{"id":11801,"date":"2026-08-10T10:37:08","date_gmt":"2026-08-10T10:37:08","guid":{"rendered":"https:\/\/nokobox.com\/index.php\/item\/udemy-computer-vision-yolo-custom-object-detection-with-colab-gpu-2023-1\/"},"modified":"2026-08-10T10:37:08","modified_gmt":"2026-08-10T10:37:08","slug":"udemy-computer-vision-yolo-custom-object-detection-with-colab-gpu-2023-1","status":"publish","type":"digital_item","link":"https:\/\/nokobox.com\/index.php\/item\/udemy-computer-vision-yolo-custom-object-detection-with-colab-gpu-2023-1\/","title":{"rendered":"Udemy \u2013 Computer Vision: YOLO Custom Object Detection with Colab GPU 2023-1"},"content":{"rendered":"
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Description<\/span><\/h2>\n

Computer Vision Course: YOLO Custom Object Detection with Colab GPU. This course is the fourth in a series of computer vision courses that focuses on object detection, one of the most widely used applications in the field. In this course, participants will learn how to create and train object detection models based on pre-trained or custom data using the YOLO framework and Python. The course is divided into two parts: Part 1 uses the Coco dataset to detect 80 object classes, and Part 2 trains the model with custom data, such as building a coronavirus detection model. After introducing the YOLO theory, the programming environment is prepared with Anaconda and the OpenCV library. For beginners, basic Python topics including functions, data structures, and flow control are taught. Then, participants will be introduced to convolutional neural networks and their stages and implement the YOLO model for object detection in images and videos. The Non Maxima Suppression technique is used to solve the problem of multiple object detection. In the customization section, the steps of data collection, labeling with LabelImg, data partitioning, and file configuration are taught. Colab GPU is used to train the model and periodically save the weights files to Google Drive. Finally, the trained model is tested on images and videos.<\/span><\/p>\n

What you will learn<\/span><\/h3>\n