Popular GitHub Repositories for Fpn

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

Segmentation models with pretrained backbones. Keras and TensorFlow Keras.

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.

R2CNN: Rotational Region CNN Based on FPN (Tensorflow)

NAS-FPN: Learning Scalable Feature Pyramid Architecture for Object Detection.

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

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

Scalable Instance Segmentation using PyTorch & PyTorch Lightning.

Experiments with UNET/FPN models and cityscapes/kitti datasets [Pytorch]

[BMVC-20] Official PyTorch implementation of PPDet.

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

Faster R-CNN R-101-FPN model was implemented with TensorFlow2.0 eager execution.

This repository has been moved. The new location is in https://github.com/TexasInstruments/edgeai-tensorlab

Pytorch implementation(LeNet, VGGNet, GAN, UNet, Mask R-CNN, ...)

Tiny ResNet inspired FPN network (<2M params) for Rotated Object Detection using 5-parameter Modulated Rotation Loss

Fine-tuning Resnet-50 FPN based DNN for road crack detection on RDD2022

Distinguishing Urban Roads from Non-Road Regions in the Indian Driving Dataset(IDD) Using Binary Deep Learning Segmentation ?️

Object Detection With PyTorch and Custom the Model By Flask is an end-to-end solution for deploying object detection models using PyTorch, with an easy-to-use Flask web interface for serving predictions. The project leverages the power of the Faster R-CNN model with a ResNet-50-FPN-V2 backbone to accurately detect and localize objects in images.

Image Segmentation using Oxford Pets Dataset with the goal to improve animal tranquilizer aiming system.