Real-time object detection system for autonomous driving using YOLOv5 and ONNX Runtime. Built with Streamlit for interactive visualization of vehicle, pedestrian, and traffic signal detection. Optimized for CPU inference with Tesla-inspired UI/UX design. Opencv, Pytorch,Tesla,vehicle-detection,traffic-signal-detection,single-stage-detector

Creation of a model based on yolov8 that uses the file downloaded from https://www.kaggle.com/datasets/valentynsichkar/traffic-signs-dataset-in-yolo-format/data as a custom dataset to detect traffic signs. The detected signals can be recognized using the project https://github.com/ablanco1950/RecognizeTrafficSign.

🚗 Implement real-time object detection for autonomous driving using YOLOv5 and ONNX Runtime, featuring a sleek Tesla-inspired Streamlit interface.

Traffic signal identification using Keras LeNet architecture. Identify 43 different classes of images with over 90% accuracy.

A fine tuned YOLO11 model up to 100 epochs. This custom dataset based fine tuned yolo11s is down streamed on the task of traffic signals detection in both images, videos. Furthermore, the model has been exported to the ONNX format as well. You may export it to your desired serialization format.