metadata

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




  This is a [FiftyOne](https://github.com/voxel51/fiftyone) dataset with 30000
  samples.


  ## Installation


  If you haven't already, install FiftyOne:


  ```bash

  pip install -U fiftyone

  ```


  ## Usage


  ```python

  import fiftyone as fo

  from fiftyone.utils.huggingface import load_from_hub


  # Load the dataset

  # Note: other available arguments include 'max_samples', etc

  dataset =
  load_from_hub("harpreetsahota/Data-Centric-Visual-AI-Challenge-Train-Set")


  # Launch the App

  session = fo.launch_app(dataset)

  ```

Dataset Card for Data-Centric-Visual-AI-Train-Set

This is a FiftyOne dataset with 30,000 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/Data-Centric-Visual-AI-Challenge-Train-Set")

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

Dataset Overview

Welcome to our Data Curation Challenge for Object Detection! This page provides essential information about the dataset you'll be working with.

These images are a subset from Open Images v7 .

Full information about the dataset can be found here.

Dataset Description

Our dataset is a custom collection of images specifically curated for this competition. It focuses on everyday objects, people, and transportation, with a particular emphasis on clothing and accessories.

Key details:

Total images: 30,000
Image format: JPEG
Annotation format: FiftyOne detection format

Object Classes

The dataset includes 25 object classes across several categories:

Transportation: Airplane, Truck, Van, Ambulance, Helicopter, Motorcycle, Bicycle, Unicycle, Bus, Taxi, Balloon
Electronics: Computer monitor, Laptop, Mobile phone, Tablet phone
Sports Equipment: Tennis ball, Tennis racket, Table tennis racket, Golf ball, Ball, Rugby ball, Football, Kite, Volleyball (Ball)
Food: Hamburger, Hot dog

Annotations

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

<Detection: {
    'id': '66a037ceef34f40a421a9810',
    'attributes': {},
    'tags': [],
    'label': 'Jeans',
    'bounding_box': [0.446875, 0.36773, 0.16562500000000002, 0.321763],
    'mask': None,
    'confidence': None,
    'index': None,
    'IsOccluded': True,
    'IsTruncated': False,
    'IsGroupOf': False,
    'IsDepiction': False,
    'IsInside': False,
}>

Key annotation features:

Bounding box coordinates (normalized)
Object class labels
Occlusion and truncation flags
Group, depiction, and inside flags

Your Task

Your challenge is to curate a subset of this dataset that:

Reduces the overall size of the dataset
Maintains or improves the performance of an object detection model (YOLOv8m)

Remember, the goal is to find the optimal balance between dataset size and model performance, as measured by our evaluation metric:

Score = (mAP * log(N)) / N

Where mAP is the Mean Average Precision on our hidden test set, and N is the number of images in your curated dataset.

Additional Notes

Ensure you comply with all dataset usage terms and conditions
Do not use external data sources for this challenge

Good luck, and happy curating!

Citations

The Open Images v4 paper can be found here

@article{OpenImages,
  author = {Alina Kuznetsova and Hassan Rom and Neil Alldrin and Jasper Uijlings and Ivan Krasin and Jordi Pont-Tuset and Shahab Kamali and Stefan Popov and Matteo Malloci and Alexander Kolesnikov and Tom Duerig and Vittorio Ferrari},
  title = {Open Images Dataset V6: A Large-Scale Dataset for Object Detection in the Wild.}, {Open Images Dataset V7: A Large-Scale Dataset for Object Detection in the Wild.}
  year = {2020},
  journal = {IJCV}
}