Optimize your datasets for ML Goodbye, boilerplate code
The fastest dataset optimization and management tool for computer vision.
The open source community
creating the future of data
Trended #1 in Python
Your model's gonna love your data.
You will, too
Managing computer vision datasets is hard. Therefore, you may try to increase performance by tinkering with your models rather than your data. But optimal models need optimized datasets! With Hub, you can easily improve your datasets because Hub neatly rearranges your computer vision datasets into our Data 2.0 format (NumPy-like, cloud-native arrays). As a result, Hub datasets are very easy to manage and optimize for computer vision applications.Check out our Repo
Better data for your team, at scale
Load Data from AnywhereHub works locally, on Google Cloud, MinIO, AWS, Azure as well as Activeloop storage (no servers needed). Load data (even from Kaggle!) and directly stream datasets from cold storage to ML workflows. It's that fast.
Dataset Version ControlModify dataset elements across different versions and seamlessly switch between them. Hub's intuitive Python API works with datasets of any size and overcomes the limitations of file-based version control.
Transformations at ScaleQuickly modify, update, or resample your datasets in order to find the optimal dataset for your models. Scale to hundreds of machines with one line of code.
Visualize any computer
Instantly visualize any slice of the dataset you upload to Hub.
- Rapidly visualize different versions of your data
- Understand your data and improve its quality
- Run transforms on the data with in-built compute infrastructure
- Train your models on our compute infrastructure
Open data, one click away
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- Explore verified datasets
by team Activeloop...
- ... public organizations
(like Google!) ...
- ... and our open-source
Optimizing data is easy(If you use Hub)
1import hub 2from PIL import Image 3 4ds = hub.load('hub://activeloop/mnist-train') # Hub Dataset 5 6# Display an image 7Image.fromarray(ds.images.numpy())