ImageGPT (small sized model) ImageGPT (iGPT) model pre trained on ImageNet ILSVRC 2012 (14 million images, 21,843 classes) at resolution 32x32. It was introduced in the paper Generative Pretraining from Pixels by Chen et al. and first released in this repository. See also the official blog post. Disclaimer: The team releasing ImageGPT did not write a model card for this model so this model card has been written by the Hugging Face team. Model description The ImageGPT (iGPT) is a transformer decoder model (GPT like) pretrained on a large collection of images in a self supervised fashion, namely ImageNet 21k, at a resolution of 32x32 pixels. The goal for the model is simply to predict the next pixel value, given the previous ones. By pre training the model, it learns an inner representation of images that can then be used to: extract features useful for downstream tasks: one can either use ImageGPT to produce fixed image features, in order to train a linear model (like a sklearn logistic regression model or SVM). This is also referred to as "linear probing". perform (un)conditional image generation. Intended uses & limitations You can use the raw model for either feature extractor or…
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