Which dataset is best for object detection?
Introduction
- CIFAR-10: CIFAR-10 is a comprehensive dataset that consists of 60,000 colour images in 10 different categories.
- LISA Traffic Sign Detection Dataset:
- Open Images:
- The 20BN-SOMETHING-SOMETHING Dataset V2:
- ImageNet:
- BDD100K:
- DOTA:
- MaskedFaceNet:
How would you prepare a dataset for object detection?
Procedure
- From the cluster management console, select Workload > Spark > Deep Learning.
- Select the Datasets tab.
- Click New.
- Create a dataset from Images for Object Detection.
- Provide a dataset name.
- Specify a Spark instance group.
- Provide a training folder.
- Provide the percentage of training images for validation.
What are the best image datasets for object detection?
MS COCO: MS COCO is among the most detailed image datasets as it features a large-scale object detection, segmentation, and captioning dataset of over 200,000 labeled images. Lego Bricks: This image dataset contains 12,700 images of Lego bricks that have each been previously classified and rendered using
What’s in the home objects dataset?
Home Objects: Contains commonly found objects from around the house. Celebfaces: This image dataset features over 200,000 images of your favorite celebrities. Each celebrity comage comes with 40 attribute annotations. Stanford Dogs Dataset: 20,580 images of dogs across 120 unique breed categories with roughly 150 images for each class.
What can a data scientist do with an image data set?
With the right image datasets a data scientist can teach a computer to essentially function as though it had eyes of its own. This technology forms the backbone for many of tomorrow’s breakthroughs and innovations like facial recognition and autonomous vehicles.
What is 5050+ object detection datasets?
50+ Object Detection Datasets from different industry domains was originally published in Towards AI — Multidisciplinary Science Journal on Medium, where people are continuing the conversation by highlighting and responding to this story. A Probabilistic Algorithm to Reduce Dimensions: t — Distributed Stochastic Neighbor Embedding…