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v3.8.16
v3.8.16
  • Deep Lake Docs
  • Vector Store Quickstart
  • Deep Learning Quickstart
  • Storage & Credentials
    • Storage Options
    • User Authentication
    • Storing Deep Lake Data in Your Own Cloud
      • Microsoft Azure
        • Provisioning Federated Credentials
        • Enabling CORS
      • Amazon Web Services
        • Provisioning Role-Based Access
        • Enabling CORS
  • List of ML Datasets
  • 🏢High-Performance Features
    • Introduction
    • Performant Dataloader
    • Tensor Query Language (TQL)
      • TQL Syntax
      • Sampling Datasets
    • Deep Memory
      • How it Works
    • Index for ANN Search
      • Caching and Optimization
    • Managed Tensor Database
      • REST API
      • Migrating Datasets to the Tensor Database
  • 📚EXAMPLE CODE
    • Getting Started
      • Vector Store
        • Step 1: Hello World
        • Step 2: Creating Deep Lake Vector Stores
        • Step 3: Performing Search in Vector Stores
        • Step 4: Customizing Vector Stores
      • Deep Learning
        • 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
    • Tutorials (w Colab)
      • Vector Store Tutorials
        • Vector Search Options
          • Deep Lake Vector Store API
          • REST API
          • LangChain API
        • Image Similarity Search
        • Deep Lake Vector Store in LangChain
        • Deep Lake Vector Store in LlamaIndex
        • Improving Search Accuracy using Deep Memory
      • 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
      • Concurrent Writes
        • Concurrency Using Zookeeper Locks
    • Playbooks
      • Querying, Training and Editing Datasets with Data Lineage
      • Evaluating Model Performance
      • Training Reproducibility Using Deep Lake and Weights & Biases
      • Working with Videos
    • Low-Level API Summary
  • 🔬Technical Details
    • Best Practices
      • Creating Datasets at Scale
      • Training Models at Scale
      • Storage Synchronization and "with" Context
      • Restoring Corrupted Datasets
      • Concurrent Writes
    • Data Layout
    • Version Control and Querying
    • Dataset Visualization
    • Tensor Relationships
    • Visualizer Integration
    • Shuffling in dataloaders
    • How to Contribute
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On this page
  • How to visualize machine learning datasets
  • Visualization can be performed in 3 ways:
  • Requirements for correctly visualizing your own datasets
  • Visualizer Controls and Modes
  • Downsampling Data for Faster Visualization

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  1. Technical Details

Dataset Visualization

How to visualize Deep Lake datasets

PreviousVersion Control and QueryingNextTensor Relationships

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How to visualize machine learning datasets

has a web interface for visualizing, versioning, and querying . It utilizes the Deep Lake format under-the-hood, and it can be connected to datasets stored in all Deep Lake .

Visualization can be performed in 3 ways:

Requirements for correctly visualizing your own datasets

Deep Lake makes assumptions about underlying data types and relationships between tensors in order to display the data correctly. Understanding the following concepts is necessary in order to use the visualizer:

Visualizer Controls and Modes

Downsampling Data for Faster Visualization

For faster visualization of images and masks, tensors can be downsampled during dataset creation. The downsampled data are stored in the dataset and are automatically rendered by the visualizer depending on the zoom level.

# 3X downsampling per layer, 2X layers
ds.create_tensor('images', htype = 'image', downsampling = (3,2))

Note: since downsampling requires decompression and recompression of data, it will slow down dataset ingestion.

In the (most feature-rich and performant option)

In the using ds.visualize()

In your own application using .

To add downsampling to your tensors, specify the downsampling factor and the number of downsampling layers during :

🔬
Deep Lake UI
python API
our integration options
Data Types (htypes)
Relationships between tensors
tensor creation
Deep Lake
machine learning datasets
storage locations