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Overview The Ultralytcis ecosystem ships with a lightweight Annotator utility that can overlay detection masks, bounding boxes, oriented boxes, and keypoints on any image or video stream. The snippets below demonstrate typical use-cases. Interactive sweep counter on a video The following example tracks every object that crosses a user-draggable ...

When resizing images in computer vision applications, bounding box coordinates must be scaled proportionally to maintain accurate object detection. import cv2 import numpy as np from ultralytics.utils.ops import scale_boxes # Load the original image original_image = cv2.imread("sample_images/vehicle.jpg") original_height, original_w ...

Attention mechanisms have significantly improved the performance of deep learning models in computer vision tasks. This article provides an overview of several popular attention modules that can be easily integrated into object detection models like YOLOv5, YOLOv7, YOLOv8, YOLOv9, and YOLOv10. SE Paper: Squeeze-and-Excitation Networks Link: arX ...

This guide demonstrates how to wrap the YOLOv5 model in a lightweight Flask service that lets users upload an image or a short video, view the detections in the browser, and download the annotated result. What the service provides Drag-and-drop or click-to-upload for images and MP4 videos. Real-time preview of the original and processed media. ...

Model Parameter Profiling Utility functions in torch_utils facilitate the analysis of model complexity, including layer counts, parameter volumes, and computational load (FLOPs). The following snippet demonstrates how to aggregate parameter statistics and estimate floating-point operations using a dummy input tensor aligned with the model's str ...