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 timelineoraudio timeline)
- Install the AggMe package. Run the following command in the command shell:
$ pip install aggmeNote that aggme framework supports only python=3.10+
- To use AggMe with
Visualisationmodule and see results, installjupyter notebook
$ pip install notebook- Simple code example to start using framework:
from aggme.aggregation import BboxAggregation
from aggme.utils import AnnotationData
from aggme.utils.processing import toloka_bboxes_to_template
import pandas as pd
df = pd.read_csv("markups.csv") # read dataframe with annotations
data = AnnotationData(markup_type='bboxes') # init aggregator
data.load_markups(df, toloka_bboxes_to_template)
aggregator = BboxAggregation()
results = aggregator.get_aggregation_results(data)For more information see examples dir.
- Majority vote for data annotation in computer vision tasks
- A recipe for perfect markup in computer vision
This work is licensed under a variant of Creative Commons Attribution-ShareAlike 4.0 International License.
Please see the specific license.