YOLO-NAS + SAM

This project demonstrates how to perform object detection and image segmentation using YOLO-NAS for object detection and SAM for image segmentation. YOLO-NAS developed by DeciAi is a state-of-the-art object detection model optimized for both accuracy and low-latency inference. SAM, on the other hand, is a powerful segmentation model developed by Meta AI. Code: YOLO-NAS + SAM

Vehicle Detection + Tracking App

Streamlit web application for vehicle tracking using different SOTA object detection models. The app offers two options: YOLO-NAS with SORT tracking and YOLOv8 with ByteTrack and Supervision tracking. It enables users to upload a video file, set confidence levels, and visualize the tracking results in real-time. Code: Vehicle Detection + Tracking App

YOLO-NAS & EasyOCR Automatic Number Plate Recognition

This project uses YOLO-NAS and EasyOCR to detect license plates and perform Optical Character Recognition (OCR) on them. The project includes both image and video processing capabilities and has been deployed as a Streamlit web application. This is an update to a previous project, Optical-Character-Recognition-WebApp

Face Mask Detection YOLOv7

Object Detection project created to detect face masks using YOLOv7 trained on a custom dataset. All 853 images were manually annotated using labelimg, two labels were used to classify the images, “Mask” and “No Mask”. The training was performed over 300 epochs and a batch size of 8 using google colab in the YOLOv7 Training.ipynb file.

Pnuemonia Classifcation (PyTorch Lightning)

Developed and evaluated two models, to detect pneumonia cases from medical images. Our custom resnet18 was evaluated at an 81% accuracy, 66% precision, and 78% recall. Valuable for timely detection of pneumonia patients, improving outcomes, and reducing mortality. CAM visualizations provide provide insights into model decision-making

Lung Cancer Segmentation (U-Net)

The purpose of this project is to enhance lung cancer diagnosis and treatment through automatic tumor segmentation, employing advanced algorithms for precise and efficient detection. I was able to Developed a Lung Cancer Segmentation model using the U-Net architecture and PyTorch Lightning framework. Achieved an unimpressive dice loss of 0.0247 more work is required.

YOLOv5 + InceptionResNetV2 Optical Character Recognition WebApp

This is a web application for detecting license plates and extracting text using Optical Character Recognition (OCR) technology. The application is built using Python, OpenCV, Tensorflow, Ultralytics YOLOv5, LabelImg, Pytesseract, InceptionResNetV2, Streamlit and Flask. Code: Optical Character Recognition WebApp