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
  • Upload multi-label samples
  • 1. Using the CSV Wizard
  • 2. Using Edge Impulse info.labels description file
  • 2. Using Edge Impulse structured_labels.labels description file
  • Visualizing multi-label samples
  • Edit multi-label samples
  • Classify multi-label data
  • Limitations
  • Resources

Was this helpful?

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

Multi-label (time-series)

PreviousCSV Wizard (time-series)NextTabular data (pre-processed & non-time-series)

Last updated 4 months ago

Was this helpful?

The multi-label feature brings considerable value by preserving the context of longer data samples, simplifying data preparation, and enabling more efficient and effective data analysis.

The first improvement is in the way you can analyze and process complex datasets, especially for applications where context and continuity are crucial. With this feature, you can maintain the integrity of longer-duration samples, such as hour-long exercise sessions or night-long sleep studies, without the need to segment these into smaller fragments every time there is a change in activity. This holistic view not only preserves the context but also provides a richer data set for analysis.

Then, the ability to select window sizes directly in Edge Impulse addresses a common pain point - data duplication. Without the multi-label feature, you need to pre-process data, either externally or using , creating multiple copies of the same data with different window sizes to determine the optimal configuration. This process is not only time-consuming but also prone to errors and inefficiencies. With multi-label samples, adjusting the window size becomes a simple parameter change in the "", streamlining the process significantly. This flexibility saves time, reduces the risk of errors, and allows for more dynamic experimentation with data, leading to potentially more accurate and insightful models.

Upload multi-label samples

1. Using the CSV Wizard

For example:

seconds_elapsed,    accX,   accY,    accZ,    label

0.00,               0.14642,-0.01645,-0.00858,idle
0.16,               0.15051,-0.01149,-0.00345,idle
0.32,               0.15546,-0.02141,-0.00342,idle
...
20.48,              0.14347,-0.03758,-0.00369,running
20.56,              0.13447,-0.01657,-0.01520,running
20.72,              0.11453,-0.00961,-0.01021,running

2. Using Edge Impulse info.labels description file

The other way is to create a info.labels file, present in your dataset. Edge Impulse will automatically detect it when you upload your dataset and will use this file to set the labels.

The info.labels looks like the following:

{
    "version": 1,
    "files": [{
        "path": "audio1.wav",
        "category": "split",
        "label": {
                "type": "multi-label",
                "labels": [
                    {
                        "label": "noise",
                        "startIndex": 0,
                        "endIndex": 5000
                    },
                    {
                        "label": "nominal_mode",
                        "startIndex": 5001
                        "endIndex": 60000
                    },
                    {
                        "label": "defect",
                        "startIndex": 60001
                        "endIndex": 60200
                    }
                ],
        "metadata": {
            "site_collected": "Factory_01"
            }
        }
    },
    {
        "path": "audio2.wav",
        "category": "split",
        "label": {
                "type": "multi-label",
                "labels": [
                    {
                        "label": "noise",
                        "startIndex": 0,
                        "endIndex": 2000
                    },
                    {
                        "label": "nominal_mode",
                        "startIndex": 2001
                        "endIndex": 40000
                    }
                ],
        "metadata": {
            "site_collected": "Factory_02"
            }
        }
    },
    ]
}

Tip

You can export a public project dataset that uses the multi-label feature to understand how the info.labels is structured.

Once you have your info.labels file available, to upload it, you can use:

The Studio Uploader will automatically detect the info.labels file:

> edge-impulse-uploader * --info-file info.labels

Edge Impulse uploader v1.23.0
Endpoints:
    API:         https://studio.edgeimpulse.com
    Ingestion:   https://ingestion.edgeimpulse.com

Upload configuration:
    Label:       Not set, will be inferred from file name
    Category:    training
    Project:     Example Multi-label upload (ID: XXXXX)

[ 1/11] Uploading training/machine_multilabel_8.json OK (1589 ms)
[ 2/11] Uploading testing/machine_multilabel_3.json OK (2024 ms)
[ 3/11] Uploading training/machine_multilabel_6.json OK (2176 ms)
[ 4/11] Uploading training/machine_multilabel_2.json OK (2224 ms)
[ 5/11] Uploading testing/machine_multilabel_1.json OK (2394 ms)
[ 6/11] Uploading training/machine_multilabel_8.json OK (2395 ms)
[ 7/11] Uploading training/machine_multilabel_9.json OK (2485 ms)
[ 8/11] Uploading training/machine_multilabel_7.json OK (2603 ms)
[ 9/11] Uploading testing/machine_multilabel_4.json OK (2617 ms)
[10/11] Uploading training/machine_multilabel_11.json OK (3426 ms)
[11/11] Uploading training/machine_multilabel_10.json OK (3488 ms)

Done. Files uploaded successful: 11. Files that failed to upload: 0.

2. Using Edge Impulse structured_labels.labels description file

The structured_labels.labels format looks like the following:

{
    "version": 1,
    "type": "structured-labels",
    "structuredLabels": {
        "updown.3.json": [{
            "startIndex": 0,
            "endIndex": 300,
            "label": "first_label"
        }, {
            "startIndex": 301,
            "endIndex": 621,
            "label": "second_label"
        }]
    }
}

Then you can run the following command:

curl -X POST \
    -H "x-api-key: $EI_PROJECT_API_KEY" \
    -H "Content-Type: multipart/form-data" \
    -F "data=@updown.3.json" \
    -F "data=@structured_labels.labels" \
    https://ingestion.edgeimpulse.com/api/training/files

Visualizing multi-label samples

Please note that you can also hide the sensors in the graph:

Edit multi-label samples

To edit the labels using the UI, click â‹® -> Edit labels. The following model will appear:

Please note that you will need to provide continuous and non-overlapping labels for the full length of your data sample.

The format is the like following:

[
    {
        "label": "label 1",
        "startMs": 0,
        "endMs": 2000
    },
    {
        "label": "label 2",
        "startMs": 2001,
        "endMs": 4000
    },
    {
        "label": "label 3",
        "startMs": 4001,
        "endMs": 4500
    }
]

Classify multi-label data

In the Live classification tab, you can classify your multi-label test samples:

Limitations

  • Labeling UI is available but is only text-based.

  • Overlapping labels are not supported

  • The entire data sample needs to have a label, you cannot leave parts unlabeled.

Please, leave us a note on the forum or feedback using the "?" widget (bottom-right corner) if you see a need or an issue. This can help us prioritize the development or improvement of the features.

Resources

Public projects

If your dataset is in the CSV format and contains a label column, the is probably the easiest method to import your multi-label data.

Once your CSV Wizard is configured, you can use the , the or the :

Check the section for multi-label public projects.

The :

The :

If you want to use the , you need to use the structured_labels.labels format:

You can have a look at this tutorial for a better understanding: .

CSV Wizard
Studio Uploader
CLI Uploader
Ingestion API
Studio Uploader
CLI Uploader
Ingestion API
Ingest multi-label data with Edge Impulse API
Coffee Machine Stages - Multi-label data
Resources
transformation jobs
Impulse design
Multi-label
Multi-label workflow
Studio Uploader multi-label dataset
Multi-label sample preview
Multi-label sample preview - Hide sensors
Edit labels
Test multi-label sampled