CNN Explainer is interactive content that traces how a convolutional neural network (CNN) classifies an image through layer-by-layer visualization. You can see how the input image is transformed through convolution, activation, pooling, and fully connected layers.
Because filters, feature maps, neuron activations, and final prediction probabilities are shown in the same context, the tool makes it easier to connect the model structure with the flow of inference. It is a useful learning tool for explaining what happens inside an image recognition model.
