mlsanityData to 7 Oct 2026

Fast R-CNN

arXiv 1504.08083 ↗First posted 30 Apr 2015cs.CVcited by 2,930

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Cited by 64 in Multimodal LLMs / vision-language models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

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.

Key facts. Cited by 2,930 papers in the map (data through 7 Oct 2026). First posted on arXiv 30 Apr 2015.

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Citing papers per year: 2018: 1; 2019: 0; 2020: 0; 2021: 697; 2022: 574; 2023: 625; 2024: 511; 2025: 358; 2026: 164.

2,930 papers.

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