Top 6 Open-Source Datasets for Computer Vision

Computer vision

Let’s see the top 6 open-source datasets for computer vision that deserve to be explored.

Computer vision is one of the top tech trends that has been booming these days. This technology is accelerating every domain in the industry helping organizations to revolutionize the way machines are being used. And to build a robust deep learning model for computer vision, one should apply high-quality datasets into the training phase. Let’s see the top 6 open-source datasets for computer vision that deserve to be explored.

  • ImageNet: It is an ongoing research effort aiming to provide researchers with an accessible image database. It is one of the most well-known image databases that is liked by researchers and learners alike and provides an average of 1000 images to illustrate each synset.
  • CIFAR-10 and CIFAR-100: CIFAR-10 and CIFAR-100 are a collection of images that are used to train machine learning and computer vision algorithms for beginners working in the field. These are also some of the most popular datasets for machine learning for quick comparison of algorithms as it captures the weaknesses and strengths without putting much burden on the parameter tuning process.
  • MS COCO: The MS COCO dataset, also known as the Microsoft Common Objects in Context, consists of 328K images. It annotates for object detection, key points detection, panoptic segmentation, captioning, and dense human pose estimation.
  • MPII Human Pose: This dataset is used for the evaluation of articulated human pose estimation. It consists of around 25K images comprising over 40K people with annotated body joints. Each image is extracted from different YouTube videos and is provided with preceding. Overall, the dataset covers around 410 humans and each image is labelled with a different activity.
  •  Barkley DeepDrive: This dataset is used for autonomous vehicle training. It comprises over 100K video sequences with diverse kinds of annotations like object bounding boxes, drivable areas, image-level tagging, lane markings, and much more. Also, it presents a wide variety in representing various geographic, environmental, and weather conditions.
  • CityScapes: It is a database containing a diverse set of stereo and video sequences recorded in the street scenes from 50 different cities. It also includes semantic, instance-wise, and dense pixel annotations for 30 divisions grouped in 8 categories. CityScapes provides pixel-level annotations for 5000 frames and 20,000 coarsely annotated frames. 

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