SegmentationCpp VS torchRL

Compare SegmentationCpp vs torchRL and see what are their differences.

SegmentationCpp

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. (by AllentDan)

torchRL

TorchRL is a C++ reinforcement library using PyTorch C++ backend LibTorch (by adepierre)
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SegmentationCpp torchRL
3 1
402 4
- -
3.4 0.0
5 months ago almost 2 years ago
C++ C++
MIT License MIT License
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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SegmentationCpp

Posts with mentions or reviews of SegmentationCpp. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2021-02-23.

torchRL

Posts with mentions or reviews of torchRL. We have used some of these posts to build our list of alternatives and similar projects.

What are some alternatives?

When comparing SegmentationCpp and torchRL you can also consider the following projects:

segmentation_models.pytorch - Segmentation models with pretrained backbones. PyTorch.

LibtorchTutorials - This is a code repository for pytorch c++ (or libtorch) tutorial.

Drogon-torch-serve - Serve pytorch / torch models using Drogon

tensorrtx - Implementation of popular deep learning networks with TensorRT network definition API

muzero-cpp - A C++ pytorch implementation of MuZero

segmentation_models - Segmentation models with pretrained backbones. Keras and TensorFlow Keras.

snakeAI - testing MLP, DQN, PPO, SAC, policy-gradient by snake

Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration

open_spiel - OpenSpiel is a collection of environments and algorithms for research in general reinforcement learning and search/planning in games.

vowpal_wabbit - Vowpal Wabbit is a machine learning system which pushes the frontier of machine learning with techniques such as online, hashing, allreduce, reductions, learning2search, active, and interactive learning.