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* First draft * More improvements * Add convnext backbone * Add conversion script * Add more improvements * Comment out to_dict * Add to_dict method * Add default config * Fix config * Fix backbone * Fix backbone some more * Add docs, auto mapping, tests * Fix some tests * Fix more tests * Fix more tests * Add conversion script * Improve conversion script * Add support for getting reshaped undownsampled hidden states * Fix forward pass * Add print statements * Comment out set_shift_and_window_size * More improvements * Correct downsampling layers conversion * Fix style * First draft * Fix conversion script * Remove config attribute * Fix more tests * Update READMEs * Update ConvNextBackbone * Fix ConvNext tests * Align ConvNext with Swin * Remove files * Fix index * Improve docs * Add output_attentions to model forward * Add backbone mixin, improve tests * More improvements * Update init_weights * Fix interpolation of logits * Add UperNetImageProcessor * Improve image processor * Fix image processor * Remove print statements * Remove script * Update import * Add image processor tests * Remove print statements * Fix test * Add integration test * Add convnext integration test * Update docstring * Fix README * Simplify config * Apply suggestions * Improve docs * Rename class * Fix test_initialization * Fix import * Address review * Fix confg * Convert all checkpoints * Fix default backbone * Usage same processor as segformer * Apply suggestions * Fix init_weights, update conversion scripts * Improve config * Use Auto API instead of creating a new image processor * Fix docs * Add doctests * Remove ResNetConfig dependency * Add always_partition argument * Fix rebaseé * Improve docs * Convert checkpoints Co-authored-by: Niels Rogge <nielsrogge@Nielss-MacBook-Pro.local> Co-authored-by: Niels Rogge <nielsrogge@Nielss-MBP.localdomain>
65 lines
3.2 KiB
Plaintext
65 lines
3.2 KiB
Plaintext
<!--Copyright 2022 The HuggingFace Team. All rights reserved.
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
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the License. You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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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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an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
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specific language governing permissions and limitations under the License.
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# UPerNet
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## Overview
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The UPerNet model was proposed in [Unified Perceptual Parsing for Scene Understanding](https://arxiv.org/abs/1807.10221)
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by Tete Xiao, Yingcheng Liu, Bolei Zhou, Yuning Jiang, Jian Sun. UPerNet is a general framework to effectively segment
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a wide range of concepts from images, leveraging any vision backbone like [ConvNeXt](convnext) or [Swin](swin).
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The abstract from the paper is the following:
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*Humans recognize the visual world at multiple levels: we effortlessly categorize scenes and detect objects inside, while also identifying the textures and surfaces of the objects along with their different compositional parts. In this paper, we study a new task called Unified Perceptual Parsing, which requires the machine vision systems to recognize as many visual concepts as possible from a given image. A multi-task framework called UPerNet and a training strategy are developed to learn from heterogeneous image annotations. We benchmark our framework on Unified Perceptual Parsing and show that it is able to effectively segment a wide range of concepts from images. The trained networks are further applied to discover visual knowledge in natural scenes.*
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<img src="https://huggingface.co/datasets/huggingface/documentation-images/blob/main/transformers/model_doc/upernet_architecture.jpg"
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alt="drawing" width="600"/>
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<small> UPerNet framework. Taken from the <a href="https://arxiv.org/abs/1807.10221">original paper</a>. </small>
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This model was contributed by [nielsr](https://huggingface.co/nielsr). The original code is based on OpenMMLab's mmsegmentation [here](https://github.com/open-mmlab/mmsegmentation/blob/master/mmseg/models/decode_heads/uper_head.py).
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## Usage
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UPerNet is a general framework for semantic segmentation. It can be used with any vision backbone, like so:
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```py
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from transformers import SwinConfig, UperNetConfig, UperNetForSemanticSegmentation
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backbone_config = SwinConfig(out_features=["stage1", "stage2", "stage3", "stage4"])
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config = UperNetConfig(backbone_config=backbone_config)
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model = UperNetForSemanticSegmentation(config)
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```
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To use another vision backbone, like [ConvNeXt](convnext), simply instantiate the model with the appropriate backbone:
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```py
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from transformers import ConvNextConfig, UperNetConfig, UperNetForSemanticSegmentation
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backbone_config = ConvNextConfig(out_features=["stage1", "stage2", "stage3", "stage4"])
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config = UperNetConfig(backbone_config=backbone_config)
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model = UperNetForSemanticSegmentation(config)
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```
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Note that this will randomly initialize all the weights of the model.
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## UperNetConfig
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[[autodoc]] UperNetConfig
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## UperNetForSemanticSegmentation
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[[autodoc]] UperNetForSemanticSegmentation
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- forward |