| | --- |
| | 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} |
| | } |
| | ``` |
| | |
| | |
