更快的RCNN + Inception v2输入大小 [英] Faster RCNN + inception v2 input size

问题描述

更快的RCNN RPN的输入大小是多少? 我正在使用Tensorflow的对象检测API，该API使用更快的RCNN作为区域建议网络(RPN)和Inception作为特征提取器(根据配置文件). API在预测阶段使用在线方法，并分别检测每个输入图像.但是，我现在正在尝试通过使用Tensorflow数据集API将图像批量添加到网络. 如您所知，要批量处理数据，首先我们需要将所有图像调整为相同大小.我认为调整图像大小的最佳方法是将它们精确调整为更快的RCNN的输入大小，以避免重复调整大小.现在我的问题是更快的RCNN RPN的输入大小是多少? 预先感谢

What is the input size of faster RCNN RPN? I'm using an object detection API of Tensorflow which is using faster RCNN as region proposal network ( RPN ) and Inception as feature extractor ( according to the config file ). The API is using the online approach in prediction phase and detects every input image singly. however, I'm now trying to feed images to the network in the batch manner by use of Tensorflow dataset API. as you know for make batch out of the data, firstly we need to resize all of the images to a same size. I think the best way of resizing the images is to resize them exactly to the input size of faster RCNN to avoid duplicate resizing. Now my question is what is the input size of the faster RCNN RPN? thanks in advance

推荐答案

这取决于在image_resizer中的管道配置文件中指定的输入分辨率. 例如，对于在COCO数据集上训练的InceptionV2上的Faster R-CNN，请参见此配置文件. 指定的分辨率为600x1024.

It depends on the input resolution which was specified in the pipeline config file, in image_resizer. For example, for Faster R-CNN over InceptionV2 trained on COCO dataset, see this config file. The specified resolution is 600x1024.

另一方面，全卷积架构(例如RFCN，SSD，YOLO)不限于单个分辨率，即，您可以在不修改架构的情况下将它们应用于不同的输入分辨率. 但这并不意味着如果您使用单一分辨率进行训练，该模型将对它具有鲁棒性.

On a side note, fully convolutional architectures (such as RFCN, SSD, YOLO) don't restrict to a single resolution, i.e. you can apply them on different input resolution without modifying the architecture. But this doesn't mean that the model will be robust to it if you're training on a single resolution.

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