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9 ???? Object Detection Datasets
Latest   Machine Learning

9 ???? Object Detection Datasets

Last Updated on July 20, 2023 by Editorial Team

Author(s): Akula Hemanth Kumar

Originally published on Towards AI.

Photo by Jimmy Chang on Unsplash

Starter code Available using Monk Libraries

In this article, I am going to share a few datasets for Object Detection. Starter code is provided in Github and you can directly run them in Colab.

P.S: Description of dataset is taken directly from the websites.

  1. Traffic Sign Recognition U+1F6A6U+1F6B3

The LISA Traffic Sign Dataset is a set of videos and annotated frames containing US traffic signs.

2. Exclusively-Dark-Image-Dataset U+1F9B8‍U+2642️

It is the largest collection of low-light images taken in very low-light environments to twilight (i.e 10 different conditions) to-date with image class and object-level annotations.

3.WGISD U+1F347

It provides images and annotations to study object detection and instance segmentation for image-based monitoring and field robotics in viticulture.

4.TACO U+1F37E

TACO is an open image dataset of waste in the wild. It contains photos of litter taken under diverse environments. Annotations are provided in the COCO format.

5.Open Images Dataset

Open Image is a dataset of ~9M images annotated with image-level labels, object bounding boxes, object segmentation masks, visual relationships, and localized narratives. It contains a total of 16M bounding boxes for 600 object classes on 1.9M images, making it the largest existing dataset with object location annotations.

  • Monk Starter code for Billboard: Github
  • Monk Starter code for Football: Github

6.CAMEL Dataset U+1F42B

It provides visual-infrared object detection and tracking.

7.Playing Card

It provides playing cards object detection.

8.DeepFashion2 U+1F455

DeepFashion2 is a comprehensive fashion dataset. It contains 491K diverse images of 13 popular clothing categories from both commercial shopping stores and consumers.

9.WIDER Pedestrian Detection

The main goal of the WIDER Person Challenge is to address the problem of detecting pedestrians and cyclists in unconstrained environments.

I am extremely passionate about computer vision and deep learning. I am an open-source contributor to Monk Libraries.

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