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Add configs and benchmarks. (open-mmlab#2446)
* add configs for all models * fix some configs * Add benchmark models * update some benchmarks * Fix retina config * add more benchmarks * update MODEL_ZOO.md * fix cascade mask num_class bug * change caffe pretrained url * fix paramwise_cfg bug * fix paramwise cfg * restrict minimum h and w in crop_and_resize * add grad clip for fcos * add grad clip for ga retinanet * fix grad clip * fix img norm * fix rpn loss bbox * fix rpn proposal num * fix url * clean * update comparison * minor update * add grad clip for guided anchoring * fix some typos * add inference time details * clean Co-authored-by: ZwwWayne <[email protected]>
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‎configs/_base_/models/faster_rcnn_r50_caffe_c4.py

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norm_cfg = dict(type='BN', requires_grad=False)
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model = dict(
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type='FasterRCNN',
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pretrained='open-mmlab://resnet50_caffe',
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pretrained='open-mmlab://resnet50_caffe_bgr',
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backbone=dict(
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type='ResNet',
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depth=50,

‎configs/_base_/models/faster_rcnn_r50_fpn.py

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pos_fraction=0.5,
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neg_pos_ub=-1,
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add_gt_as_proposals=False),
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allowed_border=0,
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allowed_border=-1,
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pos_weight=-1,
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debug=False),
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rpn_proposal=dict(

‎configs/_base_/models/mask_rcnn_r50_caffe_c4.py

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norm_cfg = dict(type='BN', requires_grad=False)
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model = dict(
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type='MaskRCNN',
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pretrained='open-mmlab://resnet50_caffe',
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pretrained='open-mmlab://resnet50_caffe_bgr',
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backbone=dict(
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type='ResNet',
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depth=50,

‎configs/_base_/models/mask_rcnn_r50_fpn.py

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pos_fraction=0.5,
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neg_pos_ub=-1,
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add_gt_as_proposals=False),
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allowed_border=0,
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allowed_border=-1,
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pos_weight=-1,
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debug=False),
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rpn_proposal=dict(

‎configs/_base_/models/retinanet_r50_fpn.py

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gamma=2.0,
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alpha=0.25,
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loss_weight=1.0),
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loss_bbox=dict(type='SmoothL1Loss', beta=0.11, loss_weight=1.0)))
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loss_bbox=dict(type='L1Loss', loss_weight=1.0)))
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# training and testing settings
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train_cfg = dict(
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assigner=dict(

‎configs/_base_/models/rpn_r50_caffe_c4.py

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# model settings
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model = dict(
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type='RPN',
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pretrained='open-mmlab://resnet50_caffe',
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pretrained='open-mmlab://resnet50_caffe_bgr',
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backbone=dict(
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type='ResNet',
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depth=50,
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target_stds=[1.0, 1.0, 1.0, 1.0]),
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loss_cls=dict(
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type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
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loss_bbox=dict(type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.0)))
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loss_bbox=dict(type='L1Loss', loss_weight=1.0)))
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# model training and testing settings
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train_cfg = dict(
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rpn=dict(

‎configs/_base_/models/rpn_r50_fpn.py

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target_stds=[1.0, 1.0, 1.0, 1.0]),
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loss_cls=dict(
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type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
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loss_bbox=dict(type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.0)))
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loss_bbox=dict(type='L1Loss', loss_weight=1.0)))
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# model training and testing settings
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train_cfg = dict(
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rpn=dict(
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rpn=dict(
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nms_across_levels=False,
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nms_pre=2000,
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nms_post=2000,
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max_num=2000,
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nms_post=1000,
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max_num=1000,
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nms_thr=0.7,
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min_bbox_size=0))

‎configs/albu_example/README.md

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## Results and Models
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| Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Download |
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|:---------:|:-------:|:-------:|:--------:|:--------------:|:------:|:-------:|:--------:|
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| R-50 | pytorch | 1x | 4.4 | 11.6 | 38.0 | 34.5 |[model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/v2.0/albu_example/mask_rcnn_r50_fpn_albu_1x_coco/mask_rcnn_r50_fpn_albu_1x_coco_20200208-ab203bcd.pth) &#124; [log](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/v2.0/albu_example/mask_rcnn_r50_fpn_albu_1x_coco/mask_rcnn_r50_fpn_albu_1x_coco_20200208_225520.log.json) |

‎configs/atss/README.md

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## Results and Models
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| Backbone | Style | Lr schd | Mem (GB) | Train time (s/iter) | Inf time (fps) | box AP | Download |
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|:---------:|:-------:|:-------:|:--------:|:-------------------:|:--------------:|:------:|:--------:|
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| R-50 | pytorch | 1x | 3.6 | 0.357 | 12.8 | 39.2 | [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/atss/atss_r50_fpn_1x_20200113-a7aa251e.pth)|
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| Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | Download |
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|:---------:|:-------:|:-------:|:--------:|:--------------:|:------:|:--------:|
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| R-50 | pytorch | 1x | 3.7 | 16.9 | 39.4 | [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/v2.0/atss/atss_r50_fpn_1x_coco/atss_r50_fpn_1x_coco_20200209-985f7bd0.pth) &#124; [log](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/v2.0/atss/atss_r50_fpn_1x_coco/atss_r50_fpn_1x_coco_20200209_102539.log.json) |

‎configs/carafe/README.md

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The results on COCO 2017 val is shown in the below table.
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| Method | Backbone | Style | Lr schd | Test Proposal Num| Box AP | Mask AP | Download |
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| :--------------------: | :-------------: | :-----: | :-----: | :--------------: | :----: | :--------: |:----------------------------------------------------------------------------------------------------: |
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| Faster R-CNN w/ CARAFE | R-50-FPN | pytorch | 1x | 1000 | 37.8 | - | [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/carafe/faster_rcnn_r50_fpn_carafe_1x-2ca2d094.pth) |
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| - | - | - | - | 2000 | 37.9 | - | - |
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| Mask R-CNN w/ CARAFE | R-50-FPN | pytorch | 1x | 1000 | 38.6 | 35.6| [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/carafe/mask_rcnn_r50_fpn_carafe_1x-2cc4b9fe.pth) |
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| - | - | - | - | 2000 | 38.6 | 35.7| - |
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| Method | Backbone | Style | Lr schd | Test Proposal Num| Box AP | Mask AP | Download |
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| :--------------------: | :------: | :-----: |:-------:| :--------------: | :----: | :--------: |:-------: |
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| Faster R-CNN w/ CARAFE | R-50-FPN | pytorch | 1x | 1000 | | | |
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| - | - | - | - | 2000 | | | |
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| Mask R-CNN w/ CARAFE | R-50-FPN | pytorch | 1x | 1000 | | | |
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| - | - | - | - | 2000 | | | |
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## Implementation
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‎configs/cascade_rcnn/README.md

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# Cascade R-CNN: High Quality Object Detection and Instance Segmentation
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## Introduction
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```
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@article{Cai_2019,
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title={Cascade R-CNN: High Quality Object Detection and Instance Segmentation},
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ISSN={1939-3539},
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url={http://dx.doi.org/10.1109/tpami.2019.2956516},
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DOI={10.1109/tpami.2019.2956516},
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journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
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publisher={Institute of Electrical and Electronics Engineers (IEEE)},
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author={Cai, Zhaowei and Vasconcelos, Nuno},
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year={2019},
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pages={1–1}
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}
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```
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## Results and models
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### Cascade R-CNN
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| Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | Download |
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| :-------------: | :-----: | :-----: | :------: | :------------: | :----: |:--------:|
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| R-50-FPN | caffe | 1x | | | | |
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| R-50-FPN | pytorch | 1x | 4.4 | 14.3 | 40.3 | [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_r50_fpn_1x_coco/cascade_rcnn_r50_fpn_1x_coco_20200316-3dc56deb.pth) &#124; [log](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_r50_fpn_1x_coco/cascade_rcnn_r50_fpn_1x_coco_20200316_214748.log.json) |
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| R-50-FPN | pytorch | 20e | | | | |
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| R-101-FPN | caffe | 1x | | | | |
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| R-101-FPN | pytorch | 1x | 6.4 | 11.6 | 42.0 | [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_r101_fpn_1x_coco/cascade_rcnn_r101_fpn_1x_coco_20200317-0b6a2fbf.pth) &#124; [log](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_r101_fpn_1x_coco/cascade_rcnn_r101_fpn_1x_coco_20200317_101744.log.json) |
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| R-101-FPN | pytorch | 20e | | | | |
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| X-101-32x4d-FPN | pytorch | 1x | 7.6 | 10.2 | 43.7 | [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_x101_32x4d_fpn_1x_coco/cascade_rcnn_x101_32x4d_fpn_1x_coco_20200316-95c2deb6.pth) &#124; [log](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_x101_32x4d_fpn_1x_coco/cascade_rcnn_x101_32x4d_fpn_1x_coco_20200316_055608.log.json) |
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| X-101-32x4d-FPN | pytorch | 20e | | | | |
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| X-101-64x4d-FPN | pytorch | 1x | | | | |
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| X-101-64x4d-FPN | pytorch | 20e | | | | |
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### Cascade Mask R-CNN
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| Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Download |
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| :-------------: | :-----: | :-----: | :------: | :------------: | :----: | :-----: | :----------------: |
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| R-50-C4 | caffe | 1x | | | | | |
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| R-50-FPN | caffe | 1x | | | | | |
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| R-50-FPN | pytorch | 1x | 6.0 | 8.5 | 41.2 | 35.9 | [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r50_fpn_1x_coco/cascade_mask_rcnn_r50_fpn_1x_coco_20200203-9d4dcb24.pth) &#124; [log](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r50_fpn_1x_coco/cascade_mask_rcnn_r50_fpn_1x_coco_20200203_170449.log.json) |
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| R-50-FPN | pytorch | 20e | | | | | |
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| R-101-FPN | caffe | 1x | | | | | |
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| R-101-FPN | pytorch | 1x | 7.9 | 7.2 | 42.9 | 37.3 | [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r101_fpn_1x_coco/cascade_mask_rcnn_r101_fpn_1x_coco_20200203-befdf6ee.pth) &#124; [log](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r101_fpn_1x_coco/cascade_mask_rcnn_r101_fpn_1x_coco_20200203_092521.log.json) |
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| R-101-FPN | pytorch | 20e | | | | | |
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| X-101-32x4d-FPN | pytorch | 1x | 9.2 | 6.8 | 44.3 | 38.3 | [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_1x_coco_20200201-0f411b1f.pth) &#124; [log](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_1x_coco_20200201_052416.log.json) |
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| X-101-32x4d-FPN | pytorch | 20e | | | | | |
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| X-101-64x4d-FPN | pytorch | 1x | 12.2 | 5.7 | 45.3 | 39.2 | [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_x101_64x4d_fpn_1x_coco/cascade_mask_rcnn_x101_64x4d_fpn_1x_coco_20200203-9a2db89d.pth) &#124; [log](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_x101_64x4d_fpn_1x_coco/cascade_mask_rcnn_x101_64x4d_fpn_1x_coco_20200203_044059.log.json) |
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| X-101-64x4d-FPN | pytorch | 20e | | | | | |
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**Notes:**
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- The `20e` schedule in Cascade (Mask) R-CNN indicates decreasing the lr at 16 and 19 epochs, with a total of 20 epochs.

‎configs/cascade_rcnn/cascade_mask_rcnn_r101_caffe_fpn_1x_coco.py

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_base_ = './cascade_mask_rcnn_r50_caffe_fpn_1x_coco.py'
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model = dict(
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pretrained='open-mmlab://resnet101_caffe_bgr', backbone=dict(depth=101))

‎configs/cascade_rcnn/cascade_mask_rcnn_r101_fpn_20e_coco.py

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_base_ = './cascade_mask_rcnn_r50_fpn_20e_coco.py'
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model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101))

‎configs/cascade_rcnn/cascade_mask_rcnn_r50_caffe_fpn_1x_coco.py

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_base_ = ['./cascade_mask_rcnn_r50_fpn_1x_coco.py']
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model = dict(
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pretrained='open-mmlab://resnet50_caffe_bgr',
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backbone=dict(
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norm_cfg=dict(requires_grad=False), norm_eval=True, style='caffe'))
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img_norm_cfg = dict(
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mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False)
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train_pipeline = [
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dict(type='LoadImageFromFile'),
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dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
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dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
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dict(type='RandomFlip', flip_ratio=0.5),
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dict(type='Normalize', **img_norm_cfg),
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dict(type='Pad', size_divisor=32),
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dict(type='DefaultFormatBundle'),
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dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']),
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]
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test_pipeline = [
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dict(type='LoadImageFromFile'),
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dict(
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type='MultiScaleFlipAug',
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img_scale=(1333, 800),
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flip=False,
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transforms=[
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dict(type='Resize', keep_ratio=True),
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dict(type='RandomFlip'),
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dict(type='Normalize', **img_norm_cfg),
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dict(type='Pad', size_divisor=32),
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dict(type='ImageToTensor', keys=['img']),
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dict(type='Collect', keys=['img']),
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])
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]
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data = dict(
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train=dict(pipeline=train_pipeline),
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val=dict(pipeline=test_pipeline),
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test=dict(pipeline=test_pipeline))

‎configs/cascade_rcnn/cascade_mask_rcnn_r50_fpn_20e_coco.py

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_base_ = [
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'../_base_/models/cascade_mask_rcnn_r50_fpn.py',
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'../_base_/datasets/coco_instance.py',
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'../_base_/schedules/schedule_20e.py', '../_base_/default_runtime.py'
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]

‎configs/reppoints/reppoints_moment_x101_fpn_dconv_c3-c5_gn-neck+head_mstrain_480-960_2x_coco.py ‎configs/cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_20e_coco.py

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_base_ = './reppoints_moment_r50_fpn_gn-neck+head_mstrain_480-960_2x_coco.py'
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_base_ = './cascade_mask_rcnn_r50_fpn_20e_coco.py'
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model = dict(
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pretrained='open-mmlab://resnext101_32x4d',
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backbone=dict(
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out_indices=(0, 1, 2, 3),
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frozen_stages=1,
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norm_cfg=dict(type='BN', requires_grad=True),
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style='pytorch',
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dcn=dict(type='DCN', deformable_groups=1, fallback_on_stride=False),
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stage_with_dcn=(False, True, True, True)))
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style='pytorch'))

‎configs/cascade_rcnn/cascade_mask_rcnn_x101_64x4d_fpn_20e_coco.py

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_base_ = './cascade_mask_rcnn_r50_fpn_20e_coco.py'
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model = dict(
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pretrained='open-mmlab://resnext101_64x4d',
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backbone=dict(
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type='ResNeXt',
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depth=101,
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groups=64,
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base_width=4,
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num_stages=4,
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out_indices=(0, 1, 2, 3),
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frozen_stages=1,
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norm_cfg=dict(type='BN', requires_grad=True),
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style='pytorch'))

‎configs/cascade_rcnn/cascade_rcnn_r101_caffe_fpn_1x_coco.py

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_base_ = './cascade_rcnn_r50_caffe_fpn_1x_coco.py'
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model = dict(
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pretrained='open-mmlab://resnet101_caffe_bgr', backbone=dict(depth=101))

‎configs/cascade_rcnn/cascade_rcnn_r101_fpn_20e_coco.py

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_base_ = './cascade_rcnn_r50_fpn_20e_coco.py'
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model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101))

‎configs/cascade_rcnn/cascade_rcnn_r50_caffe_fpn_1x_coco.py

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_base_ = './cascade_rcnn_r50_fpn_1x_coco.py'
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model = dict(
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pretrained='open-mmlab://resnet50_caffe_bgr',
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backbone=dict(norm_cfg=dict(requires_grad=False), style='caffe'))
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# use caffe img_norm
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img_norm_cfg = dict(
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mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False)
10+
train_pipeline = [
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dict(type='LoadImageFromFile'),
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dict(type='LoadAnnotations', with_bbox=True),
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dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
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dict(type='RandomFlip', flip_ratio=0.5),
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dict(type='Normalize', **img_norm_cfg),
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dict(type='Pad', size_divisor=32),
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dict(type='DefaultFormatBundle'),
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dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
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]
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test_pipeline = [
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dict(type='LoadImageFromFile'),
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dict(
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type='MultiScaleFlipAug',
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img_scale=(1333, 800),
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flip=False,
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transforms=[
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dict(type='Resize', keep_ratio=True),
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dict(type='RandomFlip'),
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dict(type='Normalize', **img_norm_cfg),
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dict(type='Pad', size_divisor=32),
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dict(type='ImageToTensor', keys=['img']),
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dict(type='Collect', keys=['img']),
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])
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]
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data = dict(
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train=dict(pipeline=train_pipeline),
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val=dict(pipeline=test_pipeline),
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test=dict(pipeline=test_pipeline))

‎configs/cascade_rcnn/cascade_rcnn_r50_fpn_20e_coco.py

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1+
_base_ = './cascade_rcnn_r50_fpn_1x_coco.py'
2+
# learning policy
3+
lr_config = dict(step=[16, 19])
4+
total_epochs = 20

‎configs/cascade_rcnn/cascade_rcnn_x101_32x4d_fpn_20e_coco.py

+13
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1+
_base_ = './cascade_rcnn_r50_fpn_20e_coco.py'
2+
model = dict(
3+
pretrained='open-mmlab://resnext101_32x4d',
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backbone=dict(
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type='ResNeXt',
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depth=101,
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groups=32,
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base_width=4,
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num_stages=4,
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out_indices=(0, 1, 2, 3),
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frozen_stages=1,
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norm_cfg=dict(type='BN', requires_grad=True),
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style='pytorch'))

‎configs/cascade_rcnn/cascade_rcnn_x101_64x4d_fpn_20e_coco.py

+14
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1+
_base_ = './cascade_rcnn_r50_fpn_20e_coco.py'
2+
model = dict(
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type='CascadeRCNN',
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pretrained='open-mmlab://resnext101_64x4d',
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backbone=dict(
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type='ResNeXt',
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depth=101,
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groups=64,
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base_width=4,
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num_stages=4,
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out_indices=(0, 1, 2, 3),
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frozen_stages=1,
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norm_cfg=dict(type='BN', requires_grad=True),
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style='pytorch'))

‎configs/cityscapes/README.md

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@@ -10,12 +10,12 @@
1010

1111
### Faster R-CNN
1212

13-
| Backbone | Style | Lr schd | Scale | Mem (GB) | Train time (s/iter) | Inf time (fps) | box AP | Download |
14-
| :-------------: | :-----: | :-----: | :---: | :------: | :-----------------: | :------------: | :----: | :------: |
15-
| R-50-FPN | pytorch | 1x | 800-1024 | 4.9 | - | - | 41.6 | [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/cityscapes/faster_rcnn_r50_fpn_1x_cityscapes_20200227-362cfbbf.pth) |
13+
| Backbone | Style | Lr schd | Scale | Mem (GB) | Inf time (fps) | box AP | Download |
14+
| :-------------: | :-----: | :-----: | :---: | :------: | :------------: | :----: | :------: |
15+
| R-50-FPN | pytorch | 1x | 800-1024 | | | | |
1616

1717
### Mask R-CNN
1818

19-
| Backbone | Style | Lr schd | Scale | Mem (GB) | Train time (s/iter) | Inf time (fps) | box AP | mask AP | Download |
20-
| :-------------: | :-----: | :-----: | :------: | :------: | :-----------------: | :------------: | :----: | :-----: | :------: |
21-
| R-50-FPN | pytorch | 1x | 800-1024 | 4.9 | - | - | 41.9 | 37.1 | [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/cityscapes/mask_rcnn_r50_fpn_1x_cityscapes_20200227-afe51d5a.pth) |
19+
| Backbone | Style | Lr schd | Scale | Mem (GB) | Inf time (fps) | box AP | mask AP | Download |
20+
| :-------------: | :-----: | :-----: | :------: | :------: | :------------: | :----: | :-----: | :------: |
21+
| R-50-FPN | pytorch | 1x | 800-1024 | | | | | |

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