AgML VS labelme2coco

Compare AgML vs labelme2coco and see what are their differences.

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. (by Project-AgML)
Deep Learning Agriculture Pytorch Dataset image-classification object-detection semantic-segmentation Computer Vision synthetic-data
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labelme2coco

A lightweight package for converting your labelme annotations into COCO object detection format. (by fcakyon)
labelme Coco object-detection instance-segmentation semantic-segmentation image-classification Deep Learning Machine Learning Computer Vision Windows Linux MacOS Python annotation-conversion
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AgML labelme2coco
1 1
156 247
5.8% -
7.5 3.8
14 days ago 26 days ago
Python Python
Apache License 2.0 GNU General Public License v3.0 only
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AgML

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

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.

What are some alternatives?

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

gluon-cv - Gluon CV Toolkit

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

ScanRefer - [ECCV 2020] ScanRefer: 3D Object Localization in RGB-D Scans using Natural Language

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

cvat - Annotate better with CVAT, the industry-leading data engine for machine learning. Used and trusted by teams at any scale, for data of any scale. [Moved to: https://github.com/cvat-ai/cvat]

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

pytorch-grad-cam - Advanced AI Explainability for computer vision. Support for CNNs, Vision Transformers, Classification, Object detection, Segmentation, Image similarity and more.

coco-viewer - Minimalistic COCO Dataset Viewer in Tkinter

Entity - EntitySeg Toolbox: Towards Open-World and High-Quality Image Segmentation

mmsegmentation - OpenMMLab Semantic Segmentation Toolbox and Benchmark.

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

autogluon - Fast and Accurate ML in 3 Lines of Code

AgML vs gluon-cv labelme2coco vs labelme AgML vs ScanRefer labelme2coco vs albumentations AgML vs cvat labelme2coco vs bpycv AgML vs pytorch-grad-cam labelme2coco vs coco-viewer AgML vs Entity labelme2coco vs mmsegmentation AgML vs PixelLib labelme2coco vs autogluon