Fast R-CNN

Code

• github.com/rbgirshick/fast-rcnn
  OfficialIn papercaffe2★ 0
• github.com/serengil/retinaface
  tf★ 1,948
• github.com/msuhail1997/Faster-RCNN-Pytorch_Object_Detection
  pytorch★ 0
• github.com/AkashGanesan/PedestrianAttention
  none★ 0
• github.com/devsoft123/fast-cnn
  caffe2★ 0
• github.com/BlackAngel1111/Fast-RCNN
  caffe2★ 0
• github.com/saumya-jetley/pp_ICVSS15_TacklingBkgndDifferently
  none★ 0
• github.com/nirajdpandey/Object-detection-and-localization-using-SSD
  tf★ 0
• github.com/sangamdeuja/Helsinki_pedestrian_crossing_detection
  none★ 0
• github.com/nirajdevpandey/Object-detection-and-localization-using-SSD-
  tf★ 0

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.

Tasks

Benchmark Results

Reproductions