Species Distribution Models based on Convolutional Neural Networks (CNN-SDMs) have recently emerged, demonstrating greater effectiveness than traditional SDMs in several contexts. A limited number of studies, however, have focused on species abundance patterns, as the datasets available for this purpose are generally too small to effectively learn a deep learning model with millions of parameters. Our study demonstrated that CNN-SDMs can circumvent the small sample size of species abundance datasets through the combined use of a large presence-only species dataset and transfer learning to significantly improve the performance of abundance-based CNN-SDMs. Applied to Mediterranean coastal fishes, our approach significantly improves the abundance prediction performance of CNN-SDMs, with average gains of 35% (D-squared regression score). This allows CNN-SDMs to perform better than classical SDMs in abundance prediction, with average gains of 10%. These gains are stemming from enhanced abundance predictions for rare species and where widespread species are locally rare.