What Data for This Category Looks Like
Image annotation project data primarily comes from image samples produced by annotation platforms, annotation specification documents, category dictionaries, sampling inspection results, and rework lists. Daily produced documents include box selection examples, quality inspection reports, and rework lists. Most fields include target bounding box coordinates, category labels, confidence scores, sampling inspection markers, and similar items. Data updates are triggered daily or after a single batch of annotation tasks is completed. Sample sizes and annotation rules for different tasks have no unified fixed format, and must be dynamically adjusted based on annotation requirements.
What Constraints Do These Characteristics Impose on Workflow Orchestration
The data characteristics of image annotation projects impose multiple constraints on workflow orchestration. First, data centers on visual samples. Workflows must support input nodes that access image URLs and local image paths, and adapt to verification and processing logic for visual data. Second, data updates occur daily or per batch. Workflows must support configuration for scheduled triggers or batch task triggers, to adapt to verification requirements for different update rhythms. In addition, documents such as annotation specifications and box selection examples must be passed into workflows as dynamic variables for real-time compliance verification of annotation results. Verification rules must also be set for visual-specific fields like bounding box coordinates and category matching, to prevent invalid data from flowing into subsequent links.
How to Configure
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Trigger Mode | Per batch task completion / Scheduled trigger (86400 seconds interval) | Matches the daily or per-batch data update rhythm, adapts to the execution cycle of annotation tasks |
variables | Pass file paths for annotation specifications, box selection examples, and category dictionaries | Dynamically load verification rules and reference data, adapting to the exclusive requirements of different annotation projects |
| Node Verification Logic | Enable bounding box coordinate verification and category matching verification | Verify the compliance of visual annotation results, preventing invalid data from flowing into subsequent links |
| Failure Retry Count | 2–3 times | Most annotation data verification failures are temporary format issues; limited retries can reduce manual intervention |
| Timeout Threshold | 300–600 seconds | Image parsing and verification require a certain processing duration; avoid short timeouts interrupting the workflow. FastGPT v4.17.0 optimizes timeout processing logic for visual nodes |
nodeId | Bind the IDs of quality inspection nodes and verification nodes | Ensure the correct execution order and dependency relationships of workflow nodes, avoiding missing links |
Three Common Misconfigurations
- Phenomenon: No errors occur when testing the workflow locally, but an error "undefined is not valid JSON" displays when calling via external channels. Cause: Required parameters for channel calls are not configured in
variables, leading to missing incoming data format. - Phenomenon: After passing an image URL into the workflow, the preview cannot be displayed normally. Cause: No URL verification rules are configured for the image parsing node, or the node's image rendering permission is not enabled.
- Phenomenon: No verification result output is generated after the workflow runs. Cause: The execution order of verification nodes specified by
nodeIdis not bound, so the verification link is not triggered.
How to Confirm the Configuration is Complete
- Execute a local test call, pass in annotation sample data and verification rules, check the workflow node execution logs, and confirm all verification links trigger normally.
- Configure an external channel call, pass in test data that conforms to the format, and verify whether returned results include verified annotation results and exception markers.
- Adjust the data update frequency, and verify whether the workflow automatically triggers execution per the preset scheduled or batch rules.
- Check the configuration items for
variablesandnodeId, confirm all required parameters have been correctly passed and bound to corresponding nodes.
The values provided are common starting points and should be evaluated against the operator’s own sample data.
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.