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Jerry Jiarui XU ae3b3c3ffc | 3 years ago | |
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.. | ||
README.md | 3 years ago | |
htc_r50_fpn_1x_coco.py | 3 years ago | |
htc_r50_fpn_20e_coco.py | 4 years ago | |
htc_r101_fpn_20e_coco.py | 4 years ago | |
htc_without_semantic_r50_fpn_1x_coco.py | 3 years ago | |
htc_x101_32x4d_fpn_16x1_20e_coco.py | 4 years ago | |
htc_x101_64x4d_fpn_16x1_20e_coco.py | 4 years ago | |
htc_x101_64x4d_fpn_dconv_c3-c5_mstrain_400_1400_16x1_20e_coco.py | 3 years ago |
We provide config files to reproduce the results in the CVPR 2019 paper for Hybrid Task Cascade.
@inproceedings{chen2019hybrid,
title={Hybrid task cascade for instance segmentation},
author={Chen, Kai and Pang, Jiangmiao and Wang, Jiaqi and Xiong, Yu and Li, Xiaoxiao and Sun, Shuyang and Feng, Wansen and Liu, Ziwei and Shi, Jianping and Ouyang, Wanli and Chen Change Loy and Dahua Lin},
booktitle={IEEE Conference on Computer Vision and Pattern Recognition},
year={2019}
}
HTC requires COCO and COCO-stuff dataset for training. You need to download and extract it in the COCO dataset path.
The directory should be like this.
mmdetection
├── mmdet
├── tools
├── configs
├── data
│ ├── coco
│ │ ├── annotations
│ │ ├── train2017
│ │ ├── val2017
│ │ ├── test2017
| | ├── stuffthingmaps
The results on COCO 2017val are shown in the below table. (results on test-dev are usually slightly higher than val)
Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Download |
---|---|---|---|---|---|---|---|
R-50-FPN | pytorch | 1x | 8.2 | 5.8 | 42.3 | 37.4 | model | log |
R-50-FPN | pytorch | 20e | 8.2 | - | 43.3 | 38.3 | model | log |
R-101-FPN | pytorch | 20e | 10.2 | 5.5 | 44.8 | 39.6 | model | log |
X-101-32x4d-FPN | pytorch | 20e | 11.4 | 5.0 | 46.1 | 40.5 | model | log |
X-101-64x4d-FPN | pytorch | 20e | 14.5 | 4.4 | 47.0 | 41.4 | model | log |
score_thr
is set to 0.001 for both baselines and HTC.We also provide a powerful HTC with DCN and multi-scale training model. No testing augmentation is used.
Backbone | Style | DCN | training scales | Lr schd | box AP | mask AP | Download |
---|---|---|---|---|---|---|---|
X-101-64x4d-FPN | pytorch | c3-c5 | 400~1400 | 20e | 50.4 | 43.8 | model | log |
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