Shapes: semseg


Click on the below button to generate a random picture with geometric shapes, and to display the corresponding label predictions.

Training procedure

The model used here has a simple architecture where three transposed convolution layers follow three standard convolution layers. It has been trained during 30 epochs with a set of 18000 randomly generated images involving an other set of 2000 random images at the end of each training epoch..


This toy dataset contains three labels, i.e. squares, circles and triangles. There is at most one single items for each shape: as a consequence, some pictures may be empty!