Train an Image Classification Model with No Labeling
Blog post from Roboflow
Autodistill, an open-source project by Roboflow, allows users to train image classification models without manual labeling by utilizing large foundation vision models like CLIP, BLIP, DINOv2, and ALBEF to automatically label images. This guide demonstrates how to train a classification model that determines whether street signs are in good shape or damaged using CLIP for automatic image labeling and YOLOv8 for training the classification model. After labeling images with CLIP according to text prompts, the guide details the steps to train a YOLOv8 model, test its performance, and deploy the model using Roboflow's scalable API and SDKs or self-hosted solutions like Roboflow Inference. The process emphasizes the efficiency of going from unlabeled images to a fully-trained model, suitable for various tasks like object detection, classification, and segmentation.
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