#### NOTE
This is a **Hugging Face dataset**. For large datasets, ensure `huggingface_hub>=1.1.3` to avoid rate limits. Learn more in the <a href="https://docs.voxel51.com/integrations/huggingface.html#loading-datasets-from-the-hub" target="_blank">Hugging Face integration docs</a>.

<a href="https://huggingface.co/datasets/Voxel51/Coursera_lecture_dataset_test" target="_blank">![Hugging Face](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Dataset-yellow)</a>

# Dataset Card for Lecture Test Set for Coursera MOOC - Hands Data Centric Visual AI

This dataset is the **test dataset for the in-class lectures** of the Hands-on Data Centric Visual  AI Coursera course.

This is a [FiftyOne](https://github.com/voxel51/fiftyone) dataset with 4159 samples.

## Installation

If you haven’t already, install FiftyOne:

```bash
pip install -U fiftyone
```

## Usage

```python
import fiftyone as fo
import fiftyone.utils.huggingface as fouh

# Load the dataset
# Note: other available arguments include 'max_samples', etc
dataset = fouh.load_from_hub("Voxel51/Coursera_lecture_dataset_test")

# Launch the App
session = fo.launch_app(dataset)
```

## Dataset Details

### Dataset Description

This dataset is a modified subset of the [LVIS dataset](https://www.lvisdataset.org/).

The dataset here only contains detections; **NONE** of the test set’s labels have been artificially perturbed.

This dataset has the following labels:

- ‘jacket’
- ‘coat’
- ‘jean’
- ‘trousers’
- ‘short_pants’
- ‘trash_can’
- ‘bucket’
- ‘flowerpot’
- ‘helmet’
- ‘baseball_cap’
- ‘hat’
- ‘sunglasses’
- ‘goggles’
- ‘doughnut’
- ‘pastry’
- ‘onion’
- ‘tomato’

### Dataset Sources [optional]

- **Repository:** https://www.lvisdataset.org/
- **Paper:** https://arxiv.org/abs/1908.03195

## Uses

The labels in this dataset have been **NOT** perturbed, unlike the corresponding training dataset.

## Dataset Structure

Each image in the dataset comes with detailed annotations in FiftyOne detection format. A typical annotation looks like this:

```python
<Detection: {
    'id': '66a2f24cce2f9d11d98d39f3',
    'attributes': {},
    'tags': [],
    'label': 'trousers',
    'bounding_box': [
        0.5562343750000001,
        0.4614166666666667,
        0.1974375,
        0.29300000000000004,
    ],
    'mask': None,
    'confidence': None,
    'index': None,
}>
```

## Dataset Creation

### Curation Rationale

The selected labels for this dataset are because these objects can confuse a model. Thus, making them a great choice for demonstrating data centric AI techniques.

### Source Data

This is a subset of the [LVIS dataset.](https://www.lvisdataset.org/)

## Citation

**BibTeX:**

```bibtex
@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}
}
```