What data for this category looks like
The core data of an image annotation project includes four daily document types: annotation specification documents, selected sample image sets, quality inspection reports, and rework lists. Data sources include unannotated image samples, internal annotation platforms, target category dictionaries, and spot check results. Unlike text annotation projects, this data category focuses on the position coordinates and category labels of visual targets. Updates follow daily cycles or the completion node of single-batch annotation tasks. Single-batch data includes annotation coordinates, category labels, quality inspection scores, and rework requirement fields for a single group of images. Data is archived by annotation item and batch dimension, with no fixed unified unit.
What constraints these characteristics impose on the scheduled tasks and automation link
The multi-source mixed data structure of image annotation projects requires scheduled tasks to connect to both the annotation platform API and local file storage simultaneously, to pull image annotation results and quality inspection documents. The update rhythm triggered daily or after single-batch completion requires limiting the batch range for each task execution, to avoid overloading pulled data. Annotation data includes both structured fields and image attachments. The automated summary process must distinguish processing logic for structured reports and unstructured attachments, to prevent task interruptions from failed attachment parsing. Additionally, the privacy of annotation data requires desensitization of image samples during task execution, to avoid sensitive data leaks.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Task Trigger Cycle | Daily / Within 1 hour after single-batch annotation completion | Match the daily update or single-batch delivery rhythm of annotation projects, avoid delayed summary of operational data |
| Single Pull Data Limit | Top 200 annotation records | Adapt to the single-response limit of the annotation platform interface, prevent timeouts or data truncation |
| Report Generation Format | Markdown + attachment packaging | Match the daily document review habits of annotation teams, easy to embed into team shared spaces |
| Failure Retry Count | 3 times, 600-second interval | Respond to temporary interface fluctuations of the annotation platform, avoid terminating tasks due to single exceptions |
| Custom API Request Header Handling | Disable automatic Bearer token appending | Adapt to the authentication rules of the annotation platform, avoid interface authentication failures |
Three common misconfigurations
- Phenomenon: Automatic Bearer token attachment occurs when calling the annotation platform API, causing interface authentication failure. Cause: The switch for automatically appending request headers is not disabled in the configuration, and the default Bearer token is added to custom API requests.
- Phenomenon: Rework list data is missing from generated reports after scheduled task execution. Cause: The storage path of the rework list is not added to the file list pulled by the task, so only annotation record data is pulled.
- Phenomenon: Task execution is terminated due to timeout. Cause: The single pull data limit is set too high, exceeding the single-task data processing threshold of FastGPT v4.17.0.
How to confirm the configuration is correct
- Manually trigger a scheduled task, check if the generated report includes annotation specifications, quality inspection reports, and rework lists
- View task logs, confirm that the number of pulled annotation records matches the preset single pull data limit
- Verify that no Bearer token is automatically attached to custom API requests, ensure normal interface authentication
- Wait for one trigger cycle, check if the report is generated and pushed to the specified location on time
The values given are common starting points and should be measured against the reader's own samples.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-10-19.