This video dives into the world of YOLOv9, covering both segmentation and training on custom data with Python!
In this tutorial, you'll learn:
How to download the dataset from kaggle.
The fundamentals of YOLOv9 for segmentation tasks.
How to train a YOLOv9 model using your own custom data.
Image segmentation is a method of dividing a digital image into subgroups called image segments, reducing the complexity of the image and enabling further processing or analysis of each image segment. Technically, segmentation is the assignment of labels to pixels to identify objects, people, or other important elements in the image.
A common use of image segmentation is in object detection. Instead of processing the entire image, a common practice is to first use an image segmentation algorithm to find objects of interest in the image. Then, the object detector can operate on a bounding box already defined by the segmentation algorithm. This prevents the detector from processing the entire image, improving accuracy and reducing inference time.
Image segmentation is a key building block of computer vision technologies and algorithms. It is used for many practical applications including medical image analysis, computer vision for autonomous vehicles, face recognition and detection, video surveillance, and satellite image analysis.
Keywords: YOLOv9 Tutorial, YOLOv9 Segmentation, YOLOv9 Training, yolov9, yolov9 tutorial, Yolov9 python, Yolov9 python tutorial, yolo object detection, YOLOv9, YOLOv9 Tutorial, YOLOv9 Model Training, YOLO,YOLOv9,real-time object detection,object detection tutorial,YOLOv9 architecture,GELAN,Programmable Gradient Information,object detection state-of-the-art,train YOLOv9 on custom dataset,YOLOv9 colab tutorial
👦🏻 Linkedin - www.linkedin.com/in/deasadiqbal/
🧑‍💻 GitHub - github.com/
🕸️ Website - deepnexus.blogspot.com/