Minutes, not hours. LabelGPT cranks out thousands of labels from raw images faster than you can finish your coffee. It combines multiple foundation models to handle zero-shot labeling without any manual work upfront.
Just type what you want labeled into a text prompt. Need bounding boxes around cars? Segmentation masks for medical scans? LabelGPT handles both through its foundation model approach. You'll get confidence scores for each label so you can filter out the questionable ones.
A computer vision engineer building autonomous vehicle models could dump 10,000 street images into LabelGPT and get labeled traffic signs back in minutes. Pedestrians too. Vehicles as well. No more paying annotators for weeks of work. LabelGPT claims 75% cost reduction for model building.
Free trial available. After that you'll need to pay — though pricing isn't listed anywhere.
You still need to review what comes out. Foundation models aren't perfect and some labels will be wrong. LabelGPT works with AWS, GCP, and Azure plus offers API access for custom workflows. Batch processing handles all your data with one click, which beats uploading images one by one.
Images only right now. Text or video data labeling isn't supported. Open-source datasets come built-in for experimentation, so you can see how well it performs before feeding it your own data.
