LogoLogo
API ReferenceGitHubSlackService StatusLogin
v3.9.16
v3.9.16
  • 🏠Deep Lake Docs
  • List of ML Datasets
  • 🏗️SETUP
    • Installation
    • User Authentication
      • Workload Identities (Azure Only)
    • Storage and Credentials
      • Storage Options
      • Setting up Deep Lake in Your Cloud
        • Microsoft Azure
          • Configure Azure SSO on Activeloop
          • Provisioning Federated Credentials
          • Enabling CORS
        • Google Cloud
          • Provisioning Federated Credentials
          • Enabling CORS
        • Amazon Web Services
          • Provisioning Role-Based Access
          • Enabling CORS
  • 📚Examples
    • Deep Learning
      • Deep Learning Quickstart
      • Deep Learning Guide
        • Step 1: Hello World
        • Step 2: Creating Deep Lake Datasets
        • Step 3: Understanding Compression
        • Step 4: Accessing and Updating 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
      • Deep Learning Tutorials
        • Creating Datasets
          • Creating Complex Datasets
          • Creating Object Detection Datasets
          • Creating Time-Series Datasets
          • Creating Datasets with Sequences
          • Creating Video Datasets
        • Training Models
          • Splitting Datasets for Training
          • 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
        • Updating Datasets
        • Data Processing Using Parallel Computing
      • Deep Learning Playbooks
        • Querying, Training and Editing Datasets with Data Lineage
        • Evaluating Model Performance
        • Training Reproducibility Using Deep Lake and Weights & Biases
        • Working with Videos
      • Deep Lake Dataloaders
      • API Summary
    • RAG
      • RAG Quickstart
      • RAG Tutorials
        • Vector Store Basics
        • Vector Search Options
          • LangChain API
          • Deep Lake Vector Store API
          • Managed Database REST API
        • Customizing Your Vector Store
        • Image Similarity Search
        • Improving Search Accuracy using Deep Memory
      • LangChain Integration
      • LlamaIndex Integration
      • Managed Tensor Database
        • REST API
        • Migrating Datasets to the Tensor Database
      • Deep Memory
        • How it Works
    • Tensor Query Language (TQL)
      • TQL Syntax
      • Index for ANN Search
        • Caching and Optimization
      • Sampling Datasets
  • 🔬Technical Details
    • Best Practices
      • Creating Datasets at Scale
      • Training Models at Scale
      • Storage Synchronization and "with" Context
      • Restoring Corrupted Datasets
      • Concurrent Writes
        • Concurrency Using Zookeeper Locks
    • Deep Lake Data Format
      • Tensor Relationships
      • Version Control and Querying
    • Dataset Visualization
      • Visualizer Integration
    • Shuffling in Dataloaders
    • How to Contribute
Powered by GitBook
On this page
  • How to Visualize Datasets in Deep Lake
  • Visualizing your own datasets

Was this helpful?

Edit on GitHub
  1. Examples
  2. Deep Learning
  3. Deep Learning Guide

Step 5: Visualizing Datasets

Visualizing and inspecting your datasets.

PreviousStep 4: Accessing and Updating DataNextStep 6: Using Activeloop Storage

Was this helpful?

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:

📚
Colab Notebook
visualized in the Activeloop App
Activeloop App
Dataset Visualization