--- license: mit --- --- tags: - yolov8 - ultralytics - yolo - vision - object-detection - pytorch library_name: ultralytics library_version: 8.2.31 inference: false model-index: - name: Bodhi108/Yolov8n_RD results: - task: type: object-detection language: - en pipeline_tag: object-detection --- # Model Card for YOLOv8n_RD ### Model Description The YOLOv8n_RD Record Detection model is designed to detect multiple records in scanned images of birth, death, and marriage certificates. This model enhances data processing by accurately identifying and detecting multiple records, facilitating quick extraction and further analysis. Integrate this model into your document management systems for real-time, automated record detection and data extraction. For customization or integration assistance, contact us https://www.linkedin.com/in/bodhi108/ Your feedback is essential for improving the model's performance. - **Developed by:** FATA_SCIENTISTS - **Model type:** Object Detection - **Task:** Record Detection in Images ### Supported Labels ``` ['records'] ``` ## Uses ### Direct Use The YOLOv8n_RD Record Detection model can be directly integrated into document management systems to provide real-time detection and classification of multiple records in scanned images of birth, death, and marriage certificates. This facilitates quick data extraction and analysis. ### Downstream Use The model's real-time capabilities can be leveraged to automate data extraction processes, generate alerts for specific record detections, and enhance overall document processing efficiency. ### Training data The YOLOv8n_RD model was trained on a custom dataset consisting of annotated images of birth, death, and marriage records for training and validation. ### Out-of-Scope Use The model is not designed for unrelated object detection tasks or scenarios outside the scope of detecting multiple records in scanned images of vital records. ## Bias, Risks, and Limitations The YOLOv8n_RD Record Detection model may exhibit some limitations and biases: - Performance may be affected by variations in image quality, document layout, and handwriting styles within scanned records. - Poor quality scans or damaged documents may impact the model's accuracy and responsiveness. - Record-specific anomalies not well-represented in the training data may pose challenges for detection. ### Recommendations Users should be aware of the model's limitations and potential biases. Thorough testing and validation within specific document processing environments are advised before deploying the model in production systems. ## How to Get Started with the Model To begin using the YOLOv8s_RD model for multiple record detection in an image, follow these steps: ```python pip install ultralytics==8.2.31 pip install opencv-python==4.8.0.76 ``` - Load model and perform real-time prediction: ```python from ultralytics import YOLO import os import cv2 import matplotlib.pyplot as plt model = YOLO("your yolov8 trained model.pt") def detect_records(input_folder): # Iterate over all images in the input folder for filename in os.listdir(input_folder): if filename.endswith(('.jpg', '.jpeg', '.png')): img_path = os.path.join(input_folder, filename) img = cv2.imread(img_path) results = model(img) for result in results: if result.boxes.data.shape[0] > 0: # Check for detections for i, box in enumerate(result.boxes.data.tolist()): xmin, ymin, xmax, ymax, conf, cls = box # Draw the bounding box on the image cv2.rectangle(img, (int(xmin), int(ymin)), (int(xmax), int(ymax)), (0, 255, 0), 5) plt.imshow(cv2.cvtColor(img, cv2.COLOR_BGR2RGB)) plt.title(f"Detections on {filename}") plt.axis('off') plt.show() input_folder = 'your input image directory' detect_records(input_folder) ```