This guide covers work batches in Mission Control — how to group sequences into manageable units, assign them to annotators, and track progress.
What Are Work Batches?
A work batch is a group of sequences assigned to an annotator as a single unit of work. Batches provide structure to large annotation projects by breaking them into manageable pieces.
Instead of assigning an entire dataset to one person, you create batches that:
- Distribute work evenly across your annotation team
- Set clear scope for each annotator’s session
- Enable progress tracking at a granular level
- Allow parallel work on the same dataset
Creating Work Batches
From the Project View
- Navigate to Projects → your project → Batches
- Click Create Batch
- Configure the batch:
- Name: Descriptive label (e.g., “Highway Scenes Batch 3”)
- Sequences: Select which sequences to include
- Assignee: Choose an annotator (or leave unassigned)
- Priority: Normal or High
- Due date: Optional deadline
- Click Create
Automatic Batch Creation
For large datasets, use automatic batching:
- Go to Batches → Auto-Create
- Set parameters:
- Batch size: Number of sequences per batch (e.g., 50)
- Assignment mode: Round-robin, random, or manual
- Annotators: Select the team members to distribute to
- Click Generate Batches
- Review the generated batches and confirm
Automatic batching distributes sequences evenly. If you have 500 sequences and set a batch size of 50, Avala creates 10 batches.
From a Smart View
You can also create batches from filtered views:
- Apply filters to your sequences (e.g., status, tags, metadata)
- Select the filtered results
- Click Create Batch from Selection
Assigning Batches to Annotators
Direct Assignment
- Open a batch from the Batches tab
- Click Assign
- Select an annotator from the team member list
- The annotator receives a notification and sees the batch in their work queue
Reassignment
If an annotator is unavailable:
- Open the batch
- Click Reassign
- Select a new annotator
- Optionally add a note explaining the reassignment
- The new annotator picks up where the previous one left off
Self-Assignment
When enabled in project settings, annotators can claim unassigned batches:
- Annotator goes to My Work → Available Batches
- Reviews available batches
- Clicks Claim on a batch to self-assign it
Tracking Batch Progress
Batch Dashboard
The batch dashboard shows an overview of all batches in a project:
| Column | Description |
|---|
| Name | Batch name |
| Assignee | Annotator assigned to the batch |
| Status | Current batch status |
| Progress | Sequences completed out of total |
| Due date | Deadline, if set |
| Created | When the batch was created |
Progress Indicators
Each batch shows a progress bar:
- Gray: Sequences not yet started
- Blue: Sequences in progress
- Yellow: Sequences in review
- Green: Sequences completed
Detailed Batch View
Click a batch to see:
- List of all sequences with individual statuses
- Time spent per sequence
- Issues flagged during annotation or review
- Activity timeline
Session Management
Starting a Session
When an annotator begins work:
- Go to My Work → select a batch
- Click Start Session
- The first available sequence opens in the annotation viewer
- A session timer begins tracking time
Pausing and Resuming
- Pause: Click Pause Session in the toolbar. Progress is saved, and the annotator can return later.
- Resume: Go to My Work → select the batch → Resume. The viewer opens to where the annotator left off.
Completing a Session
When the annotator finishes all sequences in a batch:
- The last sequence is submitted
- The batch status changes to In Review (if review is configured) or Completed
- A summary shows:
- Total time spent
- Number of sequences completed
- Number of annotations created
Batch Statuses
| Status | Description |
|---|
| Unassigned | Batch is created but not assigned to anyone |
| Assigned | Batch is assigned to an annotator but work has not started |
| In Progress | Annotator is actively working on the batch |
| Paused | Work is paused; can be resumed |
| In Review | All sequences are submitted and pending review |
| Rework | Reviewer rejected one or more sequences; annotator needs to correct them |
| Completed | All sequences are approved |
Unassigned → Assigned → In Progress → In Review → Completed
↕ ↓
Paused Rework → In Progress
Best Practices for Batch Sizing
| Dataset Size | Recommended Batch Size | Reasoning |
|---|
| < 100 sequences | 20-30 | Small enough for a single session |
| 100-500 sequences | 30-50 | Balanced between overhead and manageability |
| 500-2000 sequences | 50-100 | Larger batches reduce management overhead |
| > 2000 sequences | 100-200 | Large batches with periodic check-ins |
Sizing Considerations
- Annotation complexity: Complex annotations (dense segmentation, many objects per frame) warrant smaller batches
- Annotator experience: Newer annotators benefit from smaller batches with more frequent review
- Deadline pressure: Smaller batches complete faster and allow earlier review cycles
- Data variety: If sequences vary significantly, keep batches smaller so reviewers can calibrate per batch
Avoid creating very large batches (500+ sequences). They make progress tracking difficult and delay the review cycle since the entire batch must be submitted before review begins.
Next Steps
