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.
Here is an overview of the
model:
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.
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:
Training a TensorFlow Faster R-CNN Object Detection Model on Your Own Dataset: https://blog.roboflow.com/training-a-tensorflow-faster-r-cnn-object-detection-model-on-your-own-dataset/
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.
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.
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.
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.
Roboflow offers a range of SDKs with which you can deploy your model to production.
Faster R-CNN
uses the
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.
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.
Curious about how this model compares to others? Check out our model comparisons.
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