In this study, we propose a shared lightweight convolutional neural network (CNN) to automatically identify vehicle colors and types. In the basic network, an improved SqueezeNet is employed. Further, we compare the classification performances of different “slimming” SqueezeNets on the training set. In addition, the characteristics of the fully shared, partly shared, and no-shared networks are discussed. Experimental results indicate that the fully shared lightweight CNN not only reduces the number of parameters but also realizes high-precision recognition of the multiple attributes associated with the appearance of vehicles. Subsequently, an experiment was conducted on the Opendata_VRID dataset. The accuracy of vehicle color and type recognition is 98.5% and 99.1%, respectively. A single picture can be recognized on a personal computer without GPU in only 4.42 ms. Thus, the shared lightweight CNN considerably reduces time and space consumption and is more conducive for deployment in resource-constrained systems.