in this work, we develop a robust, extensible tool to automatically and accurately count retinal ganglion cell axons in optic nerve (on) tissue images from various animal models of glaucoma. We adapted deep learning to regress pixelwise axon count density estimates, which were then integrated over the image area to determine axon counts. the tool, termed Axonet, was trained and evaluated using a dataset containing images of on regions randomly selected from whole cross sections of both control and damaged rat ons and manually annotated for axon count and location. this rat-trained network was then applied to a separate dataset of non-human primate (nHp) on images. Axonet was compared to two existing automated axon counting tools, AxonMaster and AxonJ, using both datasets. Axonet outperformed the existing tools on both the rat and nHp on datasets as judged by mean absolute error, R 2 values when regressing automated vs. manual counts, and Bland-Altman analysis. Axonet does not rely on hand-crafted image features for axon recognition and is robust to variations in the extent of on tissue damage, image quality, and species of mammal. therefore, Axonet is not speciesspecific and can be extended to quantify additional ON characteristics in glaucoma and potentially other neurodegenerative diseases. Glaucoma is the leading cause of irreversible blindness worldwide 1,2 , and thus is a significant research focus. This optic neuropathy is characterized by degeneration and loss of retinal ganglion cells (RGCs), which carry visual signals from the retina to the brain. Therefore, an important outcome measure in studying glaucomatous optic neuropathy, particularly in animal models of the disease, is the number and appearance of RGC axons comprising the optic nerve 3,4 , usually evaluated from images of optic nerve cross sections. Using images obtained by light microscopy is known to result in an axon count underestimation of around 30% relative to counts from images obtained by transmission electron microscopy 5,6. However, light microscopy is widely used to count optic nerve axons because of its lower cost and favorable time requirements for tissue preparation. Therefore, in this work we focus on axon counting in optic nerve images generated by light microscopy. Manual counting is the gold standard approach to quantify RGC axons, but it is extremely labor-intensive, since RGC axon numbers in healthy nerves range from the tens of thousands in mice to more than a million in humans 7. Further complicating axon quantification is the fact that axon appearance can be highly variable. For example, in the healthy nerve, most axons are characterized by a clear central axoplasmic core and a darker myelin sheath; following previous work 5,8 , we will refer to such an appearance as "normal". However, in damaged nerves (and even occasionally in ostensibly heathy nerves), other axon appearances occur, such as an incomplete myelin sheath and/or a darker axoplasmic region. Such variability further increases the time needed for axon counting,...