Neural decoding of the visual system is a subject of research interest, both to understand how the visual system works and to be able to use this knowledge in areas, such as computer vision or brain-computer interfaces. Spike-based decoding is often used, but it is difficult to record data from the whole visual cortex, and it requires proper preprocessing. We here propose a decoding method that combines wide-field calcium brain imaging, which allows us to obtain large-scale visualization of cortical activity with a high signal-to-noise ratio (SNR), and convolutional neural networks (CNNs). A mouse was presented with ten different visual stimuli, and the activity from its primary visual cortex (V1) was recorded. A CNN we designed was then compared with other existing commonly used CNNs, that were trained to classify the visual stimuli from wide-field calcium imaging images, obtaining a weighted F1 score of more than 0.70 on the test set, showing it is possible to automatically detect what is present in the visual field of the animal.