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Week1.md

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+# GSOC-25: Traffic Intersection Monitoring with OpenVINO
+
+This project develops a real-time system to detect traffic objects at intersections. It uses YOLOv11 and YOLOv12 deep learning models optimized with OpenVINO to identify vehicles, pedestrians, and traffic signs efficiently on Intel hardware.
+
+## Current Progress (Week 1)
+
+- Built the main detection pipeline
+- Tested different YOLO models for accuracy and speed
+- Created vehicle classification based on size and shape
+- Developed image processing and visualization tools
+- Added tracking to maintain object consistency between frames
+- Implemented filtering to remove false positives and overlapping detections
+
+## FeaturesD:\Downloads\finale6\khatam\qt_app_pyside
+
+- Train custom YOLOv12n models using traffic data from the COCO dataset
+- Convert models from PyTorch format to OpenVINO IR format
+- Quantize models to INT8 for faster inference without losing accuracy
+- Run detection on images, video files, and webcam streams
+- Detect common traffic classes such as cars, trucks, pedestrians, and traffic lights
+- Deploy models on CPU, GPU, and other OpenVINO-supported devices