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
aggregation-pipleline annotation-tool computer-vision crowdsourcing image-segmentation object-detection video-captioning
Last synced: 6 months ago
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Aggregation framework for annotating datasets in computer vision tasks (detection, segmentation, video captioning etc.)
- Host: GitHub
- URL: https://github.com/ai-forever/aggme
- Owner: ai-forever
- Created: 2024-09-17T20:31:40.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2024-11-06T09:15:51.000Z (11 months ago)
- Last Synced: 2024-11-06T10:21:54.027Z (11 months ago)
- Topics: aggregation-pipleline, annotation-tool, computer-vision, crowdsourcing, image-segmentation, object-detection, video-captioning
- Language: Python
- Homepage:
- Size: 4.4 MB
- Stars: 9
- Watchers: 6
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: readme.md
- License: license/en_us.pdf
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README
# 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`)
## 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+`
2. To use __AggMe__ with `Visualisation` module and see results, install `jupyter notebook`
```shell
$ pip install notebook
```
3. Simple code example to start using framework:
```python
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)
```
## Examples
For more information see `examples` dir.
## Authors and Credits
- [Alexander Kapitanov](https://www.linkedin.com/in/hukenovs)
- [Karina Kvanchiani](https://www.linkedin.com/in/kvanchiani)
- [Alexander Sautin](https://www.linkedin.com/in/alexander-sautin-b5039623b)
- [Viktoria Volf](https://ru.linkedin.com/in/%D0%B2%D0%B8%D0%BA%D1%82%D0%BE%D1%80%D0%B8%D1%8F-%D0%B2%D0%BE%D0%BB%D1%8C%D1%84-080211217)
## Links
- [Majority vote for data annotation in computer vision tasks](https://habr.com/ru/companies/sberdevices/articles/747464/)
- [A recipe for perfect markup in computer vision](https://habr.com/ru/companies/oleg-bunin/articles/833994/)
## License
This work is licensed under a variant of Creative Commons Attribution-ShareAlike 4.0 International License.
Please see the specific [license](https://github.com/ai-forever/aggme/blob/master/license/en_us.pdf).