Task Management & Metrics
The Tasks page provides a high-level overview of project progress through a detailed data table. Each entry allows managers to monitor the health and velocity of the project using the following metrics:- Status: The current stage of the task (e.g., Prepare, Label, Review, Completed).
- Assignee: The specific user currently responsible for the task.
- Duration: The active time spent by annotators on the task.
- Object Count: The total number of annotations/objects created within the task.
- Split: The dataset category (Train, Validation, or Test) assigned to the task.
- Task History: A chronological log of every action, status change, and user interaction.
- Operational Tags: Visibility into priority levels, batch assignments, “skipped” status, and the exact time the task entered the queue.
Bulk Operations
To manage large-scale datasets efficiently, PixlHub allows for administrative actions to be performed on multiple selected tasks simultaneously.1. Change Status
This core operation moves tasks between different stages of the workflow. When changing status, managers can also perform “cleanup” actions via a modal window:- Remove Annotations: Wipes all existing labels so annotators can start from scratch.
- Remove Assignees: Unassigns users, returning the tasks to the general pool/queue.
- Remove Stage History: Deletes the chronological record of the task’s journey.
- Remove Duration: Resets the “time spent” metric to zero.
2. Set Split
This function organizes data for machine learning workflows. Tasks can be assigned manually or via Auto Split into three standard categories:- Train: Data used for model training.
- Validation: Data used for hyperparameter tuning and progress monitoring.
- Test: Independent data used for the final model evaluation.
