metadata

annotations_creators: []
language: en
size_categories:
  - 10K<n<100K
task_categories:
  - object-detection
task_ids: []
pretty_name: lecture_dataset_train
tags:
  - fiftyone
  - image
  - object-detection
dataset_summary: >




  This is a [FiftyOne](https://github.com/voxel51/fiftyone) dataset with 16638
  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_train")


  # Launch the App

  session = fo.launch_app(dataset)

  ```

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

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

This is a FiftyOne dataset with 16638 samples.

Installation

If you haven't already, install FiftyOne:

pip install -U fiftyone

Usage

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_train")

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

Dataset Details

Dataset Description

This dataset is a modified subset of the LVIS dataset.

The dataset here only contains detections, some of which have been artificially perturbed and altered to demonstrate data centric AI techniques and methodologies for the course.

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 perturbed to illustrate data centric AI techniques for the Hands-on Data Centric AI Coursera MOOC.

Dataset Structure

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

<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 is due to the fact that these objects can be confusing to a model. Thus, making them a great choice for demonstrating data centric AI techniques.

[More Information Needed]

Source Data

This is a subset of the LVIS dataset.

Citation

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}
}