27 lines
1.0 KiB
Markdown
27 lines
1.0 KiB
Markdown
# Fast R-CNN
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> [Fast R-CNN](https://arxiv.org/abs/1504.08083)
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<!-- [ALGORITHM] -->
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## Abstract
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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.
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<div align=center>
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<img src="https://user-images.githubusercontent.com/40661020/143882189-6258c05c-f2a1-4320-9282-7e2f2d502eb2.png"/>
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</div>
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## Results and Models
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## Citation
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```latex
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@inproceedings{girshick2015fast,
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title={Fast r-cnn},
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author={Girshick, Ross},
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booktitle={Proceedings of the IEEE international conference on computer vision},
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year={2015}
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}
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```
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