---
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`