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Faster R-CNN

What is Faster R-CNN?

One of the most accurate object detection algorithms but requires a lot of power at inference time. A good choice if you can do processing asynchronously on a server.

Training on SageMaker

Create a Confusion Matrix

Plot Predictions

Filter Predictions

Blur Predictions

Use SAHI for Small Object Detection

About the model

Here is an overview of the

Faster R-CNN

model:
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Date of Release
Model Type	Object Detection
Architecture
Framework Used	TensorFlow 1.5
Annotation Format	Tensorflow TFRecord
Stars on GitHub	7800+

What is Faster R-CNN?

Faster R-CNN is a state-of-the-art object detection framework. It has been around for a while and has a lot of nice integrations. Despite its name, Faster R-CNN is known as being a slower model than some other choices (like YOLOv3 or MobileNet) for inference but in return is more accurate. It is one of the many model architectures that the TensorFlow Object Detection API provides by default, including with pre-trained weights, and the implementation in this notebook links in with Tensorboard.

Faster R-CNN Architecture

The Faster R-CNN utilizes is a two-stage deep learning object detector: first, it identifies regions of interest and then passes these regions to a convolutional neural network. The outputted feature maps are passed to a support vector machine (SVM) for classification. Regression between predicted bounding boxes and ground truth bounding boxes is computed. Below is the general architecture for the Faster R-CNN:

Check out YOLOv8, defining a new state-of-the-art in computer vision

YOLOv8 is here, setting a new standard for performance in object detection and image segmentation tasks. Roboflow has developed a library of resources to help you get started with YOLOv8, covering guides on how to train YOLOv8, how the model stacks up against v5 and v7, and more.

Learn about YOLOv8

Check out YOLOv8, defining a new state-of-the-art in computer vision

Learn about YOLOv8

Check out YOLOv8, defining a new state-of-the-art in computer vision

Learn about YOLOv8

Check out YOLOv8, defining a new state-of-the-art in computer vision

Learn about YOLOv8

Model Performance

Explore this model on Roboflow

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James Nitsch used Roboflow to organize his images and build models, enabling him to quickly assess quality and whether the card should be officially graded.

Train TensorFlow Faster R-CNN Model with Custom Data

In this example we will walkthrough training an object detection model using the TensorFlow object detection API

Train TensorFlow Faster R-CNN Model with Custom Data

In this example we will walkthrough training an object detection model using the TensorFlow object detection API

Faster PyTorch models via OpenVINO™ Integration to Torch-ORT

Inference on Intel® hardware by integrating OpenVINO™ toolkit with Torch-ORT while maintaining the native PyTorch experience.

Deploy Faster R-CNN to production

Roboflow offers a range of SDKs with which you can deploy your model to production.

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Faster R-CNN Annotation Format

Faster R-CNN

uses the

Tensorflow TFRecord

annotation format. If your annotation is in a different format, you can use Roboflow's annotation conversion tools to get your data into the right format.
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Convert data between formats

Label data automatically with Faster R-CNN

You can automatically label a dataset using

Faster R-CNN

with help from Autodistill, an open source package for training computer vision models. You can label a folder of images automatically with only a few lines of code. Below, see our tutorials that demonstrate how to use

Faster R-CNN

to train a computer vision model.

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