updated readme

README.md

@@ -1,3 +1,172 @@
----
-license: mit
----
+---
+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)
+```
+
+<div align="center">
+  <img width="500" src="https://huggingface.co/Bodhi108/Yolov8n_RD/blob/main/ex1.png">
+</div>
+
+## Training Details
+
+### Training Data
+
+The model is trained on a diverse dataset containing scanned images of birth, death, and marriage records, 
+capturing various document layouts, handwriting styles, and conditions.
+
+### Training Procedure
+
+The training process involves extensive computation and is conducted over multiple epochs. 
+The model's weights are adjusted to minimize detection loss and optimize performance for accurate record detection in scanned images.
+
+#### Metrics
+
+<div align="center">
+  <img width="500" src="https://huggingface.co/Bodhi108/Yolov8n_RD/blob/main/Metrics.png">
+</div>
+
+### Model Architecture and Objective
+
+The YOLOv8n_RD architecture incorporates modifications tailored to multiple record detections in an image.
+It integrates a self-attention mechanism in the head of the network and a feature pyramid network for multi-scaled object detection, 
+enabling it to focus on various parts of an image and detect records of different sizes and scales
+
+### Compute Infrastructure
+
+#### Hardware
+
+NVIDIA GeForce RTX A6000 card
+
+#### Software
+
+The model was trained and fine-tuned using a Jupyter Notebook environment.
+
+## Model Card Contact
+
+```bibtex
+@ModelCard{
+  author    = {Tonumoy Mukherjee, Kazi Mostaq Hridoy, and Aryadip Mridha},
+  title     = {YOLOv8n Multi-Record Detection in an image},
+  year      = {2024}
+}
+```
+