labelme2coco VS HugsVision

Compare labelme2coco vs HugsVision and see what are their differences.

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labelme2coco HugsVision
1 1
239 188
- -
3.9 0.0
5 months ago 8 months ago
Python Jupyter Notebook
GNU General Public License v3.0 only 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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labelme2coco

Posts with mentions or reviews of labelme2coco. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2021-02-12.
  • What's A Simple Custom Segmentation Pipeline?
    3 projects | /r/computervision | 12 Feb 2021
    I would also suggest labelme, it's pretty easy to use. Just type "labelme" in the shell after pip installing and you will see the GUI. There are tools to convert to coco format (like https://github.com/fcakyon/labelme2coco) if needed, for instance for Detectron2.

HugsVision

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

We haven't tracked posts mentioning HugsVision yet.
Tracking mentions began in Dec 2020.

What are some alternatives?

When comparing labelme2coco and HugsVision you can also consider the following projects:

labelme - Image Polygonal Annotation with Python (polygon, rectangle, circle, line, point and image-level flag annotation).

albumentations - Fast image augmentation library and an easy-to-use wrapper around other libraries. Documentation: https://albumentations.ai/docs/ Paper about the library: https://www.mdpi.com/2078-2489/11/2/125

bpycv - Computer vision utils for Blender (generate instance annoatation, depth and 6D pose by one line code)

coco-viewer - Minimalistic COCO Dataset Viewer in Tkinter

mmsegmentation - OpenMMLab Semantic Segmentation Toolbox and Benchmark.

autogluon - AutoGluon: AutoML for Image, Text, Time Series, and Tabular Data

Real-time-Object-Detection-for-Autonomous-Driving-using-Deep-Learning - My Computer Vision project from my Computer Vision Course (Fall 2020) at Goethe University Frankfurt, Germany. Performance comparison between state-of-the-art Object Detection algorithms YOLO and Faster R-CNN based on the Berkeley DeepDrive (BDD100K) Dataset.

poolformer - PoolFormer: MetaFormer Is Actually What You Need for Vision (CVPR 2022 Oral)

fashionpedia-api - Python API for Fashionpedia Dataset

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

ganspace - Discovering Interpretable GAN Controls [NeurIPS 2020]

CoordConv