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This is a fresh implementation of the Faster R-CNN object detection model in both PyTorch and TensorFlow 2 with Keras, using Python 3.7 or higher. Although several years old now, Faster R-CNN remains a foundational work in the field and still influences modern object detectors.
Run fetch_data/fetch_faster_rcnn_final_model.m to download our trained models. Run experiments/script_faster_rcnn_demo.m to test a single demo image. You will see the timing information as below.
Faster-RCNN introduces the Region of Proposal Network (RPN) and reuses the same CNN results for the same proposal instead of running a selective search algorithm. The RPN is trained end-to-end to generate high-quality region proposals, which Fast R-CNN uses for detection.
The following model builders can be used to instantiate a Faster R-CNN model, with or without pre-trained weights. All the model builders internally rely on the torchvision.models.detection.faster_rcnn.FasterRCNN base class.
Faster R-CNN is an object detection model that improves on Fast R-CNN by utilising a region proposal network (RPN) with the CNN model. The RPN shares full-image convolutional features with the detection network, enabling nearly cost-free region proposals.
- 26
- 0.02
- fasterrcnn_mobilenet_v3_large_320_fpn
- Weight Decay, SGD with Momentum
Download the pretrained model from torchvision with the following code: import torchvision model = torchvision.models.detection.fasterrcnn_resnet50_fpn(pretrained=True) model.eval() Line 2 will download a pretrained Resnet50 Faster R-CNN model with pretrained weights.
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Jun 4, 2015 · View a PDF of the paper titled Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks, by Shaoqing Ren and 3 other authors. State-of-the-art object detection networks depend on region proposal algorithms to hypothesize object locations.
