Data Science Project | Part 1 | Industry Safety Detection using YOLO v7
Welcome to Part 1 of our Data Science Project series!
In this session, we delve into the critical application of industry safety detection using YOLO v7 (You Only Look Once), a state-of-the-art object detection model. This project focuses on enhancing safety measures in industrial settings using advanced computer vision techniques.
What You'll Learn:
-Introduction to Industry Safety Detection: Understand the importance of safety detection systems in industrial environments to prevent accidents and ensure workplace safety.
-Overview of YOLO v7: Introduction to the YOLO v7 model architecture, its efficiency in real-time object detection, and how it compares to previous versions.
-Dataset Preparation: Explore the dataset used for training the YOLO v7 model, including collecting and labeling images of safety equipment and hazardous conditions.
Training YOLO v7: Step-by-step guide on setting up the training pipeline for YOLO v7, including data augmentation, transfer learning, and fine-tuning.
Model Evaluation: Learn how to evaluate the performance of your YOLO v7 model using metrics such as mean Average Precision (mAP) and Intersection over Union (IoU).
Why Watch This session ?
This session is essential for data science enthusiasts and professionals interested in applying deep learning and computer vision to enhance industry safety. By the end of this session, you'll have the knowledge and skills to preprocess data, train a YOLO v7 model for safety detection, and evaluate its performance.
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