Implementing Object Detection with YOLO Algorithm
Program Project Program Identification Program ID: T5 Program Title: Computer Vision Program Information: Project Title: Implementing Object Detection with YOLO Algorithm Description Project Overview: This project focuses on implementing object detection using the YOLO (You Only Look Once) algorithm on a chosen dataset. Trainees will learn how to set up and fine-tune a pretrained YOLO model for object detection tasks, evaluate its performance, and demonstrate real-time object detection on streaming video data. Group Number: Match capstone project team Project Content Outline: 1. • • • YOLO Algorithm Implementation (30 marks): Utilize a pretrained YOLO model (e.g., YOLOv4, YOLOv5) with the latest version available. Fine-tune the pretrained model on a chosen object detection dataset, such as COCO or VOC. Optimize the model parameters and hyperparameters as needed for the specific dataset and task. 1 – V 1.0/05032024 2. Evaluation of YOLO Model (20 marks): • Evaluate the performance of the fine-tuned YOLO model on a separate validation dataset. • Measure detection accuracy, precision, recall, and other relevant metrics to assess model performance. 3. Application on Streaming Video (20 marks): • Implement YOLO for real-time object detection on streaming video data. • Integrate the pretrained YOLO model with video streaming frameworks or libraries to demonstrate real-time detection capabilities. 4. Model Optimization and Speed Improvement (20 marks): • Explore techniques for optimizing the pretrained YOLO model for speed and efficiency. • Experiment with model compression, pruning, or quantization techniques to reduce inference time while maintaining performance. 5. Presentation (10 marks): • Prepare a presentation summarizing the project objectives, methodologies, results, and insights. • Showcase the performance of the pretrained YOLO model on object detection tasks and its application on streaming video data. • Present the project outcomes to peers, highlighting key insights and lessons learned. Proposed Dataset: • • Dataset: COCO – Common Objects in Context Description: The COCO dataset is a large-scale object detection, segmentation, and captioning dataset containing over 200,000 images labeled with 80 categories. Use Case: Trainees can utilize this dataset to fine-tune and evaluate the pretrained YOLO model for object detection tasks. Total Marks: 100 Note: • • • Trainees are encouraged to experiment with different pretrained YOLO models and explore techniques for optimizing model performance and speed. Collaboration, knowledge sharing, and feedback from instructors and peers are highly encouraged throughout the project. The project aims to provide a practical learning experience in implementing YOLO for object detection tasks, leveraging pretrained models, and applying them to real-time streaming video data 2 – V 1.0/05032024 Project Outcomes By the end of this project trainee will deliver: A. Python code in a Notebook with Markdown documentation B. Presenting your algorithms results comparison and insights in last section of the notebook. 3 – V 1.0/05032024
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