README.md

metadata

license: mit
base_model:
  - Ultralytics/YOLOv8l

Model Training

Training Details

The YOLOv8l model was fine-tuned on a cloud A100 GPU (NVIDIA A100-SXM4-40GB) using approximately 24,000 images from the Augmented Startups Playing Cards dataset.

Training Configuration:

• Model: YOLOv8l (YOLO v8 Large)
• Dataset: Augmented Startups Playing Cards (Roboflow Universe)
  • Dataset URL: https://universe.roboflow.com/augmented-startups/playing-cards-ow27d/dataset/4
• Training Images: ~24,000 images
• Classes: 52 (one for each playing card)
• Epochs: 50
• Image Size: 640x640
• Batch Size: 16
• Hardware: NVIDIA A100-SXM4-40GB GPU
• Framework: Ultralytics YOLOv8

Training Process:

The training was performed using the Ultralytics YOLOv8 framework. The process involved:

1. Dataset Preparation: Downloaded the Augmented Startups Playing Cards dataset from Roboflow in YOLOv8 format
2. Model Initialization: Started with pre-trained YOLOv8l weights (yolov8l.pt)
3. Fine-tuning: Trained for 50 epochs on the playing cards dataset
4. Model Export: Saved the fine-tuned model as playing-cards.pt