accuracy drops when using FP32 in faster rcnn

Autonomous Machines Jetson & Embedded Systems Jetson TX2
1

Hi,

I am using faster-rcnn in FP32 mode with TensorRT-3.0.

However, as compared to caffe framework, the accuracy drops about 10%.

As far as I know, FP32 is full precision, and caffe also uses FP32 by default. I have no idea why the accuracy is different a lot with caffe.

I can’t find any accuracy benchmark about faster-rcnn on TensorRT.

So, I am not sure if this is the problem of TensorRT.

thanks.

2

Hi,

Do you use our faster-rcnn sample? Which is located at /usr/src/tensorrt/samples/.

This sample only supports ppm image; another image format may lead to wrong decode results.

3

Hi,

I am using opencv to read jpeg images.

and then tried to convert it to bgr array.

the code is something like

cv::Mat img_in = cv::imread("sample.jpg");
img_in.convertTo(img_in, CV_32FC3);
cv::Mat mean(img_in.rows, img_in.cols, CV_32FC3, cv::Scalar(102.9801f, 115.9465f, 122.7717f));
cv::subtract(img_in, mean, img_in);

// wrap into bgr array
float* data = new float[3 * img_in.rows * img_in.cols];

std::vector<cv::Mat> input_channels;
for (int i = 0; i < 3; ++i) {
  cv::Mat channel(img_in.rows, img_in.cols, CV_32FC1, data);
  input_channels->push_back(channel);
  data += img_in.rows * img_in.cols;
}

cv::split(img_in, input_channels);

// forward data to inferernce
// ...

Is something wrong with above code?

btw, Do you have tested faster rcnn with TensorRT on VOC benchmark? How is the accuracy as compared to the paper?

4

Hi,

We don’t have accuracy score since it’s this sample is to demonstrate plugin API.
It should be similar to the original fasterRCNN results.

For image → tensorRT, please check our jetson_inference sample for details:
[url]https://github.com/dusty-nv/jetson-inference/blob/master/imageNet.cpp#L295[/url]