This paper proposes a Fast Region-based Convolutional Network method (Fast R-CNN) for object detection. Fast R-CNN builds on previous work to efficiently classify object proposals using deep convolutional networks. Compared to previous work, Fast R-CNN employs several innovations to improve training and testing speed while also increasing detection accuracy. Fast R-CNN trains the very deep VGG16 network 9x faster than R-CNN, is 213x faster at test-time, and achieves a higher mAP on PASCAL VOC 2012. Compared to SPPnet, Fast R-CNN trains VGG16 3x faster, tests 10x faster, and is more accurate. Fast R-CNN is implemented in Python and C++ (using Caffe) and is available under the open-source MIT License at https://github.com/rbgirshick/fast-rcnn.
Fast R-CNN
Fast R-CNN improves object detection efficiency and accuracy over previous methods by optimizing the training and testing processes of deep convolutional networks.
- Year
- 2015
- Venue
- fast-r-cnn-1
- Authors
- 1
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- Abstract onlyARXIV-DEFAULT
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- Abstract & full text
- arxiv.org/abs/1504.08083v2ARXIV-DEFAULT
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