Segmentation models with pretrained backbones. PyTorch.
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Updated
May 16, 2024 - Python
Segmentation models with pretrained backbones. PyTorch.
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.
R-DFPN: Rotation Dense Feature Pyramid Networks (Tensorflow)
A Python Library for High-Level Semantic Segmentation Models based on TensorFlow and Keras with pretrained backbones.
Implement of paper 《Attention-guided Context Feature Pyramid Network for Object Detection》
QuarkDet lightweight object detection in PyTorch .Real-Time Object Detection on Mobile Devices.
An easy implementation of FPN (https://arxiv.org/pdf/1612.03144.pdf) in PyTorch.
Mask R-CNN, FPN, LinkNet, PSPNet and UNet with multiple backbone architectures support readily available
Instance Segmentation with PyTorch & PyTorch Lightning.
[BMVC-20] Official PyTorch implementation of PPDet.
This repository has been moved. The new location is in https://github.com/TexasInstruments/edgeai-tensorlab
1D and 2D Segmentation Models with options such as Deep Supervision, Guided Attention, BiConvLSTM, Autoencoder, etc.
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