In the brand-wise random-ordered drinking PET bottles picking task, the overlapping and viewing angle problem makes a low accuracy of the brand recognition. In this paper, we set the problem to increase the brand recognition accuracy and try to find out how the overlapping rate infects on the recognition accuracy. By using a stepping motor and transparent fixture, the training images were taken automatically from the bottles under 360 degrees to simulate a picture taken from viewing angle. After that, the images are augmented with random cropping and rotating to simulate the overlapping and rotation in a real application. By using the automatically constructed dataset, the Inception V3, which was transferred learning from ImageNet, is trained for brand recognition. By generating a random mask with a specific overlapping rate on the original image, the Inception V3 can give 80% accuracy when 45% of the object in the image is visible or 86% accuracy when the overlapping rate is lower than 30%.