users to upload an image or video of the road, and the application outputs the processed media with bounding boxes indicating detected damages.

To read the entire article, visit the link.

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Balancing Regression Datasets with KNNOR-Reg Oversampling Technique

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MemGPT: Assimilating Information from Multiple PDFs

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In this post, I explore the capabilities of MemGPT in handling Retrieval-Augmented Generation (RAG) tasks, particularly focusing on its ability to assimilate information from multiple PDF files. While GPT-4 has its strengths, it often struggles with tasks requiring the consolidation of information from several documents. MemGPT, on the other hand, excels in this area, as demonstrated through a series of comparisons.

Advanced Retrieval with LlamaPacks: Elevating RAG in Fewer Lines of Code!

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In this post, I explore five Retrieval-Augmented Generation (RAG) methods open-sourced by LlamaIndex, which claim to simplify the RAG process to just about one line of code. Through a series of experiments, I test these methods and compare their effectiveness in terms of accuracy and efficiency.