Table Of Contents
Table Of Contents

2. Predict with pre-trained Faster RCNN models

This article shows how to play with pre-trained Faster RCNN model.

First let’s import some necessary libraries:

from matplotlib import pyplot as plt
import gluoncv
from gluoncv import model_zoo, data, utils

Load a pretrained model

Let’s get an Faster RCNN model trained on Pascal VOC dataset with ResNet-50 backbone. By specifying pretrained=True, it will automatically download the model from the model zoo if necessary. For more pretrained models, please refer to Model Zoo.

The returned model is a HybridBlock gluoncv.model_zoo.FasterRCNN with a default context of cpu(0).

net = model_zoo.get_model('faster_rcnn_resnet50_v1b_voc', pretrained=True)

Pre-process an image

Next we download an image, and pre-process with preset data transforms. The default behavior is to resize the short edge of the image to 600px. But you can feed an arbitrarily sized image.

You can provide a list of image file names, such as [im_fname1, im_fname2, ...] to if you want to load multiple image together.

This function returns two results. The first is a NDArray with shape (batch_size, RGB_channels, height, width). It can be fed into the model directly. The second one contains the images in numpy format to easy to be plotted. Since we only loaded a single image, the first dimension of x is 1.

Please beware that orig_img is resized to short edge 600px.

im_fname ='' +
x, orig_img = data.transforms.presets.rcnn.load_test(im_fname)


Downloading biking.jpg from

Inference and display

The Faster RCNN model returns predicted class IDs, confidence scores, bounding boxes coordinates. Their shape are (batch_size, num_bboxes, 1), (batch_size, num_bboxes, 1) and (batch_size, num_bboxes, 4), respectively.

We can use gluoncv.utils.viz.plot_bbox() to visualize the results. We slice the results for the first image and feed them into plot_bbox:

box_ids, scores, bboxes = net(x)
ax = utils.viz.plot_bbox(orig_img, bboxes[0], scores[0], box_ids[0], class_names=net.classes)

Total running time of the script: ( 0 minutes 5.350 seconds)

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