semvec/pypi-packages huggingface dataset:
oidv6-to-voc 0.1.5
Convert Open Images Dataset v6 to PASCAL VOC format.

# OIDv6 To VOC

Convert bounding box datasets of [Open Images Dataset v6](https://storage.googleapis.com/openimages/web/index.html) to VOC XML format.

## Installation

```sh
pip3 install oidv6-to-voc
```

## Usage

Once installed, you should be able to run it directly:

```sh
oidv6-to-voc -h
```

If your shell cannot find the command, try running it with:

```sh
python3 -m oidv6_to_voc -h
```

### CLI options

To start converting, you need at least a part of the images, the class names metadata *and* at least one of the boxes annotation CSV file:

![CSV files you need](https://user-images.githubusercontent.com/31200881/95124534-2902e600-0786-11eb-8702-4fbde2ef3aee.png)

```sh
oidv6-to-voc <annotation-file(s).csv>
             -d <class-names-file.csv> 
             --imgd <directory/to/your/images>
             --outd <your/output/diretory>
```

## About the Dataset

The Open Images V6 Dataset contains 600 classes with 1900000+ images. The images are hosted on AWS, and the CSV files can be [downloaded here](https://storage.googleapis.com/openimages/web/download.html).

To download it in full, you'll need 500+ GB of disk space. For downloading a part of the dataset only, I would recommend the [DmitryRyumin/OIDv6](https://github.com/DmitryRyumin/OIDv6) tool.

## Credit

This repo is forked from [AtriSaxena/OIDv4_to_VOC](https://github.com/AtriSaxena/OIDv4_to_VOC).
poet-estimator 0.1
Python Implementation for Large Covariance Estimation by Thresholding Principal Orthogonal Complements

# POET
Python Implementation for Large Covariance Estimation by Thresholding Principal Orthogonal Complements

References:
1. https://arxiv.org/abs/1201.0175
2. https://cran.r-project.org/web/packages/POET/