YOLOv9 Card Detector

This model is a fine-tuned version of YOLOv9c trained to detect playing cards in images. It has been trained on the Set Cards dataset from Roboflow.

Model Details

Base Model: YOLOv9c
Task: Object Detection
Target Class: Cards
Training Dataset: Set Cards Dataset
Image Size: 512x512
Accuracy Metrics: Evaluated at confidence threshold of 0.5

Usage

from transformers import AutoImageProcessor, AutoModelForObjectDetection
import torch
from PIL import Image
import requests

# Load model and processor
processor = AutoImageProcessor.from_pretrained("YOUR_USERNAME/yolov9-card-detector")
model = AutoModelForObjectDetection.from_pretrained("YOUR_USERNAME/yolov9-card-detector")

# Load image
image_url = "https://example.com/path/to/card_image.jpg"
image = Image.open(requests.get(image_url, stream=True).raw)

# Prepare image for the model
inputs = processor(images=image, return_tensors="pt")

# Make prediction
with torch.no_grad():
    outputs = model(**inputs)

# Process results
results = processor.post_process_object_detection(
    outputs,
    threshold=0.5,
    target_sizes=[(image.height, image.width)]
)[0]

# Display results
for score, label, box in zip(results["scores"], results["labels"], results["boxes"]):
    box = [round(i, 2) for i in box.tolist()]
    print(
        f"Detected {model.config.id2label[label.item()]} with confidence "
        f"{round(score.item(), 3)} at location {box}"
    )

Training

This model was fine-tuned from YOLOv9c using the Ultralytics framework. It was trained for 30 epochs with an image size of 512x512.

License

This model is licensed under CC BY 4.0, following the dataset's licensing terms.

Limitations

The model is specifically trained to detect playing cards and may not perform well on other objects
Performance may vary based on lighting conditions, card orientation, and image quality
Best results are achieved with images similar to those in the training dataset