Dataset Format for AI

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About Hub

Hub is a dataset format with a simple API for creating, storing, and collaborating on AI datasets of any size. The hub data layout enables rapid tranformations and streaming of data while training models at scale. Hub is used by Google, Waymo, Red Cross, Oxford University, and Omdena.

Hub includes the following features:

• Storage agnostic API: Use the same API to upload, download, and stream datasets to/from AWS S3/S3-compatible storage, GCP, Activeloop cloud, local storage, as well as in-memory.
• Compressed storage: Store images, audios and videos in their native compression, decompressing them only when needed, for e.g., when training a model.
• Lazy NumPy-like slicing: Treat your S3 or GCP datasets as if they are a collection of NumPy arrays in your system's memory. Slice them, index them, or iterate through them. Only the bytes you ask for will be downloaded!
• Dataset version control: Commits, branches, checkout - Concepts you are already familiar with in your code repositories can now be applied to your datasets as well.
• Third-party integrations: Hub comes with built-in integrations for Pytorch and Tensorflow. Train your model with a few lines of code - we even take care of dataset shuffling. :)
• Distributed transforms: Rapidly apply transformations on your datasets using multi-threading, multi-processing, or our built-in Ray integration.
• Instant visualization support: Hub datasets are instantly visualized with bounding boxes, masks, annotations, etc. in Activeloop Platform (see below).

Getting Started with Hub

🚀 How to install Hub

Hub is written in 100% Python and can be quickly installed using pip.

pip3 install hub

🧠 Training a PyTorch model on a Hub dataset

Load CIFAR-10, one of the readily available datasets in Hub:

import hub
import torch
from torchvision import transforms, models

ds = hub.load('hub://activeloop/cifar10-train')

Inspect tensors in the dataset:

ds.tensors.keys()    # dict_keys(['images', 'labels'])
ds.labels[0].numpy() # array([6], dtype=uint32)

Train a PyTorch model on the Cifar-10 dataset without the need to download it

First, define a transform for the images and use Hub's built-in PyTorch one-line dataloader to connect the data to the compute:

tform = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5]),
])

hub_loader = ds.pytorch(num_workers=0, batch_size=4, transform={
                        'images': tform, 'labels': None}, shuffle=True)

Next, define the model, loss and optimizer:

net = models.resnet18(pretrained=False)
net.fc = torch.nn.Linear(net.fc.in_features, len(ds.labels.info.class_names))
    
criterion = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.SGD(net.parameters(), lr=0.001, momentum=0.9)

Finally, the training loop for 2 epochs:

for epoch in range(2):
    running_loss = 0.0
    for i, data in enumerate(hub_loader):
        images, labels = data['images'], data['labels']
        
        # zero the parameter gradients
        optimizer.zero_grad()

        # forward + backward + optimize
        outputs = net(images)
        loss = criterion(outputs, labels.reshape(-1))
        loss.backward()
        optimizer.step()
        
        # print statistics
        running_loss += loss.item()
        if i % 100 == 99:    # print every 100 mini-batches
            print('[%d, %5d] loss: %.3f' %
                (epoch + 1, i + 1, running_loss / 100))
            running_loss = 0.0

🏗️ How to create a Hub Dataset

A hub dataset can be created in various locations (Storage providers). This is how the paths for each of them would look like:

Let's create a dataset in the Activeloop cloud. Activeloop cloud provides free storage up to 300 GB per user (more info here). Create a new account with Hub from the terminal using activeloop register if you haven't already. You will be asked for a user name, email ID, and password. The user name you enter here will be used in the dataset path.

$ activeloop register
Enter your details. Your password must be at least 6 characters long.
Username:
Email:
Password:

Initialize an empty dataset in the Activeloop Cloud:

import hub

ds = hub.empty("hub://<USERNAME>/test-dataset")

Next, create a tensor to hold images in the dataset we just initialized:

images = ds.create_tensor("images", htype="image", sample_compression="jpg")

Assuming you have a list of image file paths, let's upload them to the dataset:

image_paths = ...
with ds:
    for image_path in image_paths:
        image = hub.read(image_path)
        ds.images.append(image)

Alternatively, you can also upload numpy arrays. Since the images tensor was created with sample_compression="jpg", the arrays will be compressed with jpeg compression.

import numpy as np

with ds:
    for _ in range(1000):  # 1000 random images
        random_image = np.random.randint(0, 256, (100, 100, 3))  # 100x100 image with 3 channels
        ds.images.append(random_image)

🚀 How to load a Hub Dataset

You can load the dataset you just created with a single line of code:

import hub

ds = hub.load("hub://<USERNAME>/test-dataset")

You can also access other publicly available hub datasets, not just the ones you created. Here is how you would load the Objectron Bikes Dataset:

import hub

ds = hub.load('hub://activeloop/objectron_bike_train')

To get the first image in the Objectron Bikes dataset in numpy format:

image_arr = ds.image[0].numpy()

📚 Documentation

Getting started guides, examples, tutorials, API reference, and other useful information can be found on our documentation page.

🎓 For Students and Educators

Hub users can access and visualize a variety of popular datasets through a free integration with Activeloop's Platform. Users can also create and store their own datasets and make them available to the public. Free storage of up to 300 GB is available for students and educators:

👩‍💻 Comparisons to Familiar Tools

Community

Join our Slack community to learn more about unstructured dataset management using Hub and to get help from the Activeloop team and other users.

We'd love your feedback by completing our 3-minute survey.

As always, thanks to our amazing contributors!

Made with contributors-img.

Please read CONTRIBUTING.md to get started with making contributions to Hub.

README Badge

Using Hub? Add a README badge to let everyone know:

[![hub](https://img.shields.io/badge/powered%20by-hub%20-ff5a1f.svg)](https://github.com/activeloopai/Hub)

Disclaimers

activeloop reporting --off

Acknowledgment

This technology was inspired by our research work at Princeton University. We would like to thank William Silversmith @SeungLab for his awesome cloud-volume tool.