In recent years, more and more companies and research institutions have made their autonomous driving datasets open to the public.

However, the best datasets are not always easy to find, and scouring the internet for them takes time.To help, we at SiaSearch have put together a list of the top 15 open datasets for autonomous driving.

A2D2 DatasetThe Audi Autonomous Driving Dataset (A2D2) features over 41,000 labeled with 38 features.

semantic segmentation, 3D bounding box).2.

ApolloScape DatasetApolloScape is an evolving research project that aims to foster innovation across all aspects of autonomous driving, from perception to navigation and control.

Via their website, users can explore a variety of simulation tools and over 100K street view frames, 80k lidar point cloud and 1000km trajectories for urban traffic.An example of lanemark segmentation in the ApolloScape dataset3.

Using data curation tools, engineers can get a better understanding of the data they’ve collected, identify the most important subsets and edge cases, and curate custom training datasets to feed back into their models.The role data curation tools play in machine learningThe best data curation tools enable you to:Visualize large scale data: Make it easy to obtain insights on key metrics, as well as the general distribution and diversity of your datasets regardless of sensor type and format.

Curate diverse scenarios: Identify the most interesting segments within your dataset, and manipulate them within the tool to create completely customized training sets.Seamlessly integrate: The tool should fit well within your existing workflows and toolset.What are the best data curation tools for computer vision?With an overwhelming amount of AI products and platforms popping up year after year, how do you know which will provide the most value?

Based on our experience, we are sharing our honest reviews of the top tools, hoping that this will be of use for engineers searching for a data curation solution.Read on below to find out which data curation tool is the best fit for your computer vision project.Aquarium LearningAquarium is a data management platform that aims to make it easy to identify labeling errors and model failures.

With Aquarium, users can version and combine model predictions with their ground truth.Aquarium is especially focused on curating and maintaining training datasets, catering less to raw data management use cases.

They also support multiple annotation types, such as classification, detection, and segmentation.Interactive model evaluation - Users can manipulate evaluation thresholds and obtain interactive visualizations to obtain required samples quickly.Collaborative features - Users can collaborate with each other on the Aquarium platform to build data subsets, associate them with issues, and identify new data for annotation.FiftyOneDeveloped by Voxel51, FiftyOne is an open-source tool to visualize and interpret computer vision datasets.

Today, the platform lacks collaborative features; for example, a single instance cannot host multiple user accounts.Key Features:Model & dataset zoo - FiftyOne taps into TF and Pytorch dataset zoos to provide access to a variety of open datasets and open-source models.Advanced data analysis - Via the Brain, a separate closed-source Python package, users can quantitatively assess the uniqueness, mistakenness, and hardness of data.External integrations - FiftyOne directly integrates with popular annotation tools such as LabelBox.

Open data is fueling commercial and technological advancement in autonomous driving—one of most well known resources being the nuScenes dataset.Developed by the team at Motional (formerly nuTonomy), nuScenes is one of the most popular open-source datasets for autonomous driving.

The nuScenes dataset enables researchers to study a wide range of urban driving situations using data captured by the full sensor suite of a self-driving car.

Recorded in Boston and Singapore, nuScenes features a diverse range of traffic situations, driving maneuvers, and unexpected behaviors.The dataset includes:Full sensor suite: 32-beam LiDAR, 6 cameras and radars with complete 360° coverage1000 urban street scenes, 20 seconds each1,440,000 camera images23 classes and 8 attributesAccessing nuScenes data in SiaSearchTo access the data yourself, you’ll need to sign up for a free account on SiaSearch.

This view lets you quickly understand the overall dataset composition, as well as identify any gaps in data distribution.Querying the nuScenes DatasetHaving a holistic view of the dataset, while useful, is not enough.

The ability to drill into specific subsets can uncover insights and imbalances in the data—a critical step in model building and validation.SiaSearch makes every piece of nuScenes data searchable against all available and auto-extracted dimensions using its intelligent search interface.

The platform features two ways to search for the exact sequences you want, using either a visual or code interface.