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v3.1.5
v3.1.5
  • Deep Lake Docs
  • List of ML Datasets
  • Quickstart
  • Dataset Visualization
  • Storage & Credentials
    • Storage Options
    • Managed Credentials
      • Enabling CORS
      • Provisioning Role-Based Access
  • API Reference
  • Enterprise Features
    • Querying Datasets
      • Sampling Datasets
    • Performant Dataloader
  • EXAMPLE CODE
  • Getting Started
    • Step 1: Hello World
    • Step 2: Creating Deep Lake Datasets
    • Step 3: Understanding Compression
    • Step 4: Accessing Data
    • Step 5: Visualizing Datasets
    • Step 6: Using Activeloop Storage
    • Step 7: Connecting Deep Lake Datasets to ML Frameworks
    • Step 8: Parallel Computing
    • Step 9: Dataset Version Control
    • Step 10: Dataset Filtering
  • Tutorials (w Colab)
    • Creating Datasets
      • Creating Complex Datasets
      • Creating Object Detection Datasets
      • Creating Time-Series Datasets
      • Creating Datasets with Sequences
      • Creating Video Datasets
    • Training Models
      • Training an Image Classification Model in PyTorch
      • Training Models Using MMDetection
      • Training Models Using PyTorch Lightning
      • Training on AWS SageMaker
      • Training an Object Detection and Segmentation Model in PyTorch
    • Data Processing Using Parallel Computing
  • Playbooks
    • Querying, Training and Editing Datasets with Data Lineage
    • Evaluating Model Performance
    • Training Reproducibility Using Deep Lake and Weights & Biases
    • Working with Videos
  • API Summary
  • How Deep Lake Works
    • Data Layout
    • Version Control and Querying
    • Tensor Relationships
    • Visualizer Integration
    • Shuffling in ds.pytorch()
    • Storage Synchronization
    • How to Contribute
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  • How to Visualize Datasets in Deep Lake
  • Visualizing your own datasets

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  1. Getting Started

Step 5: Visualizing Datasets

Visualizing and inspecting your datasets.

PreviousStep 4: Accessing DataNextStep 6: Using Activeloop Storage

Last updated 2 years ago

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How to Visualize Datasets in Deep Lake

One of Deep Lake's core features is to enable users to visualize and interpret large amounts of data. Let's load the COCO dataset, which is one of the most popular datasets in computer vision.

import deeplake

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

The tensor layout for this dataset can be inspected using:

ds.summary()

The dataset can be or using an iframe in a jupyter notebook. If you don't already have flask and ipython installed, make sure to install Deep Lake using pip install deeplake[visualizer].

ds.visualize()

Visualizing datasets in will unlock more features and faster performance compared to visualization in Jupyter notebooks.

Visualizing your own datasets

Any Deep Lake dataset can be visualized using the methods above as long as it follows the conventions necessary for the visualization engine to interpret and parse the data. These conventions are explained in the link below:

visualized in the Activeloop UI
Activeloop Platform
Dataset Visualization