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Has anyone implement the FRCNN for TensorFlow version? I found some related repos as following:
but for 1: assume the roi pooling layer works (I haven't tried), and there are something need to be implemented as following:
For 2: em...., it seems based on py-faster-rcnn which based on Caffe to prepared pre-processing (e.g. roidb) and feed data into Tensorflow to train the model, it seems weird, so I may not tried it.
So what I want to know is that, will Tensorflow support Faster RCNN in the future?. If not, do I have any mis-understand which mentioned above? or has any repo or someone support that?
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Results: The mean average precision (MAP) of Faster R-CNN reached 87.69% but YOLO v3 had a significant advantage in detection speed where the frames per second (FPS) was more than eight times than that of Faster R-CNN. This means that YOLO v3 can operate in real time with a high MAP of 80.17%.
Faster R-CNN is a deep convolutional network used for object detection, that appears to the user as a single, end-to-end, unified network. The network can accurately and quickly predict the locations of different objects.
In general, Faster R-CNN is more accurate while R-FCN and SSD are faster. Faster R-CNN using Inception Resnet with 300 proposals gives the highest accuracy at 1 FPS for all the tested cases. SSD on MobileNet has the highest mAP among the models targeted for real-time processing.
Tensorflow has just released an official Object Detection API here, that can be used for instance with their various slim models.
This API contains implementation of various Pipelines for Object Detection, including popular Faster RCNN, with their pre-trained models as well.
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