· 2015
Fast R-CNN
- 28,041citations
- 2015year
Short summary
Fast R-CNN is a novel object detection method that significantly improves training and testing speeds while increasing accuracy by efficiently classifying object proposals using deep convolutional networks.
AI-generated from the title and abstract; the full text is not read.
Key points
- Fast R-CNN improves object detection speed and accuracy using deep convolutional networks.
- Achieves 9x faster training and 213x faster testing compared to R-CNN on VGG16.
- Outperforms SPPnet with 3x faster training and 10x faster testing.
- Demonstrates higher mean Average Precision (mAP) on PASCAL VOC 2012.
AI-generated from the title and abstract; the full text is not read.
Abstract
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.
The authors' abstract, as published at the source., 2015 · DOI ↗
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Field: Computer Vision and Pattern Recognition
Computer Vision and Pattern RecognitionComputer Science