update card

README.md

@@ -0,0 +1,111 @@
+---
+license: mit
+language:
+- en
+base_model:
+- jameslahm/yolov10n
+---
+
+
+```markdown
+# Document Layout Detection
+
+This script demonstrates how to use the document layout detection model on an image.
+Below are the steps and code implementation.
+
+---
+
+## Step 1: Import Required Libraries
+```python
+import cv2
+import matplotlib.pyplot as plt
+import numpy as np
+from ultralytics import YOLOv10
+from google.colab.patches import cv2_imshow
+```
+
+- **cv2**: For image processing.
+- **matplotlib.pyplot**: For plotting if needed.
+- **numpy**: For numerical operations.
+- **YOLOv10**: For object detection.
+- **cv2_imshow**: For displaying images in Google Colab.
+
+---
+
+## Step 2: Load YOLOv10 Model
+```python
+model = YOLOv10('vprashant/doclayout_detector/weights')  
+```
+
+- Load the YOLOv10 model with the path to your trained weights.
+
+---
+
+## Step 3: Read and Prepare the Image
+```python
+img = cv2.imread('/content/yolov10/dataset/train/images/11.png')
+img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+```
+
+- Read the image from the specified path.
+- Convert the image from BGR to RGB color space.
+
+---
+
+## Step 4: Perform Object Detection
+```python
+results = model(img)
+```
+
+- Run the YOLOv10 model on the image to get detection results.
+
+---
+
+## Step 5: Extract and Process Detection Results
+```python
+results = results[0] 
+boxes = results.boxes
+data = boxes.data.cpu().numpy()  
+```
+
+- Extract the first result (image-level result).
+- Access the detected bounding boxes.
+- Convert detection data to a NumPy array for processing.
+
+---
+
+## Step 6: Visualize Results
+```python
+for i, detection in enumerate(data):
+    x1, y1, x2, y2, conf, cls_id = detection
+    x1, y1, x2, y2 = map(int, [x1, y1, x2, y2])  # Convert coordinates to integers
+    cv2.rectangle(img, (x1, y1), (x2, y2), (0, 255, 0), 2)  # Draw bounding box
+    
+    class_name = model.names[int(cls_id)]  # Get class name
+    label = f"{class_name}: {conf:.2f}"  # Create label with confidence score
+    cv2.putText(img, label, (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 
+                0.9, (0, 255, 0), 2)  # Add text label
+```
+
+- Loop through all detections.
+- Draw bounding boxes and labels on the image.
+
+---
+
+## Step 7: Display the Processed Image
+```python
+cv2_imshow(img) 
+cv2.waitKey(0)
+cv2.destroyAllWindows()
+```
+
+- Display the image with detections in Google Colab using `cv2_imshow`.
+- Wait for a keypress and close any OpenCV windows.
+
+---
+
+## Note
+- Ensure you have the trained YOLOv10 model and the dataset in the specified paths.
+- Replace the paths with your local or Colab paths.
+- Install necessary libraries like OpenCV, Matplotlib, and ultralytics if not already installed.
+```