Supervisely
AboutAPI ReferenceSDK Reference
  • 🤖What's Supervisely
  • 🚀Ecosystem of Supervisely Apps
  • 💡FAQ
  • 📌Getting started
    • How to import
    • How to annotate
    • How to invite team members
    • How to connect agents
    • How to train models
  • 🔁Import and Export
    • Import
      • Overview
      • Import using Web UI
      • Supported annotation formats
        • Images
          • 🤖Supervisely JSON
          • 🤖Supervisely Blob
          • COCO
          • Yolo
          • Pascal VOC
          • Cityscapes
          • Images with PNG masks
          • Links from CSV, TXT and TSV
          • PDF files to images
          • Multiview images
          • Multispectral images
          • Medical 2D images
          • LabelMe
          • LabelStudio
          • Fisheye
          • High Color Depth
        • Videos
          • Supervisely
        • Pointclouds
          • Supervisely
          • .PCD, .PLY, .LAS, .LAZ pointclouds
          • Lyft
          • nuScenes
          • KITTI 3D
        • Pointcloud Episodes
          • Supervisely
          • .PCD, .PLY, .LAS, .LAZ pointclouds
          • Lyft
          • nuScenes
          • KITTI 360
        • Volumes
          • Supervisely
          • .NRRD, .DCM volumes
          • NIfTI
      • Import sample dataset
      • Import into an existing dataset
      • Import using Team Files
      • Import from Cloud
      • Import using API & SDK
      • Import using agent
    • Migrations
      • Roboflow to Supervisely
      • Labelbox to Supervisely
      • V7 to Supervisely
      • CVAT to Supervisely
    • Export
  • 📂Data Organization
    • Core concepts
    • MLOps Workflow
    • Projects
      • Datasets
      • Definitions
      • Collections
    • Team Files
    • Disk usage & Cleanup
    • Quality Assurance & Statistics
      • Practical applications of statistics
    • Operations with Data
      • Data Filtration
        • How to use advanced filters
      • Pipelines
      • Augmentations
      • Splitting data
      • Converting data
        • Convert to COCO
        • Convert to YOLO
        • Convert to Pascal VOC
    • Data Commander
      • Clone Project Meta
  • 📝Labeling
    • Labeling Toolboxes
      • Images
      • Videos 2.0
      • Videos 3.0
      • 3D Point Clouds
      • DICOM
      • Multiview images
      • Fisheye
    • Labeling Tools
      • Navigation & Selection Tools
      • Point Tool
      • Bounding Box (Rectangle) Tool
      • Polyline Tool
      • Polygon Tool
      • Brush Tool
      • Mask Pen Tool
      • Smart Tool
      • Graph (Keypoints) Tool
      • Frame-based tagging
    • Labeling Jobs
      • Labeling Queues
      • Labeling Consensus
      • Labeling Statistics
    • Labeling with AI-Assistance
  • 🤝Collaboration
    • Admin panel
      • Users management
      • Teams management
      • Server disk usage
      • Server trash bin
      • Server cleanup
      • Server stats and errors
    • Teams & workspaces
    • Members
    • Issues
    • Guides & exams
    • Activity log
    • Sharing
  • 🖥️Agents
    • Installation
      • Linux
      • Windows
      • AMI AWS
      • Kubernetes
    • How agents work
    • Restart and delete agents
    • Status and monitoring
    • Storage and cleanup
    • Integration with Docker
  • 🔮Neural Networks
    • Overview
    • Inference & Deployment
      • Overview
      • Supervisely Serving Apps
      • Deploy & Predict with Supervisely SDK
      • Using trained models outside of Supervisely
    • Model Evaluation Benchmark
      • Object Detection
      • Instance Segmentation
      • Semantic Segmentation
      • Custom Benchmark Integration
    • Custom Model Integration
      • Overview
      • Custom Inference
      • Custom Training
    • Legacy
      • Starting with Neural Networks
      • Train custom Neural Networks
      • Run pre-trained models
  • 👔Enterprise Edition
    • Get Supervisely
      • Installation
      • Post-installation
      • Upgrade
      • License Update
    • Kubernetes
      • Overview
      • Installation
      • Connect cluster
    • Advanced Tuning
      • HTTPS
      • Remote Storage
      • Single Sign-On (SSO)
      • CDN
      • Notifications
      • Moving Instance
      • Generating Troubleshoot Archive
      • Storage Cleanup
      • Private Apps
      • Data Folder
      • Firewall
      • HTTP Proxy
      • Offline usage
      • Multi-disk usage
      • Managed Postgres
      • Scalability Tuning
  • 🔧Customization and Integration
    • Supervisely .JSON Format
      • Project Structure
      • Project Meta: Classes, Tags, Settings
      • Tags
      • Objects
      • Single-Image Annotation
      • Single-Video Annotation
      • Point Cloud Episodes
      • Volumes Annotation
    • Developer Portal
    • SDK
    • API
  • 💡Resources
    • Changelog
    • GitHub
    • Blog
    • Ecosystem
Powered by GitBook
On this page
  • Overview
  • Format description
  • Key Features
  • How to Use
  • Input files structure
  • Useful links

Was this helpful?

  1. Import and Export
  2. Import
  3. Supported annotation formats
  4. Images

Links from CSV, TXT and TSV

Overview

You can import Images into Supervisely project using a .csv, .tsv or .txt file. This import converter is designed to help you quickly upload images to Supervisely from a file containing image paths from Team Files or URLs from cloud storage or any accessible internet link (✨ Available only in Enterprise Edition).

Additionally, you can assign tags to each image by providing a tag column in the input file. This feature is optional, and you can choose to import images without any tags.

Format description

Supported file formats: .csv, .tsv, and .txt. With annotations: yes (optional) Supported annotation types: tags Grouped by: Any structure (will be uploaded as a single dataset)\

Key Features

  • Import Images from Team Files

  • Import Images by URLs from cloud storage or any accessible internet link (✨ Available only in Enterprise Edition)

  • Supported file formats: .csv, .tsv or .txt

  • Automatically assign Tags to each Image (optional)

How to Use

All images will be uploaded to a single dataset, so you don't have to worry about the full project structure in Supervisely format. All you need is to prepare a file with URLs or paths and drop this file in quick import.

Input files structure

In your input file, the first column is crucial as it contains either the paths or URLs to the images you want to import. This column is mandatory for the importer to function correctly.

The second column, which contains the tags, is optional. If provided, these tags will be automatically assigned to the corresponding images upon import. If this column is omitted, the images will be imported without any tags.

Yes, tags are optional for each image. If you choose not to assign tags to certain images, simply leave the tag column blank for those images in your input file. The importer will still process these images and upload them without any tags.

Delimiters for File Formats

In the context of this importer, we use specific delimiters for different purposes in the .csv file:

Column Delimiter

  • .csv - A semicolon (;) is used to separate different columns. Each column represents a different attribute, such as the image path/url or the image tag.

  • .tsv - A tab () is used as a delimiter to separate different columns. Similar to the .csv format, each column represents a different attribute.

  • .txt - Multiple spaces ( ), tabs (), or a semicolon (;) can be used to separate different columns in a .txt file. Each column represents a different attribute, similar to the .csv and .tsv formats. Please note, ⚠️ a single space ( ) cannot be used as a delimiter.

Tag Delimiter

A comma (,) is used to separate different tags assigned to the same image for all file formats. This allows you to assign multiple tags to a single image.

Examples

Regardless of the file format you choose (.csv, .tsv, or .txt), you can specify either a path or a URL for each image, but not both in the same file. This means that each file should contain either paths to local images or URLs to internet images, but not a mix of both.

  1. Team Files: Create a .csv file with columns for the relative path to the image and the image tag.

        path;tag
        /dogs/img_01.jpeg;dog
        /cats/img_02.jpeg;cat
        /horses/img_01.jpeg;horse
  2. URLs:

  • Create a .txt file with columns for the full URL-link to the image and the image tag. In this example, tab () delimiters are used.

        url	tag
        https://images.io/image_example_1.png	tag1,tag2
        https://images.io/image_example_2.png	tag3
        https://images.io/image_example_3.png
  • Cloud storage link example:

    link structure: <provider name>://<bucket name>/<path to image>

    url;tag
    s3://remote-img-test/08. images YOLO masks, bboxes (mix)/ds1_IMG_0748.jpeg;1
    azure://supervisely-test/TEST-NEW-IMPORT/01. images SLY (from export)/ds1/img/IMG_1836.jpeg
    google://sly-dev-test/test_img_new/berries-02.jpeg;3

Useful links

PreviousImages with PNG masksNextPDF files to images

Last updated 6 months ago

Was this helpful?

Example data:

✨ Importing images by URLs is available only in .

🔁
download ⬇️
the Enterprise Edition
[Supervisely Ecosystem] Import Images from CSV