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* AutoImageProcessor skeleton * Update references * Add mapping in init * Add model image processors to __init__ for importing * Add AutoImageProcessor tests * Fix up * Image Processor documentation * Remove pdb * Update docs/source/en/model_doc/mobilevit.mdx * Update docs * Don't add whitespace on json files * Remove fixtures * Move checking model config down * Fix up * Add check for image processor * Remove FeatureExtractorMixin in docstrings * Rename model_tmpfile to config_tmpfile * Don't make None if not in image processor map
66 lines
3.1 KiB
Plaintext
66 lines
3.1 KiB
Plaintext
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# DPT
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## Overview
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The DPT model was proposed in [Vision Transformers for Dense Prediction](https://arxiv.org/abs/2103.13413) by René Ranftl, Alexey Bochkovskiy, Vladlen Koltun.
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DPT is a model that leverages the [Vision Transformer (ViT)](vit) as backbone for dense prediction tasks like semantic segmentation and depth estimation.
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The abstract from the paper is the following:
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*We introduce dense vision transformers, an architecture that leverages vision transformers in place of convolutional networks as a backbone for dense prediction tasks. We assemble tokens from various stages of the vision transformer into image-like representations at various resolutions and progressively combine them into full-resolution predictions using a convolutional decoder. The transformer backbone processes representations at a constant and relatively high resolution and has a global receptive field at every stage. These properties allow the dense vision transformer to provide finer-grained and more globally coherent predictions when compared to fully-convolutional networks. Our experiments show that this architecture yields substantial improvements on dense prediction tasks, especially when a large amount of training data is available. For monocular depth estimation, we observe an improvement of up to 28% in relative performance when compared to a state-of-the-art fully-convolutional network. When applied to semantic segmentation, dense vision transformers set a new state of the art on ADE20K with 49.02% mIoU. We further show that the architecture can be fine-tuned on smaller datasets such as NYUv2, KITTI, and Pascal Context where it also sets the new state of the art.*
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<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/dpt_architecture.jpg"
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alt="drawing" width="600"/>
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<small> DPT architecture. Taken from the <a href="https://arxiv.org/abs/2103.13413" target="_blank">original paper</a>. </small>
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This model was contributed by [nielsr](https://huggingface.co/nielsr). The original code can be found [here](https://github.com/isl-org/DPT).
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## DPTConfig
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[[autodoc]] DPTConfig
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## DPTFeatureExtractor
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[[autodoc]] DPTFeatureExtractor
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- __call__
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- post_process_semantic_segmentation
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## DPTImageProcessor
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[[autodoc]] DPTImageProcessor
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- preprocess
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- post_process_semantic_segmentation
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## DPTModel
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[[autodoc]] DPTModel
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- forward
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## DPTForDepthEstimation
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[[autodoc]] DPTForDepthEstimation
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- forward
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## DPTForSemanticSegmentation
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[[autodoc]] DPTForSemanticSegmentation
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- forward
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