| # 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](https://universe.roboflow.com/tel-aviv/set_cards/dataset/1) |
| - **Image Size**: 512x512 |
| - **Accuracy Metrics**: Evaluated at confidence threshold of 0.5 |
|
|
| ## Usage |
|
|
| ```python |
| 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 |
|
|
