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Deep Learning methods usually require huge amounts of training data toperform at their full potential, and often require expensive manual labeling.Using synthetic images is therefore very attractive to train object detectors,as the labeling comes for free, and several approaches have been proposed tocombine synthetic and real images for training.  In this paper, we show that a simple trick is sufficient to train veryeffectively modern object detectors with synthetic images only: We freeze thelayers responsible for feature extraction to generic layers pre-trained on realimages, and train only the remaining layers with plain OpenGL rendering. Ourexperiments with very recent deep architectures for object recognition(Faster-RCNN, R-FCN, Mask-RCNN) and image feature extractors (InceptionResnetand Resnet) show this simple approach performs surprisingly well.

On Pre-Trained Image Features and Synthetic Images for Deep Learning