Computer vision without doubt is going to change almost every aspect of how a machine interacts with our environment and us in the near future. It is still a young field (originated from 1960s) but the technology starts to work at the first time in history with recent advances in machine learning. Applications like self-driving cars, robotics, VR/AR have motivated people to enter the field and apply the technology to much broader areas. It has become one of the most active subfields of Artificial Intelligence.
Crazy ideas around computer vision happen everyday for new research, side projects or business use cases. To work with computer vision, you need three things: data, algorithm and computation. Giant tech companies like Google and Amazon has made heavy computation possible for everyone with their cloud platform offering. The research community (e.g. arXiv) and open source community (e.g. Github) bring us better algorithms on paper and easy-to-use code in practice. It has never been a better time to study this complex subject which used to require Ph.D level experience. But there is one problem left — Data. More specifically, visual data which comes with high quality annotations. Building a good dataset requires a lot of thinking in terms of size, content balance, visual variations to help the trained model generalize well to unseen cases. Labeling a large scale dataset can be very expensive and slow, making it still a barrier for individuals or small companies with limited budget to experiment with new ideas.
The answer to the problem is public datasets. Instead of building your own dataset, there already exists a rich collection of computer vision datasets contributed by academic researchers, hobbyists and companies. These datasets include diverse topics from recognizing objects to reconstructing a 3D room, from finding a person in a video to identifying a shirt in a photo. They generally come with good quality labels annotated manually. In recent years, new datasets even include pretrained deep learning models that you can directly try out without training one by yourself. However, to discover these datasets is not that easy. They are usually hosted separately on the creator’s website and it’s hard to compare different ones to choose the best one for a specific task. For newcomers, it’s also hard to come up with the right keywords to put in Google.
That’s why we built VisualData, a search engine for computer vision datasets. Each dataset is either curated or submitted by the community, tagged with relevant topics for filtering and you can type keywords in the search bar which will be matched to the title and description of the dataset to easily find the best ones to use. They are sorted according to published date so the most recent addition gets surfaced. You can also sort by popularity to see what are the trending datasets based on the visit frequency. The detail view shows the full description of a dataset and links to open source code/models. Other useful information will be added incrementally in the future, e.g. articles/tutorials about the dataset, difficulty to work with etc. Some example screenshots of the site are illustrated below.
From left to right: filtering with (multiple) topics, search with keywords.
From left to right: sort by date or popularity, dataset detail.
This is a site I wish that existed during my Ph.D. Now, I hope it could help people interested in computer vision easily explore existing data to do their experiments faster. It is still in an early form, and there are some cool ideas to be added for future versions. We hope to hear how it can be more useful to you.
Follow us on Twitter to get updates on new datasets and code release. If you created or know an interesting dataset, don’t hesitate to submit to VisualData by clicking the ‘Add my dataset’ button. This post was also featured on KDnugget website.