Object detection is a technique in computer vision that actively looks for and identifies specific things inside an image or video. 

"The key to AI has always been representation." - Jeff Hawkins.

It's a crucial component of many ML uses, such as autonomous vehicles, surveillance systems, and augmented reality. Object detection is locating and classifying objects in an image or video.

This article compiles the eight best object detection datasets as of 2023.

nuScenes Dataset

nuScenes is a public dataset for autonomous car perception established by nuTonomy (now owned by Aptiv), an autonomous vehicle technology company. The collection includes various data from real-world autonomous vehicles, including high-resolution LIDAR and camera data and corresponding annotation. The nuScenes dataset contains 1000 scenes, each lasting 20 seconds and is collected at a rate of 20Hz.

LISA Traffic Sign Detection Dataset

The LISA (Laboratory for Intelligent and Safe Automobiles) Traffic Sign Dataset collects annotated frames and videos of US traffic signals. In addition, the dataset contains photos from several cameras, 47 types of US signs, and 7855 annotations on 6610 boundaries. LISA is released in two parts: with pictures and videos and photos.

ObjectNet3D Dataset

The ObjectNet3D benchmark dataset is a large-scale 3D object recognition and detection dataset. The collection includes photographs of 3D items from the top, bottom, front, and back. The ObjectNet3D collection is intended to offer a wide range of objects and situations, including everyday household items, furniture, electronics, and tools.

Images in the dataset were compiled in real-world settings, making it an ideal dataset for testing object detection algorithms in real-world applications.

Open Images V6

Open Images V6 is a freely available dataset to the public and can be used for object recognition, segmentation, and detection. The dataset, released in February 2020, contains labelled photos and pixel segmentation. The photographs in the collection came from various sources, including Flickr, Wikipedia, etc.

CIFAR 100

The image recognition dataset CIFAR-100 is widely utilized in machine learning research. There are 100 classes, each containing 600 photos, for a total of 60,000. Animals, autos, and everyday things are fine-grained classes, whereas birds and mammals are examples of coarse-grained types.

Because of its small image size and a considerable number of classes, CIFAR-100 is a challenging dataset for object recognition algorithms.

Pascal VOC

The Pascal Visual Object Classes (VOC) dataset is a computer vision benchmark for object recognition and classification. It was developed by the University of Oxford's Visual Object Classes (VOC) project and has since become a standard dataset for evaluating object detection methods. In addition, it contains photos of these things in various stances and settings, making it a diverse and challenging collection for object detection algorithms.

COCO Dataset

COCO (Common Objects in Context) is a large-scale image recognition dataset created by Microsoft. It is one of the best object detection datasets widely used in computer vision and object identification research. The images in the dataset have bounding boxes around things that have been tagged on them, resulting in a comprehensive training set for object detection algorithms. The collection also includes instance segmentation masks, which provide information about the shape of objects in a picture.

ImageNet

ImageNet is a classification of images based on the WordNet hierarchy. Each system link is portrayed by hundreds of thousands of photos in this dataset.

The dataset was created in response to two essential needs in computer vision research. First, the initial requirement was creating a North Star computer vision difficulty. Second, more supplementary data was required for more generalizable machine learning techniques.

Image source: Unsplash