LogoLogo
HomeAPI & SDKsProjectsForumStudio
  • Getting started
    • For beginners
    • For ML practitioners
    • For embedded engineers
  • Frequently asked questions (FAQ)
  • Tutorials
    • End-to-end tutorials
      • Computer vision
        • Image classification
        • Object detection
          • Object detection with bounding boxes
          • Detect objects with centroid (FOMO)
        • Visual anomaly detection
        • Visual regression
      • Audio
        • Sound recognition
        • Keyword spotting
      • Time-series
        • Motion recognition + anomaly detection
        • Regression + anomaly detection
        • HR/HRV
        • Environmental (Sensor fusion)
    • Data
      • Data ingestion
        • Collecting image data from the Studio
        • Collecting image data with your mobile phone
        • Collecting image data with the OpenMV Cam H7 Plus
        • Using the Edge Impulse Python SDK to upload and download data
        • Trigger connected board data sampling
        • Ingest multi-labeled data using the API
      • Synthetic data
        • Generate audio datasets using Eleven Labs
        • Generate image datasets using Dall-E
        • Generate keyword spotting datasets using Google TTS
        • Generate physics simulation datasets using PyBullet
        • Generate timeseries data with MATLAB
      • Labeling
        • Label audio data using your existing models
        • Label image data using GPT-4o
      • Edge Impulse Datasets
    • Feature extraction
      • Building custom processing blocks
      • Sensor fusion using embeddings
    • Machine learning
      • Classification with multiple 2D input features
      • Visualize neural networks decisions with Grad-CAM
      • Sensor fusion using embeddings
      • FOMO self-attention
    • Inferencing & post-processing
      • Count objects using FOMO
      • Continuous audio sampling
      • Multi-impulse (C++)
      • Multi-impulse (Python)
    • Lifecycle management
      • CI/CD with GitHub Actions
      • Data aquisition from S3 object store - Golioth on AI
      • OTA model updates
        • with Arduino IDE (for ESP32)
        • with Arduino IoT Cloud
        • with Blues Wireless
        • with Docker on Allxon
        • with Docker on Balena
        • with Docker on NVIDIA Jetson
        • with Espressif IDF
        • with Nordic Thingy53 and the Edge Impulse app
        • with Particle Workbench
        • with Zephyr on Golioth
    • API examples
      • Customize the EON Tuner
      • Ingest multi-labeled data using the API
      • Python API bindings example
      • Running jobs using the API
      • Trigger connected board data sampling
    • Python SDK examples
      • Using the Edge Impulse Python SDK to run EON Tuner
      • Using the Edge Impulse Python SDK to upload and download data
      • Using the Edge Impulse Python SDK with Hugging Face
      • Using the Edge Impulse Python SDK with SageMaker Studio
      • Using the Edge Impulse Python SDK with TensorFlow and Keras
      • Using the Edge Impulse Python SDK with Weights & Biases
    • Expert network projects
  • Edge Impulse Studio
    • Organization hub
      • Users
      • Data campaigns
      • Data
        • Cloud data storage
      • Data pipelines
      • Data transformation
        • Transformation blocks
      • Upload portals
      • Custom blocks
        • Custom AI labeling blocks
        • Custom deployment blocks
        • Custom learning blocks
        • Custom processing blocks
        • Custom synthetic data blocks
        • Custom transformation blocks
      • Health reference design
        • Synchronizing clinical data with a bucket
        • Validating clinical data
        • Querying clinical data
        • Transforming clinical data
    • Project dashboard
      • Select AI hardware
    • Devices
    • Data acquisition
      • Uploader
      • Data explorer
      • Data sources
      • Synthetic data
      • Labeling queue
      • AI labeling
      • CSV Wizard (time-series)
      • Multi-label (time-series)
      • Tabular data (pre-processed & non-time-series)
      • Metadata
      • Auto-labeler | deprecated
    • Impulses
    • EON Tuner
      • Search space
    • Processing blocks
      • Audio MFCC
      • Audio MFE
      • Audio Syntiant
      • Flatten
      • HR/HRV features
      • Image
      • IMU Syntiant
      • Raw data
      • Spectral features
      • Spectrogram
      • Custom processing blocks
      • Feature explorer
    • Learning blocks
      • Anomaly detection (GMM)
      • Anomaly detection (K-means)
      • Classification
      • Classical ML
      • Object detection
        • MobileNetV2 SSD FPN
        • FOMO: Object detection for constrained devices
      • Object tracking
      • Regression
      • Transfer learning (images)
      • Transfer learning (keyword spotting)
      • Visual anomaly detection (FOMO-AD)
      • Custom learning blocks
      • Expert mode
      • NVIDIA TAO | deprecated
    • Retrain model
    • Live classification
    • Model testing
    • Performance calibration
    • Deployment
      • EON Compiler
      • Custom deployment blocks
    • Versioning
    • Bring your own model (BYOM)
    • File specifications
      • deployment-metadata.json
      • ei-metadata.json
      • ids.json
      • parameters.json
      • sample_id_details.json
      • train_input.json
  • Tools
    • API and SDK references
    • Edge Impulse CLI
      • Installation
      • Serial daemon
      • Uploader
      • Data forwarder
      • Impulse runner
      • Blocks
      • Himax flash tool
    • Edge Impulse for Linux
      • Linux Node.js SDK
      • Linux Go SDK
      • Linux C++ SDK
      • Linux Python SDK
      • Flex delegates
      • Rust Library
    • Rust Library
    • Edge Impulse Python SDK
  • Run inference
    • C++ library
      • As a generic C++ library
      • On Android
      • On your desktop computer
      • On your Alif Ensemble Series Device
      • On your Espressif ESP-EYE (ESP32) development board
      • On your Himax WE-I Plus
      • On your Raspberry Pi Pico (RP2040) development board
      • On your SiLabs Thunderboard Sense 2
      • On your Spresense by Sony development board
      • On your Syntiant TinyML Board
      • On your TI LaunchPad using GCC and the SimpleLink SDK
      • On your Zephyr-based Nordic Semiconductor development board
    • Arm Keil MDK CMSIS-PACK
    • Arduino library
      • Arduino IDE 1.18
    • Cube.MX CMSIS-PACK
    • Docker container
    • DRP-AI library
      • DRP-AI on your Renesas development board
      • DRP-AI TVM i8 on Renesas RZ/V2H
    • IAR library
    • Linux EIM executable
    • OpenMV
    • Particle library
    • Qualcomm IM SDK GStreamer
    • WebAssembly
      • Through WebAssembly (Node.js)
      • Through WebAssembly (browser)
    • Edge Impulse firmwares
    • Hardware specific tutorials
      • Image classification - Sony Spresense
      • Audio event detection with Particle boards
      • Motion recognition - Particle - Photon 2 & Boron
      • Motion recognition - RASynBoard
      • Motion recognition - Syntiant
      • Object detection - SiLabs xG24 Dev Kit
      • Sound recognition - TI LaunchXL
      • Keyword spotting - TI LaunchXL
      • Keyword spotting - Syntiant - RC Commands
      • Running NVIDIA TAO models on the Renesas RA8D1
      • Two cameras, two models - running multiple object detection models on the RZ/V2L
  • Edge AI Hardware
    • Overview
    • Production-ready
      • Advantech ICAM-540
      • Seeed SenseCAP A1101
      • Industry reference design - BrickML
    • MCU
      • Ambiq Apollo4 family of SoCs
      • Ambiq Apollo510
      • Arducam Pico4ML TinyML Dev Kit
      • Arduino Nano 33 BLE Sense
      • Arduino Nicla Sense ME
      • Arduino Nicla Vision
      • Arduino Portenta H7
      • Blues Wireless Swan
      • Espressif ESP-EYE
      • Himax WE-I Plus
      • Infineon CY8CKIT-062-BLE Pioneer Kit
      • Infineon CY8CKIT-062S2 Pioneer Kit
      • Nordic Semi nRF52840 DK
      • Nordic Semi nRF5340 DK
      • Nordic Semi nRF9160 DK
      • Nordic Semi nRF9161 DK
      • Nordic Semi nRF9151 DK
      • Nordic Semi nRF7002 DK
      • Nordic Semi Thingy:53
      • Nordic Semi Thingy:91
      • Open MV Cam H7 Plus
      • Particle Photon 2
      • Particle Boron
      • RAKwireless WisBlock
      • Raspberry Pi RP2040
      • Renesas CK-RA6M5 Cloud Kit
      • Renesas EK-RA8D1
      • Seeed Wio Terminal
      • Seeed XIAO nRF52840 Sense
      • Seeed XIAO ESP32 S3 Sense
      • SiLabs Thunderboard Sense 2
      • Sony's Spresense
      • ST B-L475E-IOT01A
      • TI CC1352P Launchpad
    • MCU + AI accelerators
      • Alif Ensemble
      • Arduino Nicla Voice
      • Avnet RASynBoard
      • Seeed Grove - Vision AI Module
      • Seeed Grove Vision AI Module V2 (WiseEye2)
      • Himax WiseEye2 Module and ISM Devboard
      • SiLabs xG24 Dev Kit
      • STMicroelectronics STM32N6570-DK
      • Synaptics Katana EVK
      • Syntiant Tiny ML Board
    • CPU
      • macOS
      • Linux x86_64
      • Raspberry Pi 4
      • Raspberry Pi 5
      • Texas Instruments SK-AM62
      • Microchip SAMA7G54
      • Renesas RZ/G2L
    • CPU + AI accelerators
      • AVNET RZBoard V2L
      • BrainChip AKD1000
      • i.MX 8M Plus EVK
      • Digi ConnectCore 93 Development Kit
      • MemryX MX3
      • MistyWest MistySOM RZ/V2L
      • Qualcomm Dragonwing RB3 Gen 2 Dev Kit
      • Renesas RZ/V2L
      • Renesas RZ/V2H
      • IMDT RZ/V2H
      • Texas Instruments SK-TDA4VM
      • Texas Instruments SK-AM62A-LP
      • Texas Instruments SK-AM68A
      • Thundercomm Rubik Pi 3
    • GPU
      • Advantech ICAM-540
      • NVIDIA Jetson
      • Seeed reComputer Jetson
    • Mobile phone
    • Porting guide
  • Integrations
    • Arduino Machine Learning Tools
    • AWS IoT Greengrass
    • Embedded IDEs - Open-CMSIS
    • NVIDIA Omniverse
    • Scailable
    • Weights & Biases
  • Tips & Tricks
    • Combining impulses
    • Increasing model performance
    • Optimizing compute time
    • Inference performance metrics
  • Concepts
    • Glossary
    • Course: Edge AI Fundamentals
      • Introduction to edge AI
      • What is edge computing?
      • What is machine learning (ML)?
      • What is edge AI?
      • How to choose an edge AI device
      • Edge AI lifecycle
      • What is edge MLOps?
      • What is Edge Impulse?
      • Case study: Izoelektro smart grid monitoring
      • Test and certification
    • Data engineering
      • Audio feature extraction
      • Motion feature extraction
    • Machine learning
      • Data augmentation
      • Evaluation metrics
      • Neural networks
        • Layers
        • Activation functions
        • Loss functions
        • Optimizers
          • Learned optimizer (VeLO)
        • Epochs
    • What is embedded ML, anyway?
    • What is edge machine learning (edge ML)?
Powered by GitBook
On this page
  • AI labeling actions
  • AI labeling blocks
  • Custom AI labeling blocks
  • Configuration
  • Select an AI labeling block
  • Add multiple AI labeling blocks
  • Filter which data to label
  • Preview
  • Set metadata (optional)
  • Run the labeling process
  • Examples
  • Bounding box labeling with OWL-ViT
  • Bounding box re-labeling with GPT-4o
  • Bounding box validation with GPT-4o
  • Image labeling with GPT-4o
  • Image labeling with pretrained models
  • Audio labeling with AudioSet
  • Troubleshooting

Was this helpful?

Export as PDF
  1. Edge Impulse Studio
  2. Data acquisition

AI labeling

PreviousLabeling queueNextCSV Wizard (time-series)

Last updated 3 months ago

Was this helpful?

The AI labeling feature is an extensible way of integrating existing AI models into your workflow and using them to automatically label your datasets. This can be achieved through leveraging ready-made blocks provided by Edge Impulse or developing custom ones to meet your specific needs. Whether you’re labeling images, bounding boxes, or audio samples, these AI labeling blocks are sure to save you time and improve your consistency.

AI labeling actions

To exit an AI labeling action configuration and return to the overview page, you can click on the < button found to the left of the block configuration title (AI Labeling - Step 1) or click the AI labeling tab.

You can create multiple AI labeling actions that contain one or more AI labeling blocks, each with different prompts, parameters and filters. From the AI labeling actions overview page you can add new actions, delete existing ones, access their configurations, or run them directly.

AI labeling blocks

There are several AI labeling blocks that have been developed by Edge Impulse and are available for your use. These are listed below with links to their associated code in public GitHub repositories:

Custom AI labeling blocks

Configuration

To begin, proceed to the Data acquisition view and ensure you have data samples in the Dataset tab. Then, continue to the AI labeling tab.

Click on an existing AI labeling action to enter the configuration view for that action. If you do not yet have an AI labeling action, you can create one using the + Add new label action button.

Select an AI labeling block

The first step is to select an AI labeling block that you would like to use. By default, blocks that are not compatible with your data modality or labeling objective are greyed out. Once you have selected an AI labeling block, the parameters specific to that block are presented.

Some blocks require an API key to interact with other providers, such as OpenAI or Hugging Face. You can set your API key directly in the AI labeling block configuration panel the first time you use the block. The key you enter will be stored in Secrets. Once created, the key value will no longer be visible anywhere in the platform.

To manage your secrets if you are an Enterprise customer, go to your organization and select the Secrets menu item. If you are not an Enterprise customer, secrets can be accessed through the settings in your developer profile. Click on your avatar and go to your Account settings -> Secrets:

Add multiple AI labeling blocks

You can chain several AI labeling blocks together to create an AI labeling action with multiple steps. For example, you can first use a zero-shot object detector to automatically detect high-level objects within an image then follow this with a step to re-label the bounding boxes with more precise labels or remove them entirely.

To add multiple AI labeling blocks, click on the button at the bottom of the block configuration panel to add an extra step.

Filter which data to label

Preview

Tip: If you want to change the number of data samples or the number of columns shown in the preview, click on the view settings icon. Changing the number of columns can be useful for object detection use cases where your objects are small and you want to see larger images.

Before running the AI labeling action on your entire dataset, we recommend to preview the label results on a small subset of your dataset. This will help you to validate your prompt and parameters so that you can iterate faster.

When clicking on the Label preview data button, the changes are staged but not directly applied.

Set metadata (optional)

You can add metadata such as ai-labeled: true, labeling-source: GPT-4o or labeled-on: Nov 2024 that will be set after running the AI labeling action. This is particularly useful if you plan to add more data samples over time and need to filter out your already-labeled samples.

Run the labeling process

Once you are satisfied with your configuration, click on the Label all data button. This will run the AI labeling action and apply the labeling updates to your dataset.

Examples

Bounding box labeling with OWL-ViT

A zero-shot object detector that uses OWL-ViT to label objects with bounding boxes. For complex objects, pair with "Bounding box re-labeling with GPT-4o" to refine labels.

Bounding box re-labeling with GPT-4o

OpenAI API key needed

Take existing bounding boxes (e.g. from a zero-shot object detector) and use GPT-4o to re-label or remove them as needed. This can be configured as a two step process in a single AI labeling action.

Bounding box validation with GPT-4o

OpenAI API key needed

Validate existing bounding boxes against provided prompts using GPT-4o and disable non-compliant images. For example, check if the label for a bounding box corresponds to the object within it, if objects are blurry, and more.

Image labeling with GPT-4o

OpenAI API key needed

Use GPT-4o to apply a single label to images. Customize prompts to return a single label, for example “Is there a person in this picture? Answer with 'yes' or 'no'.”

Image labeling with pretrained models

Audio labeling with AudioSet

Hugging Face API key needed

Troubleshooting

If you have a suggestion for an AI labeling block that you would like to see Edge Impulse develop, please let us know in our .

If none of the blocks from Edge Impulse fit your needs, you can modify them or develop from scratch to create a custom AI labeling block. This allows you to integrate your own models or prompts for unique project requirements. See the page for more information.

Select which data items in your dataset you want to label. You can use the attached to your data samples to define your own labeling strategy.

Use a model from an existing Edge Impulse project to label images (classification or object detection). You can also upload your pretrained models to Edge Impulse using the .

Label audio samples with multiple labels per sample using an Audio Spectrogram Transformer (AST) model trained on AudioSet. Use only AudioSet labels (see for reference).

No common issues have been identified thus far. If you encounter an issue, please reach out on the or, if you are an Enterprise customer, to your Solutions engineer.

Bounding Box Labeling with OWL-ViT
Bounding Box Re-Labeling with GPT-4o
Bounding Box Validation with GPT-4o
Image Labeling with GPT-4o
Image Labeling with Pretrained Models
Audio Labeling with AudioSet
forum
Custom AI labeling blocks
metadata
BYOM (Bring Your Own Model) feature
AudioSet Dataset
forum
AI labeling actions overview page
AI labeling block configuration
AI labeling blocks
Manage secrets in a developer profile
Selecting data to run the AI labeling action on
AI labeling block OWL-ViT
AI labeling bounding box relabeling
AI labeling bounding box validation
AI labeling images with GPT-4o
AI labeling using your own models
AI labeling with AudioSet
change view icon