| --- |
| license: apache-2.0 |
| datasets: |
| - Chris1/cityscapes |
| language: |
| - en |
| metrics: |
| - mean_iou |
| pipeline_tag: image-segmentation |
| tags: |
| - Image Segmentation |
| - Semantic Segmentation |
| - Computer Vision |
| - Cityscapes |
| - HRNet |
| - ONNX |
| - Int8 quantization |
| - RyzenAI |
| --- |
| |
| # HRNet model trained on Cityscapes |
|
|
| HRNet trained on Cityscapes dataset at resolution 512x1024 for semantic segmentation on images. |
| 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. |
| The code version we use from [this repository](https://github.com/HRNet/HRNet-Semantic-Segmentation). |
|
|
| We develop a modified version that could be supported by [AMD Ryzen AI](https://ryzenai.docs.amd.com/en/latest/inst.html). |
|
|
|
|
| ## Model description |
|
|
| 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. |
|
|
|
|
| ## Intended uses & limitations |
|
|
| 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. |
|
|
|
|
| ## How to use |
|
|
| ### Installation |
|
|
| Follow [Ryzen AI Installation](https://ryzenai.docs.amd.com/en/latest/inst.html) to prepare the environment for Ryzen AI. |
| Run the following script to install pre-requisites for this model. |
| ```bash |
| pip install -r requirements.txt |
| ``` |
|
|
|
|
| ### Data Preparation (optional: for accuracy evaluation) |
| 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). |
| 2. Organise the dataset directory as follows: |
| ```Shell |
| ./data/cityscapes/ |
| gtFine |
| leftImg8bit |
| train.lst |
| val.lst |
| test.lst |
| ``` |
|
|
| ### Test & Evaluation |
| - Run inference on a single image |
| ```python |
| 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 |
| #return segmentaion logits and can visualize the result. |
| ``` |
| *Note: __vaip_config.json__ is located at the setup package of Ryzen AI (refer to [Installation](#installation))* |
|
|
| - Test accuracy of the quantized model on Cityscapes. |
| ```Shell |
| python hrnet_quantized_onnx_eval.py -m .\HighResolutionNet_int.onnx -r .\data\cityscapes -l .\val.lst --ipu --provider_config .\vaip_config.json |
| ``` |
|
|
| ### Performance |
| | Model | miou| |
| |:-|:-:| |
| | HRNet_int8_onnx_model (512x1024) | 72.31% | |
| |
| |
| ```bibtex |
| @article{YuanCW19, |
| title={Object-Contextual Representations for Semantic Segmentation}, |
| author={Yuhui Yuan and Xilin Chen and Jingdong Wang}, |
| booktitle={ECCV}, |
| year={2020} |
| } |
| ``` |
| |
| |
