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https://github.com/ai-forever/aggme

Aggregation framework for annotating datasets in computer vision tasks (detection, segmentation, video captioning etc.)

https://github.com/ai-forever/aggme

Science Score: 13.0%

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Keywords

aggregation-pipleline annotation-tool computer-vision crowdsourcing image-segmentation object-detection video-captioning
Last synced: 5 months ago · JSON representation

Repository

Aggregation framework for annotating datasets in computer vision tasks (detection, segmentation, video captioning etc.)

Basic Info
  • Host: GitHub
  • Owner: ai-forever
  • Language: Python
  • Default Branch: main
  • Homepage:
  • Size: 4.4 MB
Statistics
  • Stars: 10
  • Watchers: 5
  • Forks: 1
  • Open Issues: 0
  • Releases: 0
Topics
aggregation-pipleline annotation-tool computer-vision crowdsourcing image-segmentation object-detection video-captioning
Created over 1 year ago · Last pushed over 1 year ago
Metadata Files
Readme License

readme.md

AggMe

AggMe is a python framework for aggregating overlapped annotations in computer vision tasks (object detection, semantic or instance image segmentation, video annotation).

It allows you to aggregate the annotations from crowdsourcing platforms or generated from neural networks. Current version of AggMe supports several types of data:

  • Bounding Boxes (images)
  • Segmentation Masks (images)
  • Time Intervals (video timeline or audio timeline)

title

Get Started

  1. Install the AggMe package. Run the following command in the command shell: shell $ pip install aggme

Note that aggme framework supports only python=3.10+

  1. To use AggMe with Visualisation module and see results, install jupyter notebook shell $ pip install notebook

  2. Simple code example to start using framework: ```python from aggme.aggregation import BboxAggregation from aggme.utils import AnnotationData from aggme.utils.processing import tolokabboxesto_template import pandas as pd

df = pd.readcsv("markups.csv") # read dataframe with annotations data = AnnotationData(markuptype='bboxes') # init aggregator

data.loadmarkups(df, tolokabboxestotemplate)

aggregator = BboxAggregation() results = aggregator.getaggregationresults(data) ```

Examples

For more information see examples dir.

example

Authors and Credits

Links

License

Creative Commons License
This work is licensed under a variant of Creative Commons Attribution-ShareAlike 4.0 International License.

Please see the specific license.

Owner

  • Name: AI Forever
  • Login: ai-forever
  • Kind: organization
  • Location: Armenia

Creating ML for the future. AI projects you already know. We are non-profit organization with members from all over the world.

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Dependencies

pyproject.toml pypi
requirements.txt pypi
  • matplotlib ==3.9.0
  • numpy ==1.26.4
  • opencv-python ==4.10.0.84
  • pandas ==2.2.2
  • pillow ==10.4.0
  • scikit-learn ==1.5.0
  • shapely ==2.0.4
  • tqdm ==4.66.4
setup.py pypi
  • requirements *