Train a Custom YOLOv8 Detector in Python (Playing Cards)

Question

For anyone studying custom object detection and deep learning workflows:
Real-world computer vision requires adapting pre-trained architectures to specialized feature distributions that standard public datasets cannot resolve. Leveraging Ultralytics YOLO anchor-free head for optimized detection speed and accuracy, this workflow covers isolating dependencies within a CUDA environment, visually auditing normalized annotations with OpenCV, fine-tuning the detector on a playing cards dataset, and systematically benchmarking output bounding boxes against ground-truth labels.
Reading on Medium: https://medium.com/object-detection-tutorials/how-to-train-yolov8-object-detection-on-a-custom-dataset-cards-detection-5d99bf849987
Detailed written explanation and source code: https://eranfeit.net/how-to-train-yolov8-object-detection-on-a-custom-dataset-cards-detection/
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Deep-dive video walkthrough: https://youtu.be/lw6tn3nHaj8
This content is for educational purposes only. The community is welcome to provide constructive feedback or ask technical questions regarding dataset formatting, hyperparameters, or training configurations in the comments.

Eran Feit

YOLOv8 #ObjectDetection #DeepLearning #Ultralytics