Quickstart

A jump-start guide to using Deep Lake.

How to Get Started with Activeloop Deep Lake in Under 5 Minutes

Version control, query, and train models while streaming your deep-learning datasets from a cloud of your choice.

Installing Deep Lake

Deep Lake can be installed through pip. By default, Deep Lake does not install dependencies for audio, video, google-cloud, and other features. .

$ pip3 install deeplake

Fetching Your First Deep Lake Dataset

Let's load the , a rich dataset with many object detections per image. Datasets hosted on Activeloop Platform are typically identified by host organization name followed by the dataset name: activeloop/visdrone-det-train.

import deeplake

dataset_path = 'hub://activeloop/visdrone-det-train'
ds = deeplake.load(dataset_path) # Returns a Deep Lake Dataset but does not download data locally

Reading Samples From a Deep Lake Dataset

Data is not immediately read into memory because Deep Lake operates . You can fetch data by calling the .numpy() or .data() methods:

# Indexing
image = ds.images[0].numpy() # Fetch the first image and return a numpy array

labels = ds.labels[0].data() # Fetch the labels in the first image

boxes = ds.boxes[0].numpy() # Fetch the bounding boxes in the first image

# Slicing
img_list = ds.labels[0:100].numpy(aslist=True) # Fetch 100 labels and store 
                                               # them as a list of numpy arrays

Other metadata such as the mapping between numerical labels and their text counterparts can be accessed using:

labels_list = ds.labels.info['class_names']

Visualizing a Deep Lake Dataset

Deep Lake enables users to visualize and interpret large datasets. The tensor layout for a dataset can be inspected using:

ds.summary()

ds.visualize()

Create your own Datasets

You can perform all of the steps above and more with your own datasets! Please check out the links below to learn more:

PreviousDeep Lake Docs NextDataset Visualization

Last updated 2 years ago

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