R2CNN: Rotational Region CNN Based on FPN (Tensorflow)
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Updated
Mar 21, 2018 - Python
R2CNN: Rotational Region CNN Based on FPN (Tensorflow)
Feature Pyramid Networks for Object Detection
FasterRCNN is implemented in VGG, ResNet and FPN base.
MobileNetV2 architecture combined with a dynamically generated Feature Pyramid Network
R-DFPN: Rotation Dense Feature Pyramid Networks (Tensorflow)
Faster R-CNN / R-FCN 💡 C++ version based on Caffe
An easy implementation of FPN (https://arxiv.org/pdf/1612.03144.pdf) in PyTorch.
A Tensorflow implementation of FPN detection framework.
An implementation for fpn network with mxnet
R2CNN: Rotational Region CNN Based on FPN (Tensorflow)
Mask R-CNN, FPN, LinkNet, PSPNet and UNet with multiple backbone architectures support readily available
Caffe version Generalized & Distance & Complete Iou loss Implementation for Faster RCNN/FPN bbox regression
This is a tensorflow re-implementation of Feature Pyramid Networks for Object Detection.
one-stage and two-stage detectors and segmentation-based detectors
Monocular depth estimation using Feature Pyramid Network implemented in PyTorch 1.1.0
Implement of paper 《Attention-guided Context Feature Pyramid Network for Object Detection》
Pytorch Implementation of "Feature Pyramid Networks for Object Detection"
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