Train a Segmentation Model with No Labeling

Labeling images for segmentation in computer vision can be time-consuming, especially with limited annotated data. However, the open-source project Autodistill, maintained by Roboflow, simplifies this process by automatically generating polygon annotations for various objects. This guide demonstrates how to train a segmentation model without manual labeling using Autodistill and Grounded SAM, which generates bounding boxes and segmentation masks based on text prompts. The process involves installing necessary dependencies, using Grounded SAM for auto-labeling, and training a YOLOv8 segmentation model. The model is tested and refined using a confusion matrix to improve accuracy, particularly in detecting smoke. Deployment options are provided through Roboflow, allowing users to upload model weights for commercial use or deploy models on edge devices using Roboflow Inference. The guide emphasizes the efficiency of using Autodistill, requiring minimal code to label and train a model, and encourages users to explore the full documentation for more information.