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Code for our CVPR2020 paper "Strip Pooling: Rethinking Spatial Pooling for Scene Parsing"
| Date | Stars |
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| 2026-07-24 | 415 |
| 2026-07-25 | 415 |
| 2026-07-28 | 415 |
| 2026-07-30 | 415 |
| 2026-08-06 | 415 |
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# Strip Pooling: Rethinking Spatial Pooling for Scene Parsing This repository is a PyTorch implementation for our [CVPR2020 paper](https://arxiv.org/pdf/2003.13328.pdf) (non-commercial use only). The results reported in our paper are originally based on [PyTorch-Encoding](https://github.com/zhanghang1989/PyTorch-Encoding) but the environment settings are a little bit complicated. To ease use, we reimplement our work based on [semseg](https://github.com/hszhao/semseg). ### Strip Pooling  ### Usage Before training your own models, we recommend you to refer to the instructions described [here](https://github.com/hszhao/semseg). Then, you need to update the dataset paths in the configuration files. Four GPUs with at least 11G memory on each are required for synchronized training. [PyTorch](https://pytorch.org/get-started/locally/) (>=1.0.1) and [Apex](https://github.com/NVIDIA/apex) are required for Sync-BN support. For apex, just follow the "Quick Start" part to install it. For pretrained models, you can download them from here ([resnet50](https://hangzh.s3.amazonaws.com/encoding/models/resnet50-25c4b509.zip) and [resnet101](https://hangzh.s3.amazonaws.com/encoding/models/resnet101-2a57e44d.zip)) or my [google drive](https://drive.google.com/open?id=1jrm93o5ULjuOHaRQVakVF-e8MC8IAn1X). Then, create a new folder "pretrained" and put the pretrained models in it, like ``` mkdir pretrained mv downloaded_pretrained_model ./pretrained/ ``` For training, just run ``` sh tool/train.py dataset_name model_name ``` For instance, in our case, you can run ``` sh tool/train.py ade20k spnet50 ``` For test, ``` sh tool/test.py dataset_name model_name ``` At present, multi-GPU test is not supported. Will implement it later. #### We believe designing more complicated strip pooling module also benefits to the model performance. ### Contact If you are interested in this work and want to further investigate the techniques of pooling, you are welcome to contact me via [email protected]. ### Citation You may want to cite: ``` @inproceedings{hou2020strip, title={{Strip Pooling}: Rethinking Spatial Pooling for Scene Parsing}, author={Hou, Qibin and Zhang, Li and Cheng, Ming-Ming and Feng, Jiashi}, booktitle={CVPR}, year={2020} } @misc{semseg2019, author={Zhao, Hengshuang}, title={semseg}, howpublished={\url{https://github.com/hszhao/semseg}}, year={2019} } ```
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Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:2c8d874075d97d28, topic:computer-vision