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krishnamishra8848/
Nepal_Vehicle_License_Plates_Detection_Version3

Name: Nepal_Vehicle_License_Plates_Detection_Version3
Availability: InStock

object-detection

apache-2.0

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🔍 Description **Version 3** of the Nepal Vehicle License Plates Detection Model offers advanced functionality to detect **individual characters** in Nepalese vehicle license plates and draw bounding boxes around each one. This version enhances the accuracy and granularity of detection compared to earlier versions, making it ideal for real-world applications.

✨ Key Features 📝 **Character-Wise Detection**

Detects and draws bounding boxes for each character in a license plate for precise recognition.

🎯 **High Precision and Recall**

Achieves industry-leading metrics, ensuring accurate and reliable performance.

⚡ **Real-Time Inference**

Optimized for fast predictions, making it suitable for live detection applications.

📜 Earlier Model Explore the earlier version of this project, which detects license plates as a whole: [Nepal Vehicle License Plate Detection](https://huggingface.co/krishnamishra8848/Nepal-Vehicle-License-Plate-Detection)

📊 Model Performance Metrics **Precision** 0.985 Percentage of correct bounding box predictions among all predictions. **Recall** 0.984 Percentage of ground truth objects successfully detected. **mAP@50** 0.994 Mean Average Precision at IoU threshold 0.5. **mAP@50-95** 0.861 Mean Average Precision across multiple IoU thresholds (0.5 to 0.95). **Inference Speed** ~2.4ms per image Time taken to process a single image during inference.

python

# Example Code: Test this code on colab
# Step 1: Install required libraries
!pip install ultralytics
!pip install huggingface_hub

# Step 2: Download the model from Hugging Face
from huggingface_hub import hf_hub_download

# Replace "krishnamishra8848/Nepal_Vehicle_License_Plates_Detection_Version3" with your model's Hugging Face repo ID
repo_id = "krishnamishra8848/Nepal_Vehicle_License_Plates_Detection_Version3"
model_path = hf_hub_download(repo_id=repo_id, filename="best.pt")

print(f"Model downloaded and saved at: {model_path}")

# Step 3: Load the model
from ultralytics import YOLO
model = YOLO(model_path)  # Load the model

# Step 4: Upload an image for inference
from google.colab import files
import matplotlib.pyplot as plt
import cv2

# Allow user to upload an image
uploaded = files.upload()

# Get the uploaded file's path
image_path = list(uploaded.keys())[0]

# Step 5: Perform inference
results = model.predict(source=image_path)

# Step 6: Display the results
# Extract the annotated image
annotated_image = results[0].plot()

# Display the image with bounding boxes
plt.figure(figsize=(10, 10))
plt.imshow(cv2.cvtColor(annotated_image, cv2.COLOR_BGR2RGB))
plt.axis("off")
plt.show()