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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)
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          • 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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  • Understanding the Relationships Between Deep Lake Tensors
  • Indexing
  • Relationships Between Tensors

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  1. Technical Details

Tensor Relationships

Understanding the correct data layout for successful visualization.

Understanding the Relationships Between Deep Lake Tensors

Indexing

Hub datasets and their tensors are indexed like ds[index] or ds.tensor_name[index], and data at the same index are assumed to be related. For example, a bounding_box at index 100 is assumed to apply to the image at index 100.

Relationships Between Tensors

For datasets with multiple tensors, it is important to follow the conventions below in order for the visualizer to correctly infer how tensors are related.

By default, in the absence of groups, the visualizer assumes that all tensors are related to each other.

This works well for simple use cases. For example, it is correct to assume that the images, labels, and boxes tensors are related in the dataset below:

ds
-> images (htype = image)
-> labels (htype = class_label)
-> boxes (htype = bbox)

However, if datasets are highly complex, assuming that all tensor are related may lead to visualization errors, because every tensor may not be related to every other tensor:

ds
-> images (htype = image)
-> vehicle_labels (htype = class_label)
-> vehicle_boxes (htype = bbox)
-> people_labels (htype = class_label)
-> people_masks (htype = binary_mask)

In the example above, only some of the annotation tensors are related to each other:

  • vehicle_labels -> vehicle_boxes: Boxes and labels describing cars, trucks, etc.

  • people_labels -> people_masks: Binary masks and labels describing adults, toddlers, etc.

The best method for disambiguating the relationships between tensors is to place them in groups, because the visualizer assumes that annotation tensors in different groups are not related.

In the example above, the following groups could be used to disambiguate the annotations:

ds
-> images (htype = image)
-> vehicles (group)
   -> vehicle_labels (htype = class_label)
   -> vehicle_boxes (htype = bbox)
-> people (group)
   -> people_labels (htype = class_label)
   -> people_masks (htype = binary_mask) 
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