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This guide covers work batches in Avala — how to group sequences into manageable units, assign them to annotators, and track progress.

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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

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Creating Work Batches

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From the Project View

  1. Navigate to Projects → your project → Batches
  2. Click Create Batch
  3. 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
  4. Click Create

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Automatic Batch Creation

For large datasets, use automatic batching:
  1. Go to Batches → Auto-Create
  2. 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
  3. Click Generate Batches
  4. 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.

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From a Smart View

You can also create batches from filtered views:
  1. Apply filters to your sequences (e.g., status, tags, metadata)
  2. Select the filtered results
  3. Click Create Batch from Selection

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Assigning Batches to Annotators

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Direct Assignment

  1. Open a batch from the Batches tab
  2. Click Assign
  3. Select an annotator from the team member list
  4. The annotator receives a notification and sees the batch in their work queue

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Reassignment

If an annotator is unavailable:
  1. Open the batch
  2. Click Reassign
  3. Select a new annotator
  4. Optionally add a note explaining the reassignment
  5. The new annotator picks up where the previous one left off

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Self-Assignment

When enabled in project settings, annotators can claim unassigned batches:
  1. Annotator goes to My Work → Available Batches
  2. Reviews available batches
  3. Clicks Claim on a batch to self-assign it

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Tracking Batch Progress

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Batch Dashboard

The batch dashboard shows an overview of all batches in a project:

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Progress Indicators

Each batch shows a progress bar:
  • Gray: Sequences not yet started
  • Blue: Sequences in progress
  • Yellow: Sequences in review
  • Green: Sequences completed

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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

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Session Management

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Starting a Session

When an annotator begins work:
  1. Go to My Work → select a batch
  2. Click Start Session
  3. The first available sequence opens in the annotation viewer
  4. A session timer begins tracking time

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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.

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Completing a Session

When the annotator finishes all sequences in a batch:
  1. The last sequence is submitted
  2. The batch status changes to In Review (if review is configured) or Completed
  3. A summary shows:
    • Total time spent
    • Number of sequences completed
    • Number of annotations created

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Batch Statuses

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Best Practices for Batch Sizing

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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.

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Next Steps