Use one image with one road sign and the least background you can for the best results.
Our PyTorch model for road sign image classification has been trained using transfer learning on the MobileNetV3 architecture, leveraging the pre-trained weights to achieve high accuracy and efficiency. The model has been trained on a dataset of 24,000 images that span across 43 different classes of road signs, allowing for accurate classification of a wide range of sign types. Our model achieved 99% accuracy during training.
Furthermore, the PyTorch framework enables flexibility and customization in the model architecture, making it easy to fine-tune the model for specific use cases. This means that the model can be adapted to work with different types of road signs or different regions, making it a versatile tool for a wide range of applications.
Road Signs our model supports:
‘Speed limit (20km/h)’,
‘Speed limit (30km/h)’,
‘Speed limit (50km/h)’,
‘Speed limit (60km/h)’,
‘Speed limit (70km/h)’,
‘Speed limit (80km/h)’,
‘End of speed limit (80km/h)’,
‘Speed limit (100km/h)’,
‘Speed limit (120km/h)’,
‘No passing’,
‘No passing for vehicles over 3.5 metric tons’,
‘Right-of-way at the next intersection’,
‘Priority road’,
‘Yield’,
‘Stop’,
‘No vehicles’,
‘Vehicles over 3.5 metric tons prohibited’,
‘No entry’,
‘General caution’,
‘Dangerous curve to the left’,
‘Dangerous curve to the right’,
‘Double curve’,
‘Bumpy road’,
‘Slippery road’,
‘Road narrows on the right’,
‘Road work’,
‘Traffic signals’,
‘Pedestrians’,
‘Children crossing’,
‘Bicycles crossing’,
‘Beware of ice/snow’,
‘Wild animals crossing’,
‘End of all speed and passing limits’,
‘Turn right ahead’,
‘Turn left ahead’,
‘Ahead only’,
‘Go straight or right’,
‘Go straight or left’,
‘Keep right’,
‘Keep left’,
‘Roundabout mandatory’,
‘End of no passing’,
‘End of no passing by vehicles over 3.5 metric tons’