A hands-on tutorial explaining how to generate a custom Zero-Shot image classifier without training, using a pre-trained CLIP model. Full code included.
Imagine you need to classify whether people wear glasses, but you have no data or resources to train a custom model. In this tutorial, you will learn how to use a pre-trained CLIP model to create a custom classifier without any training required. This approach is known as Zero-Shot image classification, and it enables classifying images of classes that were not explicitly seen during the training of the original CLIP model. An easy-to-use Jupyter notebook with the full code is provided below for your convenience.
The CLIP (Contrastive Language-Image Pre-training) model, developed by OpenAI, is a multi-modal vision and language model. It maps images and text descriptions to the same latent space, allowing it to determine whether an image and description match. CLIP was trained in a https://towardsdatascience.com/clip-creating-image-classifiers-without-data-b21c72b741fa?source=rss—-7f60cf5620c9—4
Lihi Gur Arie, PhD
2023-02-22 16:40:30
Towards Data Science – Medium
https://areyoupop.com/wp-content/uploads/CLIP-Creating-Image-Classifiers-Without-Data-by-Lihi-Gur.png[rule_{ruleNumber}]
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https://towardsdatascience.com/clip-creating-image-classifiers-without-data-b21c72b741fa?source=rss—-7f60cf5620c9—4
towardsdatascience.com
https%3A%2F%2Ftowardsdatascience.com%2Fclip-creating-image-classifiers-without-data-b21c72b741fa%3Fsource%3Drss—-7f60cf5620c9—4
computer-vision,deep-learning,clip,zero-shot-learning,image-classification
hashtags : #CLIP #Creating #Image #Classifiers #Data #Lihi #Gur #Arie #PhD..
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