MobileNetV2 is a lightweight deep neural network architecture optimized for mobile and edge devices. It was trained on the ImageNet dataset, which contains over 1 million labeled images across 1,000 different classes.
When you upload an image, it is processed through several convolutional layers that extract important features like edges, textures, and patterns. These features are then passed through depthwise separable convolutions, significantly reducing computation while maintaining accuracy. Finally, the model predicts the class that best matches the features detected in your image.
This allows fast and accurate image classification with minimal resource usage — perfect for real-world applications!