Annotation Editor

Ultralytics Platform includes a powerful annotation editor for labeling images with bounding boxes, polygons, keypoints, oriented boxes, and classifications. The editor supports manual drawing and SAM-powered smart annotation.

graph TB
    subgraph Manual["Manual Tools"]
        A[Box] & B[Polygon] & C[Keypoint] & D[OBB] & E[Classify]
    end
    subgraph AI["AI-Assisted"]
        F[SAM Smart]
    end
    Manual --> H[Save Labels]
    AI --> H

Supported Task Types

The annotation editor supports all 5 YOLO task types:

Task Details

Getting Started

To annotate images:

1. Navigate to your dataset
2. Click on an image to open the fullscreen viewer
3. Click Edit to enter annotation mode
4. Select your annotation tool from the toolbar
5. Draw annotations on the image
6. Click Save when finished

graph LR
    A[Open Dataset] --> B[Click Image]
    B --> C[Click Edit]
    C --> D[Draw Annotations]
    D --> E[Save]
    E --> F[Next Image]
    F --> B

    style C fill:#2196F3,color:#fff
    style D fill:#FF9800,color:#fff
    style E fill:#4CAF50,color:#fff

Annotation Modes

The editor provides two annotation modes, selectable from the toolbar:

Manual Annotation Tools

Bounding Box (Detect)

Draw rectangular boxes around objects:

1. Enter edit mode and select Draw
2. Click and drag to draw a rectangle
3. Release to complete the box
4. Select a class from the dropdown

Polygon (Segment)

Draw precise polygon masks:

1. Enter edit mode and select Draw
2. Click to add vertices
3. Right-click or press Enter to close the polygon
4. Select a class from the dropdown

Keypoint (Pose)

Place 17 COCO keypoints for human pose:

1. Enter edit mode and select Draw
2. Click to place keypoints in sequence
3. Follow the COCO skeleton order

The 17 COCO keypoints are:

Oriented Bounding Box (OBB)

Draw rotated boxes for angled objects:

1. Enter edit mode and select Draw
2. Click and drag to draw an initial box
3. Use the rotation handle to adjust angle
4. Drag corner handles to resize
5. Select a class from the dropdown

Classification (Classify)

Assign image-level class labels:

1. Enter edit mode
2. A side panel appears with class selection buttons
3. Click on class buttons or press number keys 1-9

SAM Smart Annotation

Segment Anything Model (SAM) enables intelligent annotation with just a few clicks. Smart mode is available for detect, segment, and OBB tasks.

1. Enter edit mode and select Smart or press S
2. Left-click to add positive points (include this area)
3. Right-click to add negative points (exclude this area)
4. SAM generates a precise mask in real-time
5. Press Enter or Escape to save the annotation

graph LR
    A[Press S] --> B[Left-click Object]
    B --> C[SAM Generates Mask]
    C --> D{Accurate?}
    D -->|Yes| E[Enter to Save]
    D -->|No| F[Add +/- Points]
    F --> C

    style A fill:#2196F3,color:#fff
    style C fill:#FF9800,color:#fff
    style E fill:#4CAF50,color:#fff

SAM smart annotation can generate:

• Polygons for segmentation tasks
• Bounding boxes for detection tasks
• Oriented boxes for OBB tasks

class Sidebar

The annotation editor includes a collapsible class sidebar on the right side of the canvas. The sidebar provides:

• Search classes: Filter the class list by typing in the search field. Press Enter on an exact match to select it, or create a new class if no match exists.
• Create new class inline: Click Add class at the bottom of the list, type a name, and optionally pick a custom color. Press Enter to create.
• Edit class name inline: Hover over a class name and click the pencil icon to rename it.
• Color picker: Click the color swatch next to any class to change its color.
• Per-class annotation count: Each class row shows a superscript count of annotations.
• Expand/collapse: Click the chevron to expand a class and see individual annotations listed below it.
• Bidirectional hover highlighting: Hovering an annotation on the canvas highlights it in the sidebar, and vice versa. The sidebar auto-scrolls and auto-expands to the relevant class.
• Hide/show individual annotations: Click the eye icon on any annotation row to toggle its visibility on the canvas.
• Delete annotations: Click the trash icon on any annotation row to delete it.
• Keyboard shortcuts: Press 1-9 to quickly select the first 9 classes.

Context Menu

Right-click on selected annotations to open a context menu with:

Visibility Controls

The visibility dropdown (eye icon) lets you toggle display of individual elements:

Crosshair Cursor

In edit mode, a crosshair overlay tracks the cursor position and displays pixel coordinates on the canvas. This helps place annotations with precision. Toggle it via the visibility dropdown.

SAM Hover Preview

In Smart mode for segment tasks, SAM provides a real-time mask preview as you hover over the image — before clicking any points. This lets you see the predicted segmentation boundary and decide where to click. Once you add positive or negative points, the preview updates to reflect your refinements.

Polygon Vertex Editing

For segment annotations, you can edit polygon vertices after drawing:

• Move vertices: Drag any vertex handle to reposition it
• Delete vertices: Select a vertex and press Delete to remove it

class Management

Creating Classes

Define annotation classes for your dataset in the Classes tab:

1. Navigate to the Classes tab
2. Use the input field at the bottom to type a class name
3. Click Add or press Enter
4. A color is assigned automatically from the Ultralytics palette

Add New Class During Annotation

You can create new classes directly while annotating without leaving the editor:

1. Draw an annotation on the image
2. In the class dropdown, click Add New Class
3. Enter the class name
4. Press Enter to create and assign

This allows for a seamless workflow where you can define classes as you encounter new object types in your data.

Editing Classes

• Rename: Click a class name in the table to edit it inline
• Change color: Click the color swatch to open the color picker
• Search: Use the search field to filter classes by name
• Sort: Click column headers to sort by name, label count, or image count

class Colors

Each class is assigned a color from the Ultralytics palette. You can customize colors using the color picker on the Classes tab. Colors are consistent across the platform for easy recognition.

Keyboard Shortcuts

Efficient annotation with keyboard shortcuts:

Undo/Redo

The annotation editor maintains a full undo/redo history:

• Undo: Cmd/Ctrl+Z
• Redo: Cmd/Ctrl+Shift+Z or Cmd/Ctrl+Y

History tracks:

• Adding annotations (single and batch)
• Editing annotations (move, resize, rotate)
• Deleting annotations (single and batch)
• Changing classes (single and batch)
• Reordering annotations (z-order)
• Editing polygon vertices (add, remove, move)
• Moving keypoints

Saving Annotations

Annotations are saved when you click Save or press Cmd/Ctrl+S:

• Save: Click the save button or press Cmd/Ctrl+S
• Cancel: Click cancel to discard changes
• Escape: Saves if there are unsaved changes, otherwise exits edit mode

FAQ

How accurate is SAM annotation?

SAM provides high-quality masks for most objects. Accuracy depends on:

• Object distinctiveness from background
• Image quality and resolution
• Number of positive/negative points provided

For best results, start with a positive point on the object center and add negative points to exclude nearby objects.

Can I import existing annotations?

Yes, upload your dataset with YOLO-format label files. The Platform automatically parses and displays them in the editor.

How do I annotate multiple objects of the same class?

After drawing an annotation:

1. Keep the same class selected
2. Draw the next annotation
3. Repeat until all objects are labeled

The keyboard shortcut 1-9 quickly selects classes.

Can I train on partially annotated datasets?

Yes, but for best results:

• Label all objects of your target classes in each image
• Use the label filter set to Unannotated to identify unlabeled images
• Exclude unannotated images from training configuration

Which tasks support SAM smart annotation?

SAM smart annotation is available for detect, segment, and OBB tasks. Classification and pose tasks use manual annotation only.

📅 Created 1 month ago ✏️ Updated 5 days ago

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