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transformers/docs/source/en/model_doc/efficientformer.md

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+*This model was released on 2022-06-02 and added to Hugging Face Transformers on 2023-06-20.*
+
+# EfficientFormer
+
+<div class="flex flex-wrap space-x-1">
+<img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-DE3412?style=flat&logo=pytorch&logoColor=white">
+</div>
+
+<Tip warning={true}>
+
+This model is in maintenance mode only, we don't accept any new PRs changing its code.
+If you run into any issues running this model, please reinstall the last version that supported this model: v4.40.2.
+You can do so by running the following command: `pip install -U transformers==4.40.2`.
+
+</Tip>
+
+## Overview
+
+The EfficientFormer model was proposed in [EfficientFormer: Vision Transformers at MobileNet Speed](https://huggingface.co/papers/2206.01191)
+by Yanyu Li, Geng Yuan, Yang Wen, Eric Hu, Georgios Evangelidis, Sergey Tulyakov, Yanzhi Wang, Jian Ren.  EfficientFormer proposes a
+dimension-consistent pure transformer that can be run on mobile devices for dense prediction tasks like image classification, object
+detection and semantic segmentation.
+
+The abstract from the paper is the following:
+
+*Vision Transformers (ViT) have shown rapid progress in computer vision tasks, achieving promising results on various benchmarks.
+However, due to the massive number of parameters and model design, e.g., attention mechanism, ViT-based models are generally
+times slower than lightweight convolutional networks. Therefore, the deployment of ViT for real-time applications is particularly
+challenging, especially on resource-constrained hardware such as mobile devices. Recent efforts try to reduce the computation
+complexity of ViT through network architecture search or hybrid design with MobileNet block, yet the inference speed is still
+unsatisfactory. This leads to an important question: can transformers run as fast as MobileNet while obtaining high performance?
+To answer this, we first revisit the network architecture and operators used in ViT-based models and identify inefficient designs.
+Then we introduce a dimension-consistent pure transformer (without MobileNet blocks) as a design paradigm.
+Finally, we perform latency-driven slimming to get a series of final models dubbed EfficientFormer.
+Extensive experiments show the superiority of EfficientFormer in performance and speed on mobile devices.
+Our fastest model, EfficientFormer-L1, achieves 79.2% top-1 accuracy on ImageNet-1K with only 1.6 ms inference latency on
+iPhone 12 (compiled with CoreML), which { runs as fast as MobileNetV2×1.4 (1.6 ms, 74.7% top-1),} and our largest model,
+EfficientFormer-L7, obtains 83.3% accuracy with only 7.0 ms latency. Our work proves that properly designed transformers can
+reach extremely low latency on mobile devices while maintaining high performance.*
+
+This model was contributed by [novice03](https://huggingface.co/novice03) and [Bearnardd](https://huggingface.co/Bearnardd).
+The original code can be found [here](https://github.com/snap-research/EfficientFormer).
+
+## Documentation resources
+
+- [Image classification task guide](../tasks/image_classification)
+
+## EfficientFormerConfig
+
+[[autodoc]] EfficientFormerConfig
+
+## EfficientFormerImageProcessor
+
+[[autodoc]] EfficientFormerImageProcessor
+    - preprocess
+
+## EfficientFormerModel
+
+[[autodoc]] EfficientFormerModel
+    - forward
+
+## EfficientFormerForImageClassification
+
+[[autodoc]] EfficientFormerForImageClassification
+    - forward
+
+## EfficientFormerForImageClassificationWithTeacher
+
+[[autodoc]] EfficientFormerForImageClassificationWithTeacher
+    - forward