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--- |
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license: apache-2.0 |
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tags: |
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- vision |
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datasets: |
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- imagenet-21k |
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--- |
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# ImageGPT (small-sized model) |
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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](https://cdn.openai.com/papers/Generative_Pretraining_from_Pixels_V2.pdf) by Chen et al. and first released in [this repository](https://github.com/openai/image-gpt). See also the official [blog post](https://openai.com/blog/image-gpt/). |
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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. |
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## Model description |
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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. |
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The goal for the model is simply to predict the next pixel value, given the previous ones. |
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By pre-training the model, it learns an inner representation of images that can then be used to: |
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- 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". |
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- perform (un)conditional image generation. |
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## Intended uses & limitations |
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You can use the raw model for either feature extractor or (un) conditional image generation. See the [model hub](https://huggingface.co/models?search=openai/imagegpt) to all ImageGPT variants. |
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### How to use |
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Here is how to use this model in PyTorch to perform unconditional image generation: |
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```python |
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from transformers import ImageGPTImageProcessor, ImageGPTForCausalImageModeling |
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import torch |
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import matplotlib.pyplot as plt |
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import numpy as np |
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feature_extractor = ImageGPTImageProcessor.from_pretrained('openai/imagegpt-small') |
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model = ImageGPTForCausalImageModeling.from_pretrained('openai/imagegpt-small') |
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu") |
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model.to(device) |
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# unconditional generation of 8 images |
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batch_size = 8 |
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context = torch.full((batch_size, 1), model.config.vocab_size - 1) #initialize with SOS token |
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context = torch.tensor(context).to(device) |
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output = model.generate(pixel_values=context, max_length=model.config.n_positions + 1, temperature=1.0, do_sample=True, top_k=40) |
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clusters = feature_extractor.clusters |
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n_px = feature_extractor.size |
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samples = output[:,1:].cpu().detach().numpy() |
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samples_img = [np.reshape(np.rint(127.5 * (clusters[s] + 1.0)), [n_px, n_px, 3]).astype(np.uint8) for s in samples] # convert color cluster tokens back to pixels |
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f, axes = plt.subplots(1, batch_size, dpi=300) |
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for img, ax in zip(samples_img, axes): |
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ax.axis('off') |
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ax.imshow(img) |
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``` |
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## Training data |
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The ImageGPT model was pretrained on [ImageNet-21k](http://www.image-net.org/), a dataset consisting of 14 million images and 21k classes. |
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## Training procedure |
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### Preprocessing |
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Images are first resized/rescaled to the same resolution (32x32) and normalized across the RGB channels. Next, color-clustering is performed. This means that every pixel is turned into one of 512 possible cluster values. This way, one ends up with a sequence of 32x32 = 1024 pixel values, rather than 32x32x3 = 3072, which is prohibitively large for Transformer-based models. |
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### Pretraining |
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Training details can be found in section 3.4 of v2 of the paper. |
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## Evaluation results |
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For evaluation results on several image classification benchmarks, we refer to the original paper. |
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### BibTeX entry and citation info |
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```bibtex |
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@InProceedings{pmlr-v119-chen20s, |
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title = {Generative Pretraining From Pixels}, |
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author = {Chen, Mark and Radford, Alec and Child, Rewon and Wu, Jeffrey and Jun, Heewoo and Luan, David and Sutskever, Ilya}, |
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booktitle = {Proceedings of the 37th International Conference on Machine Learning}, |
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pages = {1691--1703}, |
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year = {2020}, |
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editor = {III, Hal Daumé and Singh, Aarti}, |
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volume = {119}, |
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series = {Proceedings of Machine Learning Research}, |
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month = {13--18 Jul}, |
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publisher = {PMLR}, |
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pdf = {http://proceedings.mlr.press/v119/chen20s/chen20s.pdf}, |
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url = {https://proceedings.mlr.press/v119/chen20s.html |
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} |
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``` |
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```bibtex |
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@inproceedings{deng2009imagenet, |
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title={Imagenet: A large-scale hierarchical image database}, |
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author={Deng, Jia and Dong, Wei and Socher, Richard and Li, Li-Jia and Li, Kai and Fei-Fei, Li}, |
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booktitle={2009 IEEE conference on computer vision and pattern recognition}, |
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pages={248--255}, |
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year={2009}, |
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organization={Ieee} |
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} |
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``` |
