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--- |
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tags: |
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- yolov8 |
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- ultralytics |
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- yolo |
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- vision |
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- object-detection |
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- pytorch |
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library_name: ultralytics |
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library_version: 8.2.31 |
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language: |
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- en |
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pipeline_tag: object-detection |
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license: mit |
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--- |
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# Model Card for YOLOv8n_RD Multiple Record Detection Model |
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### Model Description |
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The YOLOv8n_RD Record Detection model is designed to detect multiple records in scanned images of birth, death, and marriage certificates. |
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This model enhances data processing by accurately identifying and detecting multiple records, facilitating quick extraction and further |
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analysis. |
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Integrate this model into your document management systems for real-time, automated record detection and data extraction. |
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For customization or integration assistance, contact us https://www.linkedin.com/in/bodhi108/ |
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Your feedback is essential for improving the model's performance. |
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- **Developed by:** FATA_SCIENTISTS |
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- **Model type:** Object Detection |
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- **Task:** Record Detection in Images |
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### Supported Labels |
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``` |
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['records'] |
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``` |
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## Uses |
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### Direct Use |
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The YOLOv8n_RD Record Detection model can be directly integrated into document management systems to provide real-time detection |
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and classification of multiple records in scanned images of birth, death, and marriage certificates. This facilitates quick data |
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extraction and analysis. |
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### Downstream Use |
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The model's real-time capabilities can be leveraged to automate data extraction processes, generate alerts for specific record detections, |
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and enhance overall document processing efficiency. |
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### Training data |
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The YOLOv8n_RD model was trained on a custom dataset consisting of annotated images of birth, death, and marriage records for |
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training and validation. |
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### Out-of-Scope Use |
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The model is not designed for unrelated object detection tasks or scenarios outside the scope of detecting multiple records |
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in scanned images of vital records. |
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## Bias, Risks, and Limitations |
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The YOLOv8n_RD Record Detection model may exhibit some limitations and biases: |
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- Performance may be affected by variations in image quality, document layout, and handwriting styles within scanned records. |
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- Poor quality scans or damaged documents may impact the model's accuracy and responsiveness. |
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- Record-specific anomalies not well-represented in the training data may pose challenges for detection. |
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### Recommendations |
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Users should be aware of the model's limitations and potential biases. Thorough testing and validation within specific document |
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processing environments are advised before deploying the model in production systems. |
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## How to Get Started with the Model |
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To begin using the YOLOv8s_RD model for multiple record detection in an image, follow these steps: |
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```python |
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pip install ultralytics==8.2.31 |
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pip install opencv-python==4.8.0.76 |
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``` |
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- Load model and perform real-time prediction: |
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```python |
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from ultralytics import YOLO |
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import os |
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import cv2 |
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import matplotlib.pyplot as plt |
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model = YOLO("Bodhi108/Yolov8n_RD") |
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def detect_records(input_folder): |
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# Iterate over all images in the input folder |
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for filename in os.listdir(input_folder): |
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if filename.endswith(('.jpg', '.jpeg', '.png')): |
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img_path = os.path.join(input_folder, filename) |
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img = cv2.imread(img_path) |
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results = model(img) |
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for result in results: |
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if result.boxes.data.shape[0] > 0: # Check for detections |
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for i, box in enumerate(result.boxes.data.tolist()): |
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xmin, ymin, xmax, ymax, conf, cls = box |
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# Draw the bounding box on the image |
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cv2.rectangle(img, (int(xmin), int(ymin)), (int(xmax), int(ymax)), (0, 255, 0), 5) |
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plt.imshow(cv2.cvtColor(img, cv2.COLOR_BGR2RGB)) |
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plt.title(f"Detections on {filename}") |
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plt.axis('off') |
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plt.show() |
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input_folder = 'your input image directory' |
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detect_records(input_folder) |
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``` |
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<div align="center"> |
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<img width="500" alt="Bodhi108/Yolov8n_RD" src="https://huggingface.co/Bodhi108/Yolov8n_RD/resolve/main/ex1.jpg"> |
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</div> |
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## Training Details |
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### Training Data |
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The model is trained on a diverse dataset containing scanned images of birth, death, and marriage records, |
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capturing various document layouts, handwriting styles, and conditions. |
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### Training Procedure |
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The training process involves extensive computation and is conducted over multiple epochs. |
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The model's weights are adjusted to minimize detection loss and optimize performance for accurate record detection in scanned images. |
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#### Metrics |
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<div align="center"> |
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<img width="500" alt="Bodhi108/Yolov8n_RD" src="https://huggingface.co/Bodhi108/Yolov8n_RD/resolve/main/Metrics.jpg"> |
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</div> |
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### Model Architecture and Objective |
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The YOLOv8n_RD architecture incorporates modifications tailored to multiple record detections in an image. |
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It integrates a self-attention mechanism in the head of the network and a feature pyramid network for multi-scaled object detection, |
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enabling it to focus on various parts of an image and detect records of different sizes and scales |
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### Compute Infrastructure |
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#### Hardware |
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NVIDIA GeForce RTX A6000 card |
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#### Software |
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The model was trained and fine-tuned using a Jupyter Notebook environment. |
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## Model Card Contact |
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```bibtex |
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@ModelCard{ |
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author = {Tonumoy Mukherjee, Kazi Mostaq Hridoy, and Aryadip Mridha}, |
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title = {YOLOv8n Multi-Record Detection in an image}, |
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year = {2024} |
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} |
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``` |
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