Image Metadata

Metadata lets you attach custom key-value pairs to images in your Roboflow workspace. Use metadata to store structured information alongside your images -- such as capture conditions, device identifiers, quality scores, or any domain-specific attributes -- and then search, filter, and organize your data around those attributes.

Overview

Each image can hold any number of metadata entries. An entry is a key (a name like camera_id) paired with a value (a string, number, or boolean).

Use cases

• Capture context -- record camera ID, GPS coordinates, weather, lighting conditions
• Quality tracking -- attach confidence scores, review status, annotator IDs
• Data slicing -- filter your dataset by any attribute to build targeted training sets
• External system linking -- store identifiers that connect images back to your internal tools

Adding Metadata

You can add metadata to images through the web UI, the Python SDK, the REST API, or automatically via S3 Bucket Mirror.

Web Application

1. Open any image in your project.
2. In the metadata section, enter a key in the first input and a value in the second input.
3. Press Enter to save or click on Add.

Values are automatically parsed by type:

Python SDK

Pass a metadata dictionary when uploading an image:

import roboflow

rf = roboflow.Roboflow(api_key="YOUR_API_KEY")
project = rf.workspace("your-workspace").project("your-project")

project.upload(
    image_path="image.jpg",
    metadata={
        "camera_id": "cam001",
        "location": "warehouse-3",
        "temperature": 72.5,
        "is_night": False
    }
)

REST API

Add metadata during upload

Include a metadata field (JSON-stringified) in the multipart form data when uploading an image:

curl -X POST "https://api.roboflow.com/dataset/your-dataset/upload?api_key=YOUR_API_KEY" \
  -F "name=image.jpg" \
  -F "split=train" \
  -F "file=@image.jpg" \
  -F 'metadata={"camera_id":"cam001","temperature":72.5}'

S3 Bucket Mirror

When using Bucket Mirror to sync images from an S3 bucket, you can attach metadata by placing a .json file alongside each image with the same base name:

my-bucket/
  images/
    photo_001.jpg
    photo_001.json      # metadata for photo_001.jpg
    photo_002.jpg
    photo_002.json      # metadata for photo_002.jpg

The JSON file contains your metadata as key-value pairs:

{
    "camera_id": "cam001",
    "location": "warehouse-3",
    "capture": { "temperature": 72.5, "humidity": 45 }
}

Nested objects are automatically flattened using dot notation. The example above produces:

Metadata file constraints

• Maximum file size: 256 KB
• Must be valid JSON
• null and undefined values are filtered out

Update strategies

Bucket Mirror supports different strategies for how synced metadata interacts with metadata you've set manually in the UI or via API:

Searching by Metadata

Metadata is indexed and searchable in the Asset Library. Use the search bar to filter images by metadata values:

metadata.camera_id:"cam001"
metadata.quality_score>80
metadata.reviewed:true

You can combine metadata filters with other search filters:

metadata.location:"warehouse-3" AND class:forklift

The Asset Library also provides autocomplete for metadata keys and values based on what exists in your workspace.

Key Naming Rules

Metadata keys must follow these rules:

Valid keys: camera_id, capture.temperature, _internal_ref, v2_score

Invalid keys: camera/id (contains /), .starts_with_dot (starts with .), has spaces (contains spaces)

Metadata vs. Tags

Both metadata and tags help you organize images, but they serve different purposes:

You can use both on the same image. For example, tag an image as reviewed and also store reviewer: "alice" and confidence: 0.95 as metadata.