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This tutorial walks through using the Roboflow plugin inside ChatGPT to auto-label images for object detection, with a warehouse example covering boxes, pallets, forklifts, and workers. The useful takeaway for ML practitioners is the workflow: a vision model proposes bounding boxes on a small sample, the user checks those predictions, and only then is the auto-label job run on the full batch. Human review of annotations remains a required step before the data feeds into training.
How to Auto-Label Images with ChatGPT and Roboflow
Learn how to auto-label images with the Roboflow plugin in ChatGPT. Create a project, preview bounding boxes and class labels and then review annotations and generate a dataset version for model training. Follow each step with simple prompts and visual previews.
To auto-label an image dataset from ChatGPT, install the Roboflow plugin, connect your workspace, and describe the job in plain language. It creates the project, uploads your images, previews an auto-labeling model on a few samples, then labels the full batch. You can auto-label images with ChatGPT using the Roboflow plugin (opens in a new tab). Upload your images, describe the objects you want to detect, and use plain-language prompts to create a project, preview bounding boxes, and start an automatic labeling job. ChatGPT manages these steps through Roboflow, while your selected vision model generates…
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