LVIS (pronounced ‘el-vis’): is a new dataset for Large Vocabulary Instance Segmentation. When complete, it will feature more than 2 million high-quality instance segmentation masks for over 1200 entry-level object categories in 164k images. The LVIS API enables reading and interacting with annotation files, visualizing annotations, and evaluating results.
For this release, we have annotated 159,623 images (100k train, 20k val, 20k test-dev, 20k test-challenge). Release v1.0 is publicly available at LVIS website and will be used in the second LVIS Challenge to be held at Joint COCO and LVIS Workshop at ECCV 2020.
You can setup a virtual environment and then install lvisapi using pip:
python3 -m venv env # Create a virtual environment
source env/bin/activate # Activate virtual environment
# install COCO API. COCO API requires numpy to install. Ensure that you installed numpy.
pip install 'git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI'
# install LVIS API
pip install lvis
# Work for a while ...
deactivate # Exit virtual environmentYou can also clone the repo first and then do the following steps inside the repo:
python3 -m venv env # Create a virtual environment
source env/bin/activate # Activate virtual environment
# install COCO API. COCO API requires numpy to install. Ensure that you installed numpy.
pip install 'git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI'
# install LVIS API
pip install .
# test if the installation was correct
python test.py
# Work for a while ...
deactivate # Exit virtual environmentIf you find this code/data useful in your research then please cite our paper:
@inproceedings{gupta2019lvis,
title={{LVIS}: A Dataset for Large Vocabulary Instance Segmentation},
author={Gupta, Agrim and Dollar, Piotr and Girshick, Ross},
booktitle={Proceedings of the {IEEE} Conference on Computer Vision and Pattern Recognition},
year={2019}
}
The code is a re-write of PythonAPI for COCO. The core functionality is the same with LVIS specific changes.