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--- |
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license: apache-2.0 |
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datasets: |
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- Chris1/cityscapes |
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language: |
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- en |
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metrics: |
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- mean_iou |
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pipeline_tag: image-segmentation |
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tags: |
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- Image Segmentation |
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- Semantic Segmentation |
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- Computer Vision |
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- Cityscapes |
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- HRNet |
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- ONNX |
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- Int8 quantization |
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- RyzenAI |
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--- |
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# HRNet model trained on Cityscapes |
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HRNet trained on Cityscapes dataset at resolution 512x1024 for semantic segmentation on images. |
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It was introduced in the paper [Segmentation Transformer: Object-Contextual Representations for Semantic Segmentation](https://arxiv.org/pdf/1909.11065.pdf) by Yuhui Yuan et al. |
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The code version we use from [this repository](https://github.com/HRNet/HRNet-Semantic-Segmentation). |
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We develop a modified version that could be supported by [AMD Ryzen AI](https://ryzenai.docs.amd.com/en/latest/inst.html). |
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## Model description |
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HRNet is an advanced algorithm used for image segmentation. It is based on deep learning techniques and is capable of providing accurate semantic segmentation in images. |
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## Intended uses & limitations |
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You can use the raw model for semantic segmentation. See the [model hub](https://huggingface.co./models?sort=trending&search=amd%2Fhrnet) to look for all available HRNet models. |
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## How to use |
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### Installation |
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Follow [Ryzen AI Installation](https://ryzenai.docs.amd.com/en/latest/inst.html) to prepare the environment for Ryzen AI. |
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Run the following script to install pre-requisites for this model. |
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```bash |
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pip install -r requirements.txt |
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``` |
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### Data Preparation (optional: for accuracy evaluation) |
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1. Download the [Cityscapes](https://www.cityscapes-dataset.com/) dataset, which includes images and annotations. Download gtFine_trainvaltest.zip (241MB) and leftImg8bit_trainvaltest.zip (11GB). |
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2. Organise the dataset directory as follows: |
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```Shell |
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./data/cityscapes/ |
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gtFine |
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leftImg8bit |
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train.lst |
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val.lst |
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test.lst |
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``` |
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### Test & Evaluation |
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- Run inference on a single image |
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```python |
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python hrnet_quantized_onnx_inference.py -m HighResolutionNet_int.onnx -idir PATH_TO_IMAGES(like .\data\cityscapes\leftImg8bit\val\frankfurt) --ipu --provider_config Path\To\vaip_config.json |
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#return segmentaion logits and can visualize the result. |
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``` |
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*Note: __vaip_config.json__ is located at the setup package of Ryzen AI (refer to [Installation](#installation))* |
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- Test accuracy of the quantized model on Cityscapes. |
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```Shell |
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python hrnet_quantized_onnx_eval.py -m .\HighResolutionNet_int.onnx -r .\data\cityscapes -l .\val.lst --ipu --provider_config .\vaip_config.json |
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``` |
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### Performance |
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| Model | miou| |
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|:-|:-:| |
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| HRNet_int8_onnx_model (512x1024) | 72.31% | |
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```bibtex |
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@article{YuanCW19, |
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title={Object-Contextual Representations for Semantic Segmentation}, |
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author={Yuhui Yuan and Xilin Chen and Jingdong Wang}, |
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booktitle={ECCV}, |
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year={2020} |
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} |
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``` |
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