Semantic segmentation models with 500+ pretrained convolutional and transformer-based backbones.

Segmentation models with pretrained backbones. Keras and TensorFlow Keras.

Feature Pyramid Networks for Object Detection

A Tensorflow implementation of FPN detection framework.

R2CNN: Rotational Region CNN Based on FPN (Tensorflow)

This is a tensorflow re-implementation of Feature Pyramid Networks for Object Detection.

A c++ trainable semantic segmentation library based on libtorch (pytorch c++). Backbone: VGG, ResNet, ResNext. Architecture: FPN, U-Net, PAN, LinkNet, PSPNet, DeepLab-V3, DeepLab-V3+ by now.

Faster R-CNN / R-FCN 💡 C++ version based on Caffe

A semantic segmentation toolbox based on PyTorch

FasterRCNN is implemented in VGG, ResNet and FPN base.

A Python Library for High-Level Semantic Segmentation Models based on TensorFlow and Keras with pretrained backbones.

R-DFPN: Rotation Dense Feature Pyramid Networks (Tensorflow)

Implement of paper 《Attention-guided Context Feature Pyramid Network for Object Detection》

An easy implementation of FPN (https://arxiv.org/pdf/1612.03144.pdf) in PyTorch.

QuarkDet lightweight object detection in PyTorch .Real-Time Object Detection on Mobile Devices.

Scalable Instance Segmentation using PyTorch & PyTorch Lightning.

[BMVC-20] Official PyTorch implementation of PPDet.

Mask R-CNN, FPN, LinkNet, PSPNet and UNet with multiple backbone architectures support readily available

1D and 2D Segmentation Models with options such as Deep Supervision, Guided Attention, BiConvLSTM, Autoencoder, etc.

Mask R-CNN creates a high-quality segmentation mask in addition to the Faster R-CNN network. In addition to class labels and scores, a segmentation mask is created for the objects detected by this neural network. In this repository, using Anaconda prompt step by step Mask R-CNN setup is shown.