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	---
	license: apache-2.0
	tags:
	datasets:
	- imagenet
	---

	# Perceiver IO for vision (learned position embeddings)

	Perceiver IO model pre-trained on ImageNet (14 million images, 1,000 classes) at resolution 224x224. It was introduced in the paper [Perceiver IO: A General Architecture for Structured Inputs & Outputs](https://arxiv.org/abs/2107.14795) by Jaegle et al. and first released in [this repository](https://github.com/deepmind/deepmind-research/tree/master/perceiver).

	Disclaimer: The team releasing Perceiver IO did not write a model card for this model so this model card has been written by the Hugging Face team.

	## Model description

	Perceiver IO is a transformer encoder model that can be applied on any modality (text, images, audio, video, ...). The core idea is to employ the self-attention mechanism on a not-too-large set of latent vectors (e.g. 256 or 512), and only use the inputs to perform cross-attention with the latents. This allows for the time and memory requirements of the self-attention mechanism to not depend on the size of the inputs.

	To decode, the authors employ so-called decoder queries, which allow to flexibly decode the final hidden states of the latents to produce outputs of arbitrary size and semantics. For image classification, the output is a tensor containing the logits, of shape (batch_size, num_labels).

	<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/perceiver_architecture.jpg" alt="drawing" width="600"/>

	<small> Perceiver IO architecture.</small>

	As the time and memory requirements of the self-attention mechanism don't depend on the size of the inputs, the Perceiver IO authors can train the model directly on raw pixel values, rather than on patches as is done in ViT. This particular model only adds learned 1D position embeddings to the pixel values, hence it is given no privileged information about the 2D structure of images.

	By pre-training the model, it learns an inner representation of images that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled images for instance, you can train a standard classifier by replacing the classification decoder.

	## Intended uses & limitations

	You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=deepmind/perceiver) to look for other fine-tuned versions on a task that may interest you.

	### How to use

	Here is how to use this model in PyTorch:

	```python
	from transformers import PerceiverFeatureExtractor, PerceiverForImageClassificationLearned
	import requests
	from PIL import Image

	feature_extractor = PerceiverFeatureExtractor.from_pretrained("deepmind/vision-perceiver-learned")
	model = PerceiverForImageClassificationLearned.from_pretrained("deepmind/vision-perceiver-learned")

	url = "http://images.cocodataset.org/val2017/000000039769.jpg"
	image = Image.open(requests.get(url, stream=True).raw)

	# prepare input
	encoding = feature_extractor(image, return_tensors="pt")
	inputs = encoding.pixel_values
	# forward pass
	outputs = model(inputs)
	logits = outputs.logits
	print("Predicted class:", model.config.id2label[logits.argmax(-1).item()])
	>>> should print Predicted class: tabby, tabby cat
	```

	## Training data

	This model was pretrained on [ImageNet](http://www.image-net.org/), a dataset consisting of 14 million images and 1k classes.

	## Training procedure

	### Preprocessing

	Images are center cropped and resized to a resolution of 224x224 and normalized across the RGB channels. Note that data augmentation was used during pre-training, as explained in Appendix H of the [paper](https://arxiv.org/abs/2107.14795).

	### Pretraining

	Hyperparameter details can be found in Appendix H of the [paper](https://arxiv.org/abs/2107.14795).

	## Evaluation results

	This model is able to achieve a top-1 accuracy of 72.7 on ImageNet-1k, despite having no privileged information about the 2D structure of images.

	### BibTeX entry and citation info

	```bibtex
	@article{DBLP:journals/corr/abs-2107-14795,
	author = {Andrew Jaegle and
	Sebastian Borgeaud and
	Jean{-}Baptiste Alayrac and
	Carl Doersch and
	Catalin Ionescu and
	David Ding and
	Skanda Koppula and
	Daniel Zoran and
	Andrew Brock and
	Evan Shelhamer and
	Olivier J. H{\'{e}}naff and
	Matthew M. Botvinick and
	Andrew Zisserman and
	Oriol Vinyals and
	Jo{\~{a}}o Carreira},
	title = {Perceiver {IO:} {A} General Architecture for Structured Inputs {\&}
	Outputs},
	journal = {CoRR},
	volume = {abs/2107.14795},
	year = {2021},
	url = {https://arxiv.org/abs/2107.14795},
	eprinttype = {arXiv},
	eprint = {2107.14795},
	timestamp = {Tue, 03 Aug 2021 14:53:34 +0200},
	biburl = {https://dblp.org/rec/journals/corr/abs-2107-14795.bib},
	bibsource = {dblp computer science bibliography, https://dblp.org}
	}
	```