ML In Depth: 3 - Deep Computer Vision - Object Detection

The ML in Depth 3 - Deep Vision: Object Detection workshop provided a practical approach to implementing object detection models using deep learning techniques. The session covered essential stages in developing and deploying such models. The talk's key points were as follows: Data Annotation: Attendees learned effective data annotation techniques using the Computer Vision Annotation Tool (CVAT). This open-source tool is designed specifically for computer vision tasks, making data labeling more efficient. Data Loading and Preparation: The workshop explained the crucial steps for loading and preparing annotated data for model training. Techniques such as data cleaning, normalization, and augmentation were demonstrated. Model Training: Participants dived into the process of training an object detection model. Various strategies to optimize model performance were explored during this stage. Model Evaluation: The session introduced attendees to important evaluation metrics like Precision, Recall, and Intersection over Union (IoU) for measuring the effectiveness of the trained model. Model Deployment: Finally, the workshop guided participants through the process of deploying the trained model into a web application for real-time object detection.