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

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

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

This is a [FiftyOne](https://github.com/voxel51/fiftyone) dataset with 4572 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_homework_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 which have been artificially perturbed.

This dataset has the following labels:

- ‘bolt’
- ‘knob’
- ‘tag’
- ‘button’
- ‘bottle_cap’
- ‘belt’
- ‘strap’
- ‘necktie’
- ‘shirt’
- ‘sweater’
- ‘streetlight’
- ‘pole’
- ‘reflector’
- ‘headlight’
- ‘taillight’
- ‘traffic_light’
- ‘rearview_mirror’

### Dataset Sources

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

## Uses

Unlike the [training dataset](https://huggingface.co/datasets/Voxel51/Coursera_homework_dataset_train) for the course, the labels in this dataset **HAVE NOT** been perturbed.

## Dataset Structure

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

```python
<Detection: {
    'id': '66a2f24cce2f9d11d98d3a21',
    'attributes': {},
    'tags': [],
    'label': 'shirt',
    'bounding_box': [
        0.25414,
        0.35845238095238097,
        0.041960000000000004,
        0.051011904761904765,
    ],
    'mask': None,
    'confidence': None,
    'index': None,
}>
```

## Dataset Creation

### Curation Rationale

The selected labels for this dataset is because these objects can be confusing to 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}
}
```