Create README.md

README.md

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+---
+license: apache-2.0
+tags:
+datasets:
+- imagenet-21k
+---
+
+# 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](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/).
+
+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 (un) conditional image generation. See the [model hub](https://huggingface.co/models?search=openai/imagegpt) to all ImageGPT variants.
+
+### How to use
+
+Here is how to use this model in PyTorch to perform unconditional image generation:
+
+```python
+from transformers import ImageGPTFeatureExtractor, ImageGPTForCausalImageModeling
+import torch
+import matplotlib.pyplot as plt
+import numpy as np
+
+feature_extractor = ImageGPTFeatureExtractor.from_pretrained('openai/imagegpt-small')
+model = ImageGPTForCausalImageModeling.from_pretrained('openai/imagegpt-small')
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model.to(device)
+
+# unconditional generation of 8 images
+batch_size = 8
+context = torch.full((batch_size, 1), model.config.vocab_size - 1) #initialize with SOS token
+context = torch.tensor(context).to(device)
+output = model.generate(pixel_values=context, max_length=model.config.n_positions + 1, temperature=1.0, do_sample=True, top_k=40)
+
+clusters = feature_extractor.clusters
+n_px = feature_extractor.size
+
+samples = output[:,1:].cpu().detach().numpy()
+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
+
+f, axes = plt.subplots(1, batch_size, dpi=300)
+for img, ax in zip(samples_img, axes):
+   ax.axis('off')
+   ax.imshow(img)
+```
+
+## Training data
+
+The ImageGPT model was pretrained on [ImageNet-21k](http://www.image-net.org/), a dataset consisting of 14 million images and 21k classes. 
+
+## Training procedure
+
+### Preprocessing
+
+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. 
+
+### Pretraining
+
+Training details can be found in section 3.4 of v2 of the paper.
+
+## Evaluation results
+
+For evaluation results on several image classification benchmarks, we refer to the original paper.
+
+### BibTeX entry and citation info
+
+```bibtex
+@InProceedings{pmlr-v119-chen20s,
+  title = 	 {Generative Pretraining From Pixels},
+  author =       {Chen, Mark and Radford, Alec and Child, Rewon and Wu, Jeffrey and Jun, Heewoo and Luan, David and Sutskever, Ilya},
+  booktitle = 	 {Proceedings of the 37th International Conference on Machine Learning},
+  pages = 	 {1691--1703},
+  year = 	 {2020},
+  editor = 	 {III, Hal Daumé and Singh, Aarti},
+  volume = 	 {119},
+  series = 	 {Proceedings of Machine Learning Research},
+  month = 	 {13--18 Jul},
+  publisher =    {PMLR},
+  pdf = 	 {http://proceedings.mlr.press/v119/chen20s/chen20s.pdf},
+  url = 	 {https://proceedings.mlr.press/v119/chen20s.html
+}
+```
+
+```bibtex
+@inproceedings{deng2009imagenet,
+  title={Imagenet: A large-scale hierarchical image database},
+  author={Deng, Jia and Dong, Wei and Socher, Richard and Li, Li-Jia and Li, Kai and Fei-Fei, Li},
+  booktitle={2009 IEEE conference on computer vision and pattern recognition},
+  pages={248--255},
+  year={2009},
+  organization={Ieee}
+}
+```