> For the complete documentation index, see [llms.txt](https://docs.roboflow.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.roboflow.com/datasets/create-and-upload/adding-data/image-metadata.md).

# 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).

| Value type | Examples                                    |
| ---------- | ------------------------------------------- |
| String     | `location: "warehouse-3"`, `shift: "night"` |
| Number     | `temperature: 72.5`, `quality_score: 95`    |
| Boolean    | `reviewed: true`, `is_night: false`         |

### 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](/datasets/create-and-upload/adding-data/datasources.md).

{% hint style="info" %}
If your images live in cloud storage like AWS S3, use [Datasources](/datasets/create-and-upload/adding-data/datasources.md) and Bucket Mirror so image files and metadata sidecars stay in sync. Signed URL or manual uploads do not provide the same ongoing metadata sync behavior.
{% endhint %}

### Web Application

{% stepper %}
{% step %}

#### Open an image

Open any image in your project.
{% endstep %}

{% step %}

#### Enter key and value

In the metadata section, enter a **key** in the first input and a **value** in the second input.
{% endstep %}

{% step %}

#### Add

Press **Enter** to save or click on Add
{% endstep %}
{% endstepper %}

Values are automatically parsed by type:

| Value entered    | Stored as                  |
| ---------------- | -------------------------- |
| `front`          | `"front"` (string)         |
| `95`             | `95` (number)              |
| `3.14`           | `3.14` (number)            |
| `true` / `false` | `true` / `false` (boolean) |

<figure><img src="/files/wceZLvC3NtsISJOsbdIr" alt=""><figcaption><p>Annotation Tool's metadata editor</p></figcaption></figure>

### Python SDK

Pass a `metadata` dictionary when uploading an image:

```python
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
    }
)
```

### CLI

Use the `roboflow image metadata` command to update metadata and tags on existing images:

```bash
# Set metadata on a single image
roboflow image metadata <image_id> -m '{"camera_id": "cam001", "location": "warehouse-3"}'

# Add tags to an image
roboflow image metadata <image_id> --tags "reviewed,v2"

# Remove metadata keys
roboflow image metadata <image_id> --remove-metadata "old_key,deprecated_field"

# Remove tags
roboflow image metadata <image_id> --remove-tags "draft"

# Combine: set metadata, add tags, and remove tags in one call
roboflow image metadata <image_id> -m '{"quality_score": 95}' --tags "reviewed" --remove-tags "pending"

# Batch update multiple images (async)
roboflow image metadata img1,img2,img3 -m '{"batch": "june-2026"}' --tags "processed" --poll
```

A single image ID updates synchronously. Multiple comma-separated IDs (up to 1,000) use the batch async endpoint. Add `--poll` to wait for the batch to finish; without it the command returns a `taskId` you can check later with `roboflow asynctasks get <task-id>`.

| Flag                   | Description                                 |
| ---------------------- | ------------------------------------------- |
| `-m`, `--metadata`     | JSON string of key-value pairs to set       |
| `--remove-metadata`    | Comma-separated metadata keys to remove     |
| `--tags`               | Comma-separated tags to add                 |
| `--remove-tags`        | Comma-separated tags to remove              |
| `--poll` / `--no-poll` | Wait for batch completion (batch mode only) |
| `--timeout`            | Polling timeout in seconds (default: 1800)  |

### REST API

#### Add metadata during upload

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

```bash
curl -X POST "https://api.roboflow.com/dataset/your-dataset/upload?api_key=$ROBOFLOW_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 [Datasources](/datasets/create-and-upload/adding-data/datasources.md) to sync images from an S3 bucket, metadata is imported via JSON sidecar files placed alongside each image. See [Datasources](/datasets/create-and-upload/adding-data/datasources.md) for sidecar file format, constraints, and update strategies.

## Searching by Metadata

Metadata is indexed and searchable in the [Asset Library](https://docs.roboflow.com/platform/workspaces/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:

| Rule                 | Detail                                                                 |
| -------------------- | ---------------------------------------------------------------------- |
| Allowed characters   | Letters (`a-z`, `A-Z`), numbers (`0-9`), underscores (`_`), dots (`.`) |
| First character      | Must be a letter, number, or underscore                                |
| Forbidden characters | Forward slashes (`/`) are not allowed                                  |

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](/datasets/manage/manage-datasets/add-tags-to-images.md) help you organize images, but they serve different purposes:

|               | Tags                                 | Metadata                                   |
| ------------- | ------------------------------------ | ------------------------------------------ |
| **Structure** | Simple labels                        | Key-value pairs                            |
| **Values**    | No value, just a name                | String, number, or boolean                 |
| **Best for**  | Categorization, workflow status      | Structured attributes, measurements        |
| **Example**   | `reviewed`, `v2`, `needs-annotation` | `temperature: 72.5`, `camera_id: "cam001"` |

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.