# Train a Custom YOLOv8 Detector in Python (Playing Cards)

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](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/](https://eranfeit.net/how-to-train-yolov8-object-detection-on-a-custom-dataset-cards-detection/)

Join my Newsletter : [https://eranfeit.net/advance-your-skills-in-computer-vision-ai/](https://eranfeit.net/advance-your-skills-in-computer-vision-ai/)

Deep-dive video walkthrough: [https://youtu.be/lw6tn3nHaj8](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