InteractiveAnnotation VS Entity

Compare InteractiveAnnotation vs Entity and see what are their differences.

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InteractiveAnnotation Entity
1 2
20 667
- 2.2%
7.2 6.9
about 1 year ago 5 months ago
Jupyter Notebook Jupyter Notebook
- GNU General Public License v3.0 or later
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.

InteractiveAnnotation

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

Entity

Posts with mentions or reviews of Entity. We have used some of these posts to build our list of alternatives and similar projects.
  • Open-world entity segmentation: eliminates the thing-stuff distinction!
    1 project | /r/u_waynerad | 8 Aug 2021
  • [R] Open-World Entity Segmentation (Better dense image segmentation without labels)
    1 project | /r/MachineLearning | 31 Jul 2021
    Abstract: We introduce a new image segmentation task, termed Entity Segmentation (ES) with the aim to segment all visual entities in an image without considering semantic category labels. It has many practical applications in image manipulation/editing where the segmentation mask quality is typically crucial but category labels are less important. In this setting, all semantically-meaningful segments are equally treated as categoryless entities and there is no thing-stuff distinction. Based on our unified entity representation, we propose a center-based entity segmentation framework with two novel modules to improve mask quality. Experimentally, both our new task and framework demonstrate superior advantages as against existing work. In particular, ES enables the following: (1) merging multiple datasets to form a large training set without the need to resolve label conflicts; (2) any model trained on one dataset can generalize exceptionally well to other datasets with unseen domains. Our code is made publicly available at this https URL.

What are some alternatives?

When comparing InteractiveAnnotation and Entity you can also consider the following projects:

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

flying-guide-dog - Official implementation of "Flying Guide Dog: Walkable Path Discovery for the Visually Impaired Utilizing Drones and Transformer-based Semantic Segmentation", IEEE ROBIO 2021

mask-rcnn - Mask-RCNN training and prediction in MATLAB for Instance Segmentation

Open3D-ML - An extension of Open3D to address 3D Machine Learning tasks

fashion-segmentation - A tensorflow model for segmentation of fashion items out of multiple product images

AgML - AgML is a centralized framework for agricultural machine learning. AgML provides access to public agricultural datasets for common agricultural deep learning tasks, with standard benchmarks and pretrained models, as well the ability to generate synthetic data and annotations.