A set of examples around hub for creating and processing datasets

Related tags

Deep Learning examples
Overview


Examples for Hub - Dataset Format for AI

A repository showcasing examples of using Hub

Colab Tutorials

Notebook Link
Getting Started with Hub Open In Colab
Creating Object Detection Datasets Open In Colab
Creating Complex Detection Datasets Open In Colab
Data Processing Using Parallel Computing Open In Colab
Training an Image Classification Model in PyTorch Open In Colab

Getting Started with Hub 🚀

Installation

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

pip3 install hub

Creating Datasets

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

Storage provider Example path
Hub cloud hub://user_name/dataset_name
AWS S3 s3://bucket_name/dataset_name
GCP gcp://bucket_name/dataset_name
Local storage path to local directory
In-memory mem://dataset_name

Let's create a dataset in the Hub cloud. 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 passowrd. The user name you enter here will be used in the dataset path.

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

Initialize an empty dataset in the hub 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, lets 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
        radnom_image = np.random.randint(0, 256, (100, 100, 3))  # 100x100 image with 3 channels
        ds.images.append(image)

Loading Datasets

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 usage information can be found on our documentation page.

Comments
  • Added various image transformations

    Added various image transformations

    Added various popular image transformations using the hub.compute decorator and created a sample pipeline to demonstrate the same. Used PIL to implement these features and random to introduce randomness to the transformations in the pipeline.

    opened by XDRAGON2002 3
  • Python script for example Image Transformation Using Hub

    Python script for example Image Transformation Using Hub

    Modify image data from Hub Datasets using basic image transforms. This modification can be applied to the existing/new datasets using hub.compute() functionality.

    PR regarding this issue.

    hacktoberfest 
    opened by TakshPanchal 2
  • Uploaded Activeloop Dataset Notebooks

    Uploaded Activeloop Dataset Notebooks

    I have added notebooks to below mentioned datasets:

    1. digits dataset
    2. phm-100
    3. fer2013 dataset
    4. plantvillage dataset
    5. seeds dataset
    6. hmdb51 dataset
    7. caltech-101 dataset
    8. cats_vs_dogs dataset
    9. coco-text dataset
    10. ecssd dataset
    11. esc-50 dataset
    12. cssd dataset
    13. GlaS dataset

    As majority of the code was is notebooks, I couldn't work with black testing module. Still few more datasets (2) are in fixing state. Let me know if you need any changes to the files.

    opened by SaiNikhileshReddy 0
  • nuScenes dataset

    nuScenes dataset

    Hi, Can you kindly provide examples playing with the complex nuScenes dataset, which contains data of cam, lidar, rader, map, etc. I can find a very old commit in the commit history of the main Hub source code "upload_nuscenes.py", but now it is gone. Thank you!

    opened by noringname 0
  • AIStore example added

    AIStore example added

    AIStore natively does not support boto3, because of http redirect issues https://github.com/NVIDIA/aistore/blob/master/docs/s3compat.md#boto3-compatibility)

    • [ ] Need to add a storage connector inside hub to support AIStore storage without boto3
    ds = hub.empty('s3://ubuntu/dataset', creds={'endpoint_url': 'http://localhost:51080/s3'})
    opened by davidbuniat 1
  • refactor examples repo

    refactor examples repo

    remove how-to and docs information from README (it's harder to maintain 2 copies of this, so the README for examples should be distinct from the main repo)

    opened by nollied 3
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