What is the Faster R-CNN object detection model?
In computer vision, object detection refers to the identification and spotting of objects in an image. The application of object detection in our daily lives is rapidly increasing. Self-driving cars are one such example that uses object detection to make intelligent choices. Most of this development is due to the deep learning models. Object detection algorithms use convolutional neural networks (CNNs) in deep learning. These models extract spatial features and patterns from the input image and combine these features and patterns to make predictions. For instance, an object detection model will use these features and patterns to predict the region of interest and the detected object. There are various models used for object detection in deep learning. We will be exploring the Faster R-CNN object detection model in this Answer.
What is Faster R-CNN?
Faster R-CNN is an object detection model that builds up on multiple convolutional neural networks. More specifically, the Faster R-CNN comprises two stages: in the first stage, the region proposal network predicts the regions of interest, and in the second stage, the Fast R-CNN network predicts the object in the suggested regions and their box coordinates.
The image illustrates the working of Faster R-CNN. We can see how one network of the model predicts the regions of interest, whereas another network predicts the objects and refines the regions of interest.
Example
There are various frameworks and libraries available for implementing deep learning pipelines. We will be using PyTorch to implement our Faster R-CNN model. The code snippet below shows a pretrained Faster R-CNN model template.
import numpy as np
import torchvision
import torch
from torchvision.models.detection import FasterRCNN_ResNet50_FPN_Weights
from PIL import Image
import matplotlib.patches as patches
# read image as an RGB array
image = np.array(Image.open("<image-path>").convert('RGB'))
# converts the image to tensors
# permutes the image to PyTorch format
image_tensor = torch.tensor(image).permute(2,0,1)/255.0
# loads the model with pretrained weights
model = torchvision.models.detection.fasterrcnn_resnet50_fpn(
weights=FasterRCNN_ResNet50_FPN_Weights.COCO_V1)
# set the model to evaluation mode
model.eval();
# model predicts on the image
predictions = model([image_tensor])Line 1–6: We import the necessary libraries.
Line 9–12: We read the image and then convert it into PyTorch format for model usage.
Line 15–16: We load the model with the pretrained weights.
Line 19: We set the model to evaluation mode. This ensures that the gradients are not updated.
Line 22: The model predicts the objects and their corresponding boxes.
Now, we'll see a live example of using the pretrained Faster-RCNN model to predict objects in an image. Click the “Run” button in the widget below.
import React from 'react';
require('./style.css');
import ReactDOM from 'react-dom';
import App from './app.js';
ReactDOM.render(
<App />,
document.getElementById('root')
);
Analysis of Faster R-CNN
In practice, Faster R-CNN is known to be an excellent object detection model. Instead of detecting objects all over the image, its multistage architecture detects objects only in the suggested areas. This makes the predictions of Faster R-CNN more reliable. Due to the accuracy of Faster R-CNN, segmentation models like Mask R-CNN are based on the same concept. However, it has a significant limitation: since it combines multiple networks, the model is costly in computational resources and time consumption. One such example is object detection in real-time video streaming.
To sum up, Faster R-CNN is a deep learning model that performs object detection in two stages. First, it predicts the regions of interest and then predicts the objects in those regions. While the model is capable of accurately detecting objects, it is resource-hungry for larger datasets.
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