Robo-Semantic-Segmentation VS torch-metrics

Compare Robo-Semantic-Segmentation vs torch-metrics and see what are their differences.

Robo-Semantic-Segmentation

Just a simple semantic segmentation library that I developed to speed up the image segmentation pipeline (by The-ML-Hero)
Deep Learning deep-neural-networks Deeplearning Computer Vision semantic-segmentation Segmentation Image processing Python Python3 Pytorch pytorch-implementation
Source Code
the-ml-hero.github.io
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torch-metrics

Metrics for model evaluation in pytorch (by enochkan)
Pytorch Machine Learning Deep Learning Metrics pytorch-implementation pytorch-cnn Computer Vision torch-metrics
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Robo-Semantic-Segmentation torch-metrics
1 2
0 109
- -
0.0 1.8
about 3 years ago about 3 years ago
Python Python
MIT License MIT License
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Robo-Semantic-Segmentation

Posts with mentions or reviews of Robo-Semantic-Segmentation. We have used some of these posts to build our list of alternatives and similar projects.
  • Where do I even start? Image segmentation.
    1 project | /r/learnprogramming | 27 Jan 2021
    Hi, you can look at this https://github.com/The-ML-Hero/Robo-Semantic-Segmentation/ which is my GitHub repo. This repo is all about segmentation specifically semantic segmentation, I have a couple of questions where did you get the dataset? and do you have the dataset ready?. But before you use the code be sure to understand the workings of semantic image segmentation architectures. The repo is implemented in Pytorch which is in the python language.

torch-metrics

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

What are some alternatives?

When comparing Robo-Semantic-Segmentation and torch-metrics you can also consider the following projects:

ros-semantic-segmentation-pytorch - Pytorch implementation of Semantic Segmentation in ROS on MIT ADE20K dataset based on semantic-segmentation-pytorch by CSAIL

ignite - High-level library to help with training and evaluating neural networks in PyTorch flexibly and transparently.

PyTorch-VAE - A Collection of Variational Autoencoders (VAE) in PyTorch.

ALAE - [CVPR2020] Adversarial Latent Autoencoders

ludwig - Low-code framework for building custom LLMs, neural networks, and other AI models

torch-points3d - Pytorch framework for doing deep learning on point clouds.

onemetric - One Metrics Library to Rule Them All!

Implicit-Internal-Video-Inpainting - [ICCV 2021]: IIVI: Internal Video Inpainting by Implicit Long-range Propagation

d2l-en - Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 500 universities from 70 countries including Stanford, MIT, Harvard, and Cambridge.

TTNet-Real-time-Analysis-System-for-Table-Tennis-Pytorch - Unofficial implementation of "TTNet: Real-time temporal and spatial video analysis of table tennis" (CVPR 2020)

datasets - 🤗 The largest hub of ready-to-use datasets for ML models with fast, easy-to-use and efficient data manipulation tools

Robo-Semantic-Segmentation vs ros-semantic-segmentation-pytorch torch-metrics vs ignite Robo-Semantic-Segmentation vs PyTorch-VAE torch-metrics vs ALAE Robo-Semantic-Segmentation vs ludwig torch-metrics vs PyTorch-VAE Robo-Semantic-Segmentation vs torch-points3d torch-metrics vs onemetric Robo-Semantic-Segmentation vs Implicit-Internal-Video-Inpainting torch-metrics vs d2l-en Robo-Semantic-Segmentation vs TTNet-Real-time-Analysis-System-for-Table-Tennis-Pytorch torch-metrics vs datasets