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README.md | 1 year ago | |
knet-s3_r50-d8_deeplabv3_8xb2-adamw-80k_ade20k-512x512.py | 1 year ago | |
knet-s3_r50-d8_fcn_8xb2-adamw-80k_ade20k-512x512.py | 1 year ago | |
knet-s3_r50-d8_pspnet_8xb2-adamw-80k_ade20k-512x512.py | 1 year ago | |
knet-s3_r50-d8_upernet_8xb2-adamw-80k_ade20k-512x512.py | 1 year ago | |
knet-s3_swin-l_upernet_8xb2-adamw-80k_ade20k-512x512.py | 1 year ago | |
knet-s3_swin-l_upernet_8xb2-adamw-80k_ade20k-640x640.py | 1 year ago | |
knet-s3_swin-t_upernet_8xb2-adamw-80k_ade20k-512x512.py | 1 year ago | |
metafile.yaml | 1 year ago |
Semantic, instance, and panoptic segmentations have been addressed using different and specialized frameworks despite their underlying connections. This paper presents a unified, simple, and effective framework for these essentially similar tasks. The framework, named K-Net, segments both instances and semantic categories consistently by a group of learnable kernels, where each kernel is responsible for generating a mask for either a potential instance or a stuff class. To remedy the difficulties of distinguishing various instances, we propose a kernel update strategy that enables each kernel dynamic and conditional on its meaningful group in the input image. K-Net can be trained in an end-to-end manner with bipartite matching, and its training and inference are naturally NMS-free and box-free. Without bells and whistles, K-Net surpasses all previous published state-of-the-art single-model results of panoptic segmentation on MS COCO test-dev split and semantic segmentation on ADE20K val split with 55.2% PQ and 54.3% mIoU, respectively. Its instance segmentation performance is also on par with Cascade Mask R-CNN on MS COCO with 60%-90% faster inference speeds. Code and models will be released at this https URL.
Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | Device | mIoU | mIoU(ms+flip) | config | download |
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KNet + FCN | R-50-D8 | 512x512 | 80000 | 7.01 | 19.24 | V100 | 43.60 | 45.12 | config | model | log |
KNet + PSPNet | R-50-D8 | 512x512 | 80000 | 6.98 | 20.04 | V100 | 44.18 | 45.58 | config | model | log |
KNet + DeepLabV3 | R-50-D8 | 512x512 | 80000 | 7.42 | 12.10 | V100 | 45.06 | 46.11 | config | model | log |
KNet + UperNet | R-50-D8 | 512x512 | 80000 | 7.34 | 17.11 | V100 | 43.45 | 44.07 | config | model | log |
KNet + UperNet | Swin-T | 512x512 | 80000 | 7.57 | 15.56 | V100 | 45.84 | 46.27 | config | model | log |
KNet + UperNet | Swin-L | 512x512 | 80000 | 13.5 | 8.29 | V100 | 52.05 | 53.24 | config | model | log |
KNet + UperNet | Swin-L | 640x640 | 80000 | 13.54 | 8.29 | V100 | 52.21 | 53.34 | config | model | log |
Note:
@inproceedings{zhang2021knet,
title={{K-Net: Towards} Unified Image Segmentation},
author={Wenwei Zhang and Jiangmiao Pang and Kai Chen and Chen Change Loy},
year={2021},
booktitle={NeurIPS},
}
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