benchclaw-stage4-answer-image-processing

To generate model-facing answer images for object detection, this Skill processes raw input images and ground truth annotations, overlaying bounding box labels and performing safe image composition to produce secure outputs ready for model training.

Yes, the image processing workflow supports various image modalities including RGB and depth data, utilizing PIL to safely copy, annotate, and compose multi-view inputs into standardized answer images suitable for model evaluation.

You can process JSONL files by running the Skill with an evidence index and image requests, which parses the JSONL data to drive image copying, annotation overlay, and multi-view composition for generating the required Stage4 answer images.

The best way to add bounding box overlays using PIL is to utilize the Skill's built-in annotation logic, which safely draws labels onto raw images and composes them into model-facing answer images designed specifically for training datasets.

Yes, you can use this Skill to compose multi-view images for object detection evaluation, as it handles multi-view composition and annotation overlay to ensure the final answer images are safe and suitable for model training.

Safe image copying is required when generating answer images to prevent data corruption and ensure the processed output images with annotations and bounding boxes remain secure and suitable for model training and evaluation.