基于机器学习的宫颈癌风险智能诊断.zip

# High-resolution networks (HRNets) for object detection ## Introduction ``` @inproceedings{SunXLW19, title={Deep High-Resolution Representation Learning for Human Pose Estimation}, author={Ke Sun and Bin Xiao and Dong Liu and Jingdong Wang}, booktitle={CVPR}, year={2019} } @article{SunZJCXLMWLW19, title={High-Resolution Representations for Labeling Pixels and Regions}, author={Ke Sun and Yang Zhao and Borui Jiang and Tianheng Cheng and Bin Xiao and Dong Liu and Yadong Mu and Xinggang Wang and Wenyu Liu and Jingdong Wang}, journal = {CoRR}, volume = {abs/1904.04514}, year={2019} } ``` ## Results and Models ### Faster R-CNN | Backbone | Style | Lr schd | box AP | Download | | :-------------: | :-----: | :-----: | :----: | :-----------------: | | HRNetV2p-W18 | pytorch | 1x | 36.1 | [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/hrnet/faster_rcnn_hrnetv2p_w18_1x_20190522-e368c387.pth) | | HRNetV2p-W18 | pytorch | 2x | 38.3 | [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/hrnet/faster_rcnn_hrnetv2p_w18_2x_20190810-9c8615d5.pth) | | HRNetV2p-W32 | pytorch | 1x | 39.5 | [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/hrnet/faster_rcnn_hrnetv2p_w32_1x_20190522-d22f1fef.pth) | | HRNetV2p-W32 | pytorch | 2x | 40.6 | [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/hrnet/faster_rcnn_hrnetv2p_w32_2x_20190810-24e8912a.pth) | | HRNetV2p-W48 | pytorch | 1x | 40.9 | [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/hrnet/faster_rcnn_hrnetv2p_w48_1x_20190820-5c6d0903.pth) | | HRNetV2p-W48 | pytorch | 2x | 41.5 | [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/hrnet/faster_rcnn_hrnetv2p_w48_2x_20190820-79fb8bfc.pth) | ### Mask R-CNN | Backbone | Style | Lr schd | box AP | mask AP | Download | | :-------------: | :-----: | :-----: | :----: | :----: | :-----------------: | | HRNetV2p-W18 | pytorch | 1x | 37.3 | 34.2 | [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/hrnet/mask_rcnn_hrnetv2p_w18_1x_20190522-c8ad459f.pth) | | HRNetV2p-W18 | pytorch | 2x | 39.2 | 35.7 | [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/hrnet/mask_rcnn_hrnetv2p_w18_2x_20190810-1e4747eb.pth) | | HRNetV2p-W32 | pytorch | 1x | 40.7 | 36.8 | [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/hrnet/mask_rcnn_hrnetv2p_w32_1x_20190522-374aaa00.pth) | | HRNetV2p-W32 | pytorch | 2x | 41.7 | 37.5 | [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/hrnet/mask_rcnn_hrnetv2p_w32_2x_20190810-773eca75.pth) | | HRNetV2p-W48 | pytorch | 1x | 42.4 | 38.1 | [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/hrnet/mask_rcnn_hrnetv2p_w48_1x_20190820-0923d1ad.pth) | | HRNetV2p-W48 | pytorch | 2x | 42.9 | 38.3 | [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/hrnet/mask_rcnn_hrnetv2p_w48_2x_20190820-70df51b2.pth) | ### Cascade R-CNN | Backbone | Style | Lr schd | box AP | Download | | :-------------: | :-----: | :-----: | :----: | :-----------------: | | HRNetV2p-W18 | pytorch | 20e | 41.2 | [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/hrnet/cascade_rcnn_hrnetv2p_w18_20e_20190810-132012d0.pth)| | HRNetV2p-W32 | pytorch | 20e | 43.7 | [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/hrnet/cascade_rcnn_hrnetv2p_w32_20e_20190522-55bec4ee.pth)| | HRNetV2p-W48 | pytorch | 20e | 44.6 | [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/hrnet/cascade_rcnn_hrnetv2p_w48_20e_20190810-f40ed8e1.pth)| ### Cascade Mask R-CNN | Backbone | Style | Lr schd | box AP | mask AP | Download | | :-------------: | :-----: | :-----: | :----: | :----: | :-----------------: | | HRNetV2p-W18 | pytorch | 20e | 41.9 | 36.4 | [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/hrnet/cascade_mask_rcnn_hrnetv2p_w18_20e_20190810-054fb7bf.pth) | | HRNetV2p-W32 | pytorch | 20e | 44.5 | 38.5 | [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/hrnet/cascade_mask_rcnn_hrnetv2p_w32_20e_20190810-76f61cd0.pth) | | HRNetV2p-W48 | pytorch | 20e | 46.0 | 39.5 | [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/hrnet/cascade_mask_rcnn_hrnetv2p_w48_20e_20190810-d04a1415.pth) | ### Hybrid Task Cascade (HTC) | Backbone | Style | Lr schd | box AP | mask AP | Download | | :-------------: | :-----: | :-----: | :----: | :----: | :-----------------: | | HRNetV2p-W18 | pytorch | 20e | 43.1 | 37.9 | [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/hrnet/htc_hrnetv2p_w18_20e_20190810-d70072af.pth) | | HRNetV2p-W32 | pytorch | 20e | 45.3 | 39.6 | [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/hrnet/htc_hrnetv2p_w32_20e_20190810-82f9ef5a.pth) | | HRNetV2p-W48 | pytorch | 20e | 46.8 | 40.7 | [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/hrnet/htc_hrnetv2p_w48_20e_20190810-f6d2c3fd.pth) | | HRNetV2p-W48 | pytorch | 28e | 47.0 | 41.0 | [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/hrnet/htc_hrnetv2p_w48_28e_20190810-a4274b38.pth) | | X-101-64x4d-FPN | pytorch | 28e | 46.8 | 40.7 | [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/hrnet/htc_x101_64x4d_28e_20190810-d7c19dc0.pth) | ### FCOS | Backbone | Style | GN | MS train | Lr schd | box AP | Download | |:---------:|:-------:|:-------:|:--------:|:-------:|:------:|:--------:| |HRNetV2p-W18| pytorch | Y | N | 1x | 35.2 | [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/hrnet/fcos_hrnetv2p_w18_1x_20190810-87a17998.pth) | |HRNetV2p-W18| pytorch | Y | N | 2x | 38.2 | [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/hrnet/fcos_hrnetv2p_w18_2x_20190810-dfd60a7b.pth) | |HRNetV2p-W32| pytorch | Y | N | 1x | 37.7 | [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/hrnet/fcos_hrnetv2p_w32_1x_20190810-62014622.pth) | |HRNetV2p-W32| pytorch | Y | N | 2x | 40.3 | [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/hrnet/fcos_hrnetv2p_w32_2x_20190810-8e987ec1.pth) | |HRNetV2p-W18| pytorch | Y | Y | 2x | 38.1 | [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/hrnet/fcos_hrnetv2p_w18_mstrain_2x_20190810-eb846b2c.pth) | |HRNetV2p-W32| pytorch | Y | Y | 2x | 41.4 | [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/hrnet/fcos_hrnetv2p_w32_mstrain_2x_20190810-96127bf8.pth) | |HRNetV2p-W48| pytorch | Y | Y | 2x | 42.9 | [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/hrnet/fcos_hrnetv2p_w48_mstrain_2x_20190810-f7dc8801.pth) | **Note:** - The `28e` schedule in HTC indicates decreasing the lr at 24 and 27 epochs, with a total of 28 epochs. - HRNetV2 ImageNet pretrained models are in [HRNets for Image Classification](https://github.com/HRNet/HRNet-Image-Classification).