Deep CNN Ensemble with Data Augmentation for Object Detection
This provides incremental improvements for computer vision researchers working on object detection benchmarks.
The authors tackled object detection on PASCAL VOC 2012 by creating an ensemble of deep CNN models with different architectures and augmenting the training data with selected images from Microsoft COCO, achieving state-of-the-art performance on the challenge.
We report on the methods used in our recent DeepEnsembleCoco submission to the PASCAL VOC 2012 challenge, which achieves state-of-the-art performance on the object detection task. Our method is a variant of the R-CNN model proposed Girshick:CVPR14 with two key improvements to training and evaluation. First, our method constructs an ensemble of deep CNN models with different architectures that are complementary to each other. Second, we augment the PASCAL VOC training set with images from the Microsoft COCO dataset to significantly enlarge the amount training data. Importantly, we select a subset of the Microsoft COCO images to be consistent with the PASCAL VOC task. Results on the PASCAL VOC evaluation server show that our proposed method outperform all previous methods on the PASCAL VOC 2012 detection task at time of submission.