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Why is Fast-R-CNN faster than R-CNN?

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R-CNN 1) Extract region proposals (2000 in the paper) and reshape them to have a fixed size (typically 224–256 pixels). You obtain a set of warped region proposals. 2) Evaluate every warped region proposal with the ConvNet. read more

Fast R-CNN 1) Compute the feature maps of the last convolutional layer for the entire image, only one time. 2) Compute the region proposals on the image and map them to the feature maps computed at step 1). read more

Each of the ideas proposed by R-CNN, Fast R-CNN, Faster R-CNN, and finally Mask R-CNN were not necessarily quantum leaps, yet their sum products have led to really remarkable results that bring us closer to a human level understanding of sight. read more

vantages of R-CNN and SPPnet, while improving on their speed and accuracy. We call this method Fast R-CNN be-cause it’s comparatively fast to train and test. The Fast R-CNN method has several advantages: 1. Higher detection quality (mAP) than R-CNN, SPPnet 2. Training is single-stage, using a multi-task loss 3. Training can update all network layers 4. read more

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[Part ?. Best CNN Architecture] 8. ResNet [5], Faster-RCNN ...
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Fast R-CNN white paper
www.cv-foundation.org