We present a catalog of visual like H-band morphologies of ∼ 50.000 galaxies (H f 160w < 24.5) in the 5 CANDELS fields (GOODS-N, GOODS-S, UDS, EGS and COSMOS). Morphologies are estimated with Convolutional Neural Networks (ConvNets). The median redshift of the sample is < z >∼ 1.25. The algorithm is trained on GOODS-S for which visual classifications are publicly available and then applied to the other 4 fields. Following the CANDELS main morphology classification scheme, our model retrieves the probabilities for each galaxy of having a spheroid, a disk, presenting an irregularity, being compact or point source and being unclassifiable. ConvNets are able to predict the fractions of votes given a galaxy image with zero bias and ∼ 10% scatter. The fraction of miss-classifications is less than 1%. Our classification scheme represents a major improvement with respect to CAS (Concentration-Asymmetry-Smoothness)-based methods, which hit a 20 − 30% contamination limit at high z. The catalog is released with the present paper via the Rainbow database

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