d966f98f83
* add icnet backbone * add icnet head * add icnet configs * nclass -> num_classes * Support ICNet * ICNet * ICNet * Add ICNeck * Add ICNeck * Add ICNeck * Add ICNeck * Adding unittest * Uploading models & logs * Uploading models & logs * add comment * smaller test_swin.py * try to delete test_swin.py * delete test_unet.py * delete test_unet.py * temp * smaller test_unet.py Co-authored-by: Junjun2016 <hejunjun@sjtu.edu.cn> |
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| .. | ||
| README.md | ||
| icnet.yml | ||
| icnet_r18-d8_832x832_80k_cityscapes.py | ||
| icnet_r18-d8_832x832_160k_cityscapes.py | ||
| icnet_r18-d8_in1k-pre_832x832_80k_cityscapes.py | ||
| icnet_r18-d8_in1k-pre_832x832_160k_cityscapes.py | ||
| icnet_r50-d8_832x832_80k_cityscapes.py | ||
| icnet_r50-d8_832x832_160k_cityscapes.py | ||
| icnet_r50-d8_in1k-pre_832x832_80k_cityscapes.py | ||
| icnet_r50-d8_in1k-pre_832x832_160k_cityscapes.py | ||
| icnet_r101-d8_832x832_80k_cityscapes.py | ||
| icnet_r101-d8_832x832_160k_cityscapes.py | ||
| icnet_r101-d8_in1k-pre_832x832_80k_cityscapes.py | ||
| icnet_r101-d8_in1k-pre_832x832_160k_cityscapes.py | ||
README.md
ICNet for Real-time Semantic Segmentation on High-resolution Images
Introduction
ICNet (ECCV'2018)
@inproceedings{zhao2018icnet,
title={Icnet for real-time semantic segmentation on high-resolution images},
author={Zhao, Hengshuang and Qi, Xiaojuan and Shen, Xiaoyong and Shi, Jianping and Jia, Jiaya},
booktitle={Proceedings of the European conference on computer vision (ECCV)},
pages={405--420},
year={2018}
}
Results and models
Cityscapes
| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download |
|---|---|---|---|---|---|---|---|---|---|
| ICNet | R-18-D8 | 832x832 | 80000 | 1.70 | 27.12 | 68.14 | 70.16 | config | model | log |
| ICNet | R-18-D8 | 832x832 | 160000 | - | - | 71.64 | 74.18 | config | model | log |
| ICNet (in1k-pre) | R-18-D8 | 832x832 | 80000 | - | - | 72.51 | 74.78 | config | model | log |
| ICNet (in1k-pre) | R-18-D8 | 832x832 | 160000 | - | - | 74.43 | 76.72 | config | model | log |
| ICNet | R-50-D8 | 832x832 | 80000 | 2.53 | 20.08 | 68.91 | 69.72 | config | model | log |
| ICNet | R-50-D8 | 832x832 | 160000 | - | - | 73.82 | 75.67 | config | model | log |
| ICNet (in1k-pre) | R-50-D8 | 832x832 | 80000 | - | - | 74.58 | 76.41 | config | model | log |
| ICNet (in1k-pre) | R-50-D8 | 832x832 | 160000 | - | - | 76.29 | 78.09 | config | model | log |
| ICNet | R-101-D8 | 832x832 | 80000 | 3.08 | 16.95 | 70.28 | 71.95 | config | model | log |
| ICNet | R-101-D8 | 832x832 | 160000 | - | - | 73.80 | 76.10 | config | model | log |
| ICNet (in1k-pre) | R-101-D8 | 832x832 | 80000 | - | - | 75.57 | 77.86 | config | model | log |
| ICNet (in1k-pre) | R-101-D8 | 832x832 | 160000 | - | - | 76.15 | 77.98 | config | model | log |
Note: in1k-pre means pretrained model is used.