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* Add tf code for efficientformer * Fix return dict bug - return last hidden state after last stage * Fix corresponding return dict bug * Override test tol * Change default values of training to False * Set training to default False X3 * Rm axis from ln * Set init in dense projection * Rm debug stuff * Make style; all tests pass. * Modify year to 2023 * Fix attention biases codes * Update the shape list logic * Add a batch norm eps config * Remove extract comments in test files * Add conditional attn and hidden states return for serving output * Change channel dim checking logic * Add exception for withteacher model in training mode * Revert layer count for now * Add layer count for conditional layer naming * Transpose for conv happens only in main layer * Make tests smaller * Make style * Update doc * Rm from_pt * Change to actual expect image class label * Remove stray print in tests * Update image processor test * Remove the old serving output logic * Make style * Make style * Complete test
84 lines
3.9 KiB
Plaintext
84 lines
3.9 KiB
Plaintext
<!--Copyright 2022 The HuggingFace Team. All rights reserved.
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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# EfficientFormer
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## Overview
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The EfficientFormer model was proposed in [EfficientFormer: Vision Transformers at MobileNet Speed](https://arxiv.org/abs/2206.01191)
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by Yanyu Li, Geng Yuan, Yang Wen, Eric Hu, Georgios Evangelidis, Sergey Tulyakov, Yanzhi Wang, Jian Ren. EfficientFormer proposes a
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dimension-consistent pure transformer that can be run on mobile devices for dense prediction tasks like image classification, object
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detection and semantic segmentation.
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The abstract from the paper is the following:
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*Vision Transformers (ViT) have shown rapid progress in computer vision tasks, achieving promising results on various benchmarks.
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However, due to the massive number of parameters and model design, e.g., attention mechanism, ViT-based models are generally
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times slower than lightweight convolutional networks. Therefore, the deployment of ViT for real-time applications is particularly
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challenging, especially on resource-constrained hardware such as mobile devices. Recent efforts try to reduce the computation
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complexity of ViT through network architecture search or hybrid design with MobileNet block, yet the inference speed is still
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unsatisfactory. This leads to an important question: can transformers run as fast as MobileNet while obtaining high performance?
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To answer this, we first revisit the network architecture and operators used in ViT-based models and identify inefficient designs.
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Then we introduce a dimension-consistent pure transformer (without MobileNet blocks) as a design paradigm.
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Finally, we perform latency-driven slimming to get a series of final models dubbed EfficientFormer.
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Extensive experiments show the superiority of EfficientFormer in performance and speed on mobile devices.
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Our fastest model, EfficientFormer-L1, achieves 79.2% top-1 accuracy on ImageNet-1K with only 1.6 ms inference latency on
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iPhone 12 (compiled with CoreML), which { runs as fast as MobileNetV2×1.4 (1.6 ms, 74.7% top-1),} and our largest model,
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EfficientFormer-L7, obtains 83.3% accuracy with only 7.0 ms latency. Our work proves that properly designed transformers can
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reach extremely low latency on mobile devices while maintaining high performance.*
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This model was contributed by [novice03](https://huggingface.co/novice03) and [Bearnardd](https://huggingface.co/Bearnardd).
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The original code can be found [here](https://github.com/snap-research/EfficientFormer). The TensorFlow version of this model was added by [D-Roberts](https://huggingface.co/D-Roberts).
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## Documentation resources
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- [Image classification task guide](../tasks/image_classification)
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## EfficientFormerConfig
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[[autodoc]] EfficientFormerConfig
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## EfficientFormerImageProcessor
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[[autodoc]] EfficientFormerImageProcessor
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- preprocess
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## EfficientFormerModel
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[[autodoc]] EfficientFormerModel
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- forward
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## EfficientFormerForImageClassification
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[[autodoc]] EfficientFormerForImageClassification
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- forward
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## EfficientFormerForImageClassificationWithTeacher
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[[autodoc]] EfficientFormerForImageClassificationWithTeacher
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- forward
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## TFEfficientFormerModel
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[[autodoc]] TFEfficientFormerModel
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- call
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## TFEfficientFormerForImageClassification
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[[autodoc]] TFEfficientFormerForImageClassification
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- call
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## TFEfficientFormerForImageClassificationWithTeacher
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[[autodoc]] TFEfficientFormerForImageClassificationWithTeacher
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- call
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