TensorFlow的更快RCNN [英] Faster RCNN for TensorFlow

问题描述

有人为TensorFlow版本实现FRCNN吗？
我发现了一些相关的存储库，如下所示：

Has anyone implement the FRCNN for TensorFlow version? I found some related repos as following:

1. 实施roi池层


2. 基于py- Fast-rcnn repo

1. Implement roi pool layer
2. Implement fast RCNN based on py-faster-rcnn repo

但对于1：假设roi池层起作用（我没有尝试过），并且需要执行以下操作：

but for 1: assume the roi pooling layer works (I haven't tried), and there are something need to be implemented as following:

• ROI数据层，例如 roidb 。


• 线性回归，例如 SmoothL1Loss


• 用于端到端培训的ROI池层后处理，应将ROI池层的结果转换为CNN进行分类。

对于2：em ....，它似乎基于py-faster-rcnn，它基于Caffe进行预处理（例如roidb）并将数据输入到Tensorflow中以训练模型，这看起来很奇怪，所以我可能没有尝试过。

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.

所以我想知道的是，会 Tensorflow支持将来更快的RCNN ？。如果没有，我是否对上述哪个有误解？还是有任何回购或有人支持？

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?

推荐答案

Tensorflow刚刚发布了官方的对象检测API 此处，例如，可与各种细长模型。

Tensorflow has just released an official Object Detection API here, that can be used for instance with their various slim models.

该API包含各种对象检测管道的实现，包括流行的Faster RCNN及其预先训练的模型。

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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