Image Labeling using CNN with Web Application

Image labeling is a basic computer vision problem applied in different areas, from medical diagnosis and self-driving vehicles to facial recognition and industrial automation. This work emphasizes using CNNs - a special family of deep models built specifically to learn from visual data - in performing image classification efficiently and effectively. CNNs employ their architecture to automatically extract and learn hierarchical spatial and temporal features from input images by including different layers. Preprocessing techniques including resizing, normalization, and data augmentation are involved in the methodology proposed to get the dataset ready for training. The CNN model is designed by multiple convolutional layers that include localized features to bring non-linearity, the activation functions like ReLU is used. To decrease spatial dimensions Pooling layers like max-poolingare used. The final layers are dense connections that map the learned features to specific image classes. Moreover, advanced optimization techniques are used to reduce the loss function and also the training efficiency will be more.The model achieves best performance, withaccuracy surpassing traditional techniques.