Training a Custom Package Detection Model with Two Labeled Images
Blog post from Roboflow
Andrew Healey's guide details a process for training a custom package detection model using only two labeled images with the help of SegGPT and Autodistill. By creating a Roboflow dataset of images featuring boxes and parcels on a conveyor belt, users can leverage SegGPT's capability to draw segmentation masks based on minimal labeled examples. The process involves uploading images to Roboflow, labeling a few, and then using SegGPT to label the rest, ultimately creating a comprehensive dataset. This labeled dataset is then used to train a computer vision model, which reaches a 95% mean average precision (mAP) after training. The guide suggests methods to improve labeling accuracy if needed and provides steps for uploading the labeled images back to Roboflow for model training and deployment. The process aims to facilitate the deployment of a reliable package detection model, highlighting the ease and efficiency of using minimal data with advanced machine learning tools.
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