Update README.md

README.md

@@ -1,4 +1,57 @@
 ---
 license: apache-2.0
 pipeline_tag: object-detection
----
+---
+# Face Mask Detection Model
+
+```python
+# Example Code: You can test this model on colab or anywhere u want
+
+# Install necessary libraries
+!pip install ultralytics huggingface_hub
+
+# Download the model from Hugging Face
+from huggingface_hub import hf_hub_download
+from ultralytics import YOLO
+from google.colab import files
+from IPython.display import Image, display
+import cv2
+import matplotlib.pyplot as plt
+
+# Define repository and file path
+repo_id = "krishnamishra8848/Face_Mask_Detection"
+filename = "best.pt"  # File name in your Hugging Face repo
+
+# Download the model file
+model_path = hf_hub_download(repo_id=repo_id, filename=filename)
+print(f"Model downloaded to: {model_path}")
+
+# Load the YOLOv8 model
+model = YOLO(model_path)
+
+# Upload an image for testing
+print("Upload an image to test:")
+uploaded = files.upload()
+image_path = list(uploaded.keys())[0]
+
+# Display the uploaded image
+print("Uploaded Image:")
+display(Image(filename=image_path))
+
+# Run inference on the uploaded image
+print("Running inference...")
+results = model.predict(source=image_path, conf=0.5)
+
+# Save and visualize the results
+print("Saving and displaying predictions...")
+for result in results:
+    annotated_image = result.plot()  # Annotate the image with bounding boxes and labels
+    # Convert annotated image to RGB for display with matplotlib
+    annotated_image_rgb = cv2.cvtColor(annotated_image, cv2.COLOR_BGR2RGB)
+    plt.figure(figsize=(10, 10))
+    plt.imshow(annotated_image_rgb)
+    plt.axis("off")
+    plt.title("Prediction Results")
+    plt.show()
+
+