leo-p
commented
7 years ago Summary:
Region-based Convolutional Neural Network (R-CNN), basically takes as input and image and several possibles objects (corresponding to Region of Interest) and score each of them.
Architecture:
The feature map is computed for the whole image and then for each region of interest a new fixed-length feature vector is computed using max-pooling. From it two predictions are made for classification and bounding-box offsets.
Results:
By sharing computation for RoIs of the same image and allowing simple SGD training it really improves performance training although at testing it's still not as fast as YOLO9000.
https://arxiv.org/pdf/1504.08083v2.pdf
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 this https URL