3.2 Using Convolutional Neural Nets to filter out unsuitable images
The optimal conditions for the Convolutional Neural Net (CNN) trained to recognise wild dogs standing up were two convolutional layers, with kernel sizes of 32 and 64, respectively, and a learning rate of 10-5. This model achieved a training accuracy of 100%, a validation accuracy of 91% (95% C.I. 90 – 92), and a testing accuracy of 90% (95% C.I. 88 – 91, Table 2). For the CNN designed to separate images of the left and right flanks, the optimal conditions were three convolutional layers, one with a kernel size of 64 and two with kernel sizes of 32, with a learning rate of 10-4. Its training, validation and testing accuracy were 100%, 96% (95% C.I. 95 - 97), and 95% (95% C.I. 94 - 96), respectively.