Vision-Project-Image-Segmentation VS Human-Segmentation-PyTorch

Compare Vision-Project-Image-Segmentation vs Human-Segmentation-PyTorch and see what are their differences.

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Vision-Project-Image-Segmentation Human-Segmentation-PyTorch
1 1
0 533
- -
2.6 0.0
about 3 years ago over 1 year ago
Jupyter Notebook Jupyter Notebook
MIT License -
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

Vision-Project-Image-Segmentation

Posts with mentions or reviews of Vision-Project-Image-Segmentation. We have used some of these posts to build our list of alternatives and similar projects.

Human-Segmentation-PyTorch

Posts with mentions or reviews of Human-Segmentation-PyTorch. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2021-11-30.

What are some alternatives?

When comparing Vision-Project-Image-Segmentation and Human-Segmentation-PyTorch you can also consider the following projects:

HugsVision - HugsVision is a easy to use huggingface wrapper for state-of-the-art computer vision

PixelLib - Visit PixelLib's official documentation https://pixellib.readthedocs.io/en/latest/

rankseg - [JMLR 2023] RankSEG: A consistent ranking-based framework for segmentation

unet - unet for image segmentation

HRNet-Semantic-Segmentation - The OCR approach is rephrased as Segmentation Transformer: https://arxiv.org/abs/1909.11065. This is an official implementation of semantic segmentation for HRNet. https://arxiv.org/abs/1908.07919

Deep-Learning-In-Production - Build, train, deploy, scale and maintain deep learning models. Understand ML infrastructure and MLOps using hands-on examples.

SipMask - SipMask: Spatial Information Preservation for Fast Image and Video Instance Segmentation (ECCV2020)

Real-time-Semantic-Segmentation

JejuNet - Real-Time Video Segmentation on Mobile Devices with DeepLab V3+, MobileNet V2. Worked on the project in 🏝 Jeju island