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mmdetection | mmdetection-master/configs/res2net/metafile.yml | Models:
- Name: faster_rcnn_r2_101_fpn_2x_coco
In Collection: Faster R-CNN
Config: configs/res2net/faster_rcnn_r2_101_fpn_2x_coco.py
Metadata:
Training Memory (GB): 7.4
Epochs: 24
Training Data: COCO
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Resources: 8x V100 GPUs
Architecture:
- Res2Net
Results:
- Task: Object Detection
Dataset: COCO
Metrics:
box AP: 43.0
Weights: https://download.openmmlab.com/mmdetection/v2.0/res2net/faster_rcnn_r2_101_fpn_2x_coco/faster_rcnn_r2_101_fpn_2x_coco-175f1da6.pth
Paper:
URL: https://arxiv.org/abs/1904.01169
Title: 'Res2Net for object detection and instance segmentation'
README: configs/res2net/README.md
Code:
URL: https://github.com/open-mmlab/mmdetection/blob/v2.1.0/mmdet/models/backbones/res2net.py#L239
Version: v2.1.0
- Name: mask_rcnn_r2_101_fpn_2x_coco
In Collection: Mask R-CNN
Config: configs/res2net/mask_rcnn_r2_101_fpn_2x_coco.py
Metadata:
Training Memory (GB): 7.9
Epochs: 24
Training Data: COCO
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Resources: 8x V100 GPUs
Architecture:
- Res2Net
Results:
- Task: Object Detection
Dataset: COCO
Metrics:
box AP: 43.6
- Task: Instance Segmentation
Dataset: COCO
Metrics:
mask AP: 38.7
Weights: https://download.openmmlab.com/mmdetection/v2.0/res2net/mask_rcnn_r2_101_fpn_2x_coco/mask_rcnn_r2_101_fpn_2x_coco-17f061e8.pth
Paper:
URL: https://arxiv.org/abs/1904.01169
Title: 'Res2Net for object detection and instance segmentation'
README: configs/res2net/README.md
Code:
URL: https://github.com/open-mmlab/mmdetection/blob/v2.1.0/mmdet/models/backbones/res2net.py#L239
Version: v2.1.0
- Name: cascade_rcnn_r2_101_fpn_20e_coco
In Collection: Cascade R-CNN
Config: configs/res2net/cascade_rcnn_r2_101_fpn_20e_coco.py
Metadata:
Training Memory (GB): 7.8
Epochs: 20
Training Data: COCO
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Resources: 8x V100 GPUs
Architecture:
- Res2Net
Results:
- Task: Object Detection
Dataset: COCO
Metrics:
box AP: 45.7
Weights: https://download.openmmlab.com/mmdetection/v2.0/res2net/cascade_rcnn_r2_101_fpn_20e_coco/cascade_rcnn_r2_101_fpn_20e_coco-f4b7b7db.pth
Paper:
URL: https://arxiv.org/abs/1904.01169
Title: 'Res2Net for object detection and instance segmentation'
README: configs/res2net/README.md
Code:
URL: https://github.com/open-mmlab/mmdetection/blob/v2.1.0/mmdet/models/backbones/res2net.py#L239
Version: v2.1.0
- Name: cascade_mask_rcnn_r2_101_fpn_20e_coco
In Collection: Cascade R-CNN
Config: configs/res2net/cascade_mask_rcnn_r2_101_fpn_20e_coco.py
Metadata:
Training Memory (GB): 9.5
Epochs: 20
Training Data: COCO
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Resources: 8x V100 GPUs
Architecture:
- Res2Net
Results:
- Task: Object Detection
Dataset: COCO
Metrics:
box AP: 46.4
- Task: Instance Segmentation
Dataset: COCO
Metrics:
mask AP: 40.0
Weights: https://download.openmmlab.com/mmdetection/v2.0/res2net/cascade_mask_rcnn_r2_101_fpn_20e_coco/cascade_mask_rcnn_r2_101_fpn_20e_coco-8a7b41e1.pth
Paper:
URL: https://arxiv.org/abs/1904.01169
Title: 'Res2Net for object detection and instance segmentation'
README: configs/res2net/README.md
Code:
URL: https://github.com/open-mmlab/mmdetection/blob/v2.1.0/mmdet/models/backbones/res2net.py#L239
Version: v2.1.0
- Name: htc_r2_101_fpn_20e_coco
In Collection: HTC
Config: configs/res2net/htc_r2_101_fpn_20e_coco.py
Metadata:
Epochs: 20
Training Data: COCO
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Resources: 8x V100 GPUs
Architecture:
- Res2Net
Results:
- Task: Object Detection
Dataset: COCO
Metrics:
box AP: 47.5
- Task: Instance Segmentation
Dataset: COCO
Metrics:
mask AP: 41.6
Weights: https://download.openmmlab.com/mmdetection/v2.0/res2net/htc_r2_101_fpn_20e_coco/htc_r2_101_fpn_20e_coco-3a8d2112.pth
Paper:
URL: https://arxiv.org/abs/1904.01169
Title: 'Res2Net for object detection and instance segmentation'
README: configs/res2net/README.md
Code:
URL: https://github.com/open-mmlab/mmdetection/blob/v2.1.0/mmdet/models/backbones/res2net.py#L239
Version: v2.1.0
| 4,908 | 32.394558 | 157 | yml |
mmdetection | mmdetection-master/configs/resnest/README.md | # ResNeSt
> [ResNeSt: Split-Attention Networks](https://arxiv.org/abs/2004.08955)
<!-- [BACKBONE] -->
## Abstract
It is well known that featuremap attention and multi-path representation are important for visual recognition. In this paper, we present a modularized architecture, which applies the channel-wise attention on different network branches to leverage their success in capturing cross-feature interactions and learning diverse representations. Our design results in a simple and unified computation block, which can be parameterized using only a few variables. Our model, named ResNeSt, outperforms EfficientNet in accuracy and latency trade-off on image classification. In addition, ResNeSt has achieved superior transfer learning results on several public benchmarks serving as the backbone, and has been adopted by the winning entries of COCO-LVIS challenge.
<div align=center>
<img src="https://user-images.githubusercontent.com/40661020/143973475-b5b33b15-ed04-4fc6-890a-521f1a62bc52.png"/>
</div>
## Results and Models
### Faster R-CNN
| Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download |
| :-------: | :-----: | :-----: | :------: | :------------: | :----: | :------------------------------------------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
| S-50-FPN | pytorch | 1x | 4.8 | - | 42.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/resnest/faster_rcnn_s50_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/resnest/faster_rcnn_s50_fpn_syncbn-backbone%2Bhead_mstrain-range_1x_coco/faster_rcnn_s50_fpn_syncbn-backbone%2Bhead_mstrain-range_1x_coco_20200926_125502-20289c16.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/resnest/faster_rcnn_s50_fpn_syncbn-backbone%2Bhead_mstrain-range_1x_coco/faster_rcnn_s50_fpn_syncbn-backbone%2Bhead_mstrain-range_1x_coco-20200926_125502.log.json) |
| S-101-FPN | pytorch | 1x | 7.1 | - | 44.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/resnest/faster_rcnn_s101_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/resnest/faster_rcnn_s101_fpn_syncbn-backbone%2Bhead_mstrain-range_1x_coco/faster_rcnn_s101_fpn_syncbn-backbone%2Bhead_mstrain-range_1x_coco_20201006_021058-421517f1.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/resnest/faster_rcnn_s101_fpn_syncbn-backbone%2Bhead_mstrain-range_1x_coco/faster_rcnn_s101_fpn_syncbn-backbone%2Bhead_mstrain-range_1x_coco-20201006_021058.log.json) |
### Mask R-CNN
| Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download |
| :-------: | :-----: | :-----: | :------: | :------------: | :----: | :-----: | :----------------------------------------------------------------------------------------------------------------------------------------: | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
| S-50-FPN | pytorch | 1x | 5.5 | - | 42.6 | 38.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/resnest/mask_rcnn_s50_fpn_syncbn-backbone+head_mstrain_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/resnest/mask_rcnn_s50_fpn_syncbn-backbone%2Bhead_mstrain_1x_coco/mask_rcnn_s50_fpn_syncbn-backbone%2Bhead_mstrain_1x_coco_20200926_125503-8a2c3d47.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/resnest/mask_rcnn_s50_fpn_syncbn-backbone%2Bhead_mstrain_1x_coco/mask_rcnn_s50_fpn_syncbn-backbone%2Bhead_mstrain_1x_coco-20200926_125503.log.json) |
| S-101-FPN | pytorch | 1x | 7.8 | - | 45.2 | 40.2 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/resnest/mask_rcnn_s101_fpn_syncbn-backbone+head_mstrain_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/resnest/mask_rcnn_s101_fpn_syncbn-backbone%2Bhead_mstrain_1x_coco/mask_rcnn_s101_fpn_syncbn-backbone%2Bhead_mstrain_1x_coco_20201005_215831-af60cdf9.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/resnest/mask_rcnn_s101_fpn_syncbn-backbone%2Bhead_mstrain_1x_coco/mask_rcnn_s101_fpn_syncbn-backbone%2Bhead_mstrain_1x_coco-20201005_215831.log.json) |
### Cascade R-CNN
| Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download |
| :-------: | :-----: | :-----: | :------: | :------------: | :----: | :-------------------------------------------------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
| S-50-FPN | pytorch | 1x | - | - | 44.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/resnest/cascade_rcnn_s50_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/resnest/cascade_rcnn_s50_fpn_syncbn-backbone%2Bhead_mstrain-range_1x_coco/cascade_rcnn_s50_fpn_syncbn-backbone%2Bhead_mstrain-range_1x_coco_20201122_213640-763cc7b5.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/resnest/cascade_rcnn_s101_fpn_syncbn-backbone%2Bhead_mstrain-range_1x_coco/cascade_rcnn_s101_fpn_syncbn-backbone%2Bhead_mstrain-range_1x_coco-20201005_113242.log.json) |
| S-101-FPN | pytorch | 1x | 8.4 | - | 46.8 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/resnest/cascade_rcnn_s101_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/resnest/cascade_rcnn_s101_fpn_syncbn-backbone%2Bhead_mstrain-range_1x_coco/cascade_rcnn_s101_fpn_syncbn-backbone%2Bhead_mstrain-range_1x_coco_20201005_113242-b9459f8f.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/resnest/cascade_rcnn_s50_fpn_syncbn-backbone%2Bhead_mstrain-range_1x_coco/cascade_rcnn_s50_fpn_syncbn-backbone%2Bhead_mstrain-range_1x_coco-20201122_213640.log.json) |
### Cascade Mask R-CNN
| Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download |
| :-------: | :-----: | :-----: | :------: | :------------: | :----: | :-----: | :------------------------------------------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
| S-50-FPN | pytorch | 1x | - | - | 45.4 | 39.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/resnest/cascade_mask_rcnn_s50_fpn_syncbn-backbone+head_mstrain_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/resnest/cascade_mask_rcnn_s50_fpn_syncbn-backbone%2Bhead_mstrain_1x_coco/cascade_mask_rcnn_s50_fpn_syncbn-backbone%2Bhead_mstrain_1x_coco_20201122_104428-99eca4c7.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/resnest/cascade_mask_rcnn_s50_fpn_syncbn-backbone%2Bhead_mstrain_1x_coco/cascade_mask_rcnn_s50_fpn_syncbn-backbone%2Bhead_mstrain_1x_coco-20201122_104428.log.json) |
| S-101-FPN | pytorch | 1x | 10.5 | - | 47.7 | 41.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/resnest/cascade_mask_rcnn_s101_fpn_syncbn-backbone+head_mstrain_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/resnest/cascade_mask_rcnn_s101_fpn_syncbn-backbone%2Bhead_mstrain_1x_coco/cascade_mask_rcnn_s101_fpn_syncbn-backbone%2Bhead_mstrain_1x_coco_20201005_113243-42607475.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/resnest/cascade_mask_rcnn_s101_fpn_syncbn-backbone%2Bhead_mstrain_1x_coco/cascade_mask_rcnn_s101_fpn_syncbn-backbone%2Bhead_mstrain_1x_coco-20201005_113243.log.json) |
## Citation
```latex
@article{zhang2020resnest,
title={ResNeSt: Split-Attention Networks},
author={Zhang, Hang and Wu, Chongruo and Zhang, Zhongyue and Zhu, Yi and Zhang, Zhi and Lin, Haibin and Sun, Yue and He, Tong and Muller, Jonas and Manmatha, R. and Li, Mu and Smola, Alexander},
journal={arXiv preprint arXiv:2004.08955},
year={2020}
}
```
| 12,139 | 219.727273 | 757 | md |
mmdetection | mmdetection-master/configs/resnest/cascade_mask_rcnn_s101_fpn_syncbn-backbone+head_mstrain_1x_coco.py | _base_ = './cascade_mask_rcnn_s50_fpn_syncbn-backbone+head_mstrain_1x_coco.py'
model = dict(
backbone=dict(
stem_channels=128,
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='open-mmlab://resnest101')))
| 261 | 31.75 | 78 | py |
mmdetection | mmdetection-master/configs/resnest/cascade_mask_rcnn_s50_fpn_syncbn-backbone+head_mstrain_1x_coco.py | _base_ = '../cascade_rcnn/cascade_mask_rcnn_r50_fpn_1x_coco.py'
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
backbone=dict(
type='ResNeSt',
stem_channels=64,
depth=50,
radix=2,
reduction_factor=4,
avg_down_stride=True,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=norm_cfg,
norm_eval=False,
style='pytorch',
init_cfg=dict(type='Pretrained', checkpoint='open-mmlab://resnest50')),
roi_head=dict(
bbox_head=[
dict(
type='Shared4Conv1FCBBoxHead',
in_channels=256,
conv_out_channels=256,
fc_out_channels=1024,
norm_cfg=norm_cfg,
roi_feat_size=7,
num_classes=80,
bbox_coder=dict(
type='DeltaXYWHBBoxCoder',
target_means=[0., 0., 0., 0.],
target_stds=[0.1, 0.1, 0.2, 0.2]),
reg_class_agnostic=True,
loss_cls=dict(
type='CrossEntropyLoss',
use_sigmoid=False,
loss_weight=1.0),
loss_bbox=dict(type='SmoothL1Loss', beta=1.0,
loss_weight=1.0)),
dict(
type='Shared4Conv1FCBBoxHead',
in_channels=256,
conv_out_channels=256,
fc_out_channels=1024,
norm_cfg=norm_cfg,
roi_feat_size=7,
num_classes=80,
bbox_coder=dict(
type='DeltaXYWHBBoxCoder',
target_means=[0., 0., 0., 0.],
target_stds=[0.05, 0.05, 0.1, 0.1]),
reg_class_agnostic=True,
loss_cls=dict(
type='CrossEntropyLoss',
use_sigmoid=False,
loss_weight=1.0),
loss_bbox=dict(type='SmoothL1Loss', beta=1.0,
loss_weight=1.0)),
dict(
type='Shared4Conv1FCBBoxHead',
in_channels=256,
conv_out_channels=256,
fc_out_channels=1024,
norm_cfg=norm_cfg,
roi_feat_size=7,
num_classes=80,
bbox_coder=dict(
type='DeltaXYWHBBoxCoder',
target_means=[0., 0., 0., 0.],
target_stds=[0.033, 0.033, 0.067, 0.067]),
reg_class_agnostic=True,
loss_cls=dict(
type='CrossEntropyLoss',
use_sigmoid=False,
loss_weight=1.0),
loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0))
],
mask_head=dict(norm_cfg=norm_cfg)))
# # use ResNeSt img_norm
img_norm_cfg = dict(
mean=[123.68, 116.779, 103.939], std=[58.393, 57.12, 57.375], to_rgb=True)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='LoadAnnotations',
with_bbox=True,
with_mask=True,
poly2mask=False),
dict(
type='Resize',
img_scale=[(1333, 640), (1333, 672), (1333, 704), (1333, 736),
(1333, 768), (1333, 800)],
multiscale_mode='value',
keep_ratio=True),
dict(type='RandomFlip', flip_ratio=0.5),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']),
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(1333, 800),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='RandomFlip'),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img']),
])
]
data = dict(
train=dict(pipeline=train_pipeline),
val=dict(pipeline=test_pipeline),
test=dict(pipeline=test_pipeline))
| 4,255 | 34.764706 | 79 | py |
mmdetection | mmdetection-master/configs/resnest/cascade_rcnn_s101_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py | _base_ = './cascade_rcnn_s50_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py'
model = dict(
backbone=dict(
stem_channels=128,
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='open-mmlab://resnest101')))
| 262 | 31.875 | 79 | py |
mmdetection | mmdetection-master/configs/resnest/cascade_rcnn_s50_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py | _base_ = '../cascade_rcnn/cascade_rcnn_r50_fpn_1x_coco.py'
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
backbone=dict(
type='ResNeSt',
stem_channels=64,
depth=50,
radix=2,
reduction_factor=4,
avg_down_stride=True,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=norm_cfg,
norm_eval=False,
style='pytorch',
init_cfg=dict(type='Pretrained', checkpoint='open-mmlab://resnest50')),
roi_head=dict(
bbox_head=[
dict(
type='Shared4Conv1FCBBoxHead',
in_channels=256,
conv_out_channels=256,
fc_out_channels=1024,
norm_cfg=norm_cfg,
roi_feat_size=7,
num_classes=80,
bbox_coder=dict(
type='DeltaXYWHBBoxCoder',
target_means=[0., 0., 0., 0.],
target_stds=[0.1, 0.1, 0.2, 0.2]),
reg_class_agnostic=True,
loss_cls=dict(
type='CrossEntropyLoss',
use_sigmoid=False,
loss_weight=1.0),
loss_bbox=dict(type='SmoothL1Loss', beta=1.0,
loss_weight=1.0)),
dict(
type='Shared4Conv1FCBBoxHead',
in_channels=256,
conv_out_channels=256,
fc_out_channels=1024,
norm_cfg=norm_cfg,
roi_feat_size=7,
num_classes=80,
bbox_coder=dict(
type='DeltaXYWHBBoxCoder',
target_means=[0., 0., 0., 0.],
target_stds=[0.05, 0.05, 0.1, 0.1]),
reg_class_agnostic=True,
loss_cls=dict(
type='CrossEntropyLoss',
use_sigmoid=False,
loss_weight=1.0),
loss_bbox=dict(type='SmoothL1Loss', beta=1.0,
loss_weight=1.0)),
dict(
type='Shared4Conv1FCBBoxHead',
in_channels=256,
conv_out_channels=256,
fc_out_channels=1024,
norm_cfg=norm_cfg,
roi_feat_size=7,
num_classes=80,
bbox_coder=dict(
type='DeltaXYWHBBoxCoder',
target_means=[0., 0., 0., 0.],
target_stds=[0.033, 0.033, 0.067, 0.067]),
reg_class_agnostic=True,
loss_cls=dict(
type='CrossEntropyLoss',
use_sigmoid=False,
loss_weight=1.0),
loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0))
], ))
# # use ResNeSt img_norm
img_norm_cfg = dict(
mean=[123.68, 116.779, 103.939], std=[58.393, 57.12, 57.375], to_rgb=True)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='LoadAnnotations',
with_bbox=True,
with_mask=False,
poly2mask=False),
dict(
type='Resize',
img_scale=[(1333, 640), (1333, 800)],
multiscale_mode='range',
keep_ratio=True),
dict(type='RandomFlip', flip_ratio=0.5),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(1333, 800),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='RandomFlip'),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img']),
])
]
data = dict(
train=dict(pipeline=train_pipeline),
val=dict(pipeline=test_pipeline),
test=dict(pipeline=test_pipeline))
| 4,127 | 34.282051 | 79 | py |
mmdetection | mmdetection-master/configs/resnest/faster_rcnn_s101_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py | _base_ = './faster_rcnn_s50_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py'
model = dict(
backbone=dict(
stem_channels=128,
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='open-mmlab://resnest101')))
| 261 | 31.75 | 78 | py |
mmdetection | mmdetection-master/configs/resnest/faster_rcnn_s50_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py | _base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py'
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
backbone=dict(
type='ResNeSt',
stem_channels=64,
depth=50,
radix=2,
reduction_factor=4,
avg_down_stride=True,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=norm_cfg,
norm_eval=False,
style='pytorch',
init_cfg=dict(type='Pretrained', checkpoint='open-mmlab://resnest50')),
roi_head=dict(
bbox_head=dict(
type='Shared4Conv1FCBBoxHead',
conv_out_channels=256,
norm_cfg=norm_cfg)))
# # use ResNeSt img_norm
img_norm_cfg = dict(
mean=[123.68, 116.779, 103.939], std=[58.393, 57.12, 57.375], to_rgb=True)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='LoadAnnotations',
with_bbox=True,
with_mask=False,
poly2mask=False),
dict(
type='Resize',
img_scale=[(1333, 640), (1333, 800)],
multiscale_mode='range',
keep_ratio=True),
dict(type='RandomFlip', flip_ratio=0.5),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(1333, 800),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='RandomFlip'),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img']),
])
]
data = dict(
train=dict(pipeline=train_pipeline),
val=dict(pipeline=test_pipeline),
test=dict(pipeline=test_pipeline))
| 1,947 | 29.920635 | 79 | py |
mmdetection | mmdetection-master/configs/resnest/mask_rcnn_s101_fpn_syncbn-backbone+head_mstrain_1x_coco.py | _base_ = './mask_rcnn_s50_fpn_syncbn-backbone+head_mstrain_1x_coco.py'
model = dict(
backbone=dict(
stem_channels=128,
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='open-mmlab://resnest101')))
| 253 | 30.75 | 70 | py |
mmdetection | mmdetection-master/configs/resnest/mask_rcnn_s50_fpn_syncbn-backbone+head_mstrain_1x_coco.py | _base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py'
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
backbone=dict(
type='ResNeSt',
stem_channels=64,
depth=50,
radix=2,
reduction_factor=4,
avg_down_stride=True,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=norm_cfg,
norm_eval=False,
style='pytorch',
init_cfg=dict(type='Pretrained', checkpoint='open-mmlab://resnest50')),
roi_head=dict(
bbox_head=dict(
type='Shared4Conv1FCBBoxHead',
conv_out_channels=256,
norm_cfg=norm_cfg),
mask_head=dict(norm_cfg=norm_cfg)))
# # use ResNeSt img_norm
img_norm_cfg = dict(
mean=[123.68, 116.779, 103.939], std=[58.393, 57.12, 57.375], to_rgb=True)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='LoadAnnotations',
with_bbox=True,
with_mask=True,
poly2mask=False),
dict(
type='Resize',
img_scale=[(1333, 640), (1333, 672), (1333, 704), (1333, 736),
(1333, 768), (1333, 800)],
multiscale_mode='value',
keep_ratio=True),
dict(type='RandomFlip', flip_ratio=0.5),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']),
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(1333, 800),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='RandomFlip'),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img']),
])
]
data = dict(
train=dict(pipeline=train_pipeline),
val=dict(pipeline=test_pipeline),
test=dict(pipeline=test_pipeline))
| 2,068 | 30.830769 | 79 | py |
mmdetection | mmdetection-master/configs/resnest/metafile.yml | Models:
- Name: faster_rcnn_s50_fpn_syncbn-backbone+head_mstrain-range_1x_coco
In Collection: Faster R-CNN
Config: configs/resnest/faster_rcnn_s50_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py
Metadata:
Training Memory (GB): 4.8
Epochs: 12
Training Data: COCO
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Resources: 8x V100 GPUs
Architecture:
- ResNeSt
Results:
- Task: Object Detection
Dataset: COCO
Metrics:
box AP: 42.0
Weights: https://download.openmmlab.com/mmdetection/v2.0/resnest/faster_rcnn_s50_fpn_syncbn-backbone%2Bhead_mstrain-range_1x_coco/faster_rcnn_s50_fpn_syncbn-backbone%2Bhead_mstrain-range_1x_coco_20200926_125502-20289c16.pth
Paper:
URL: https://arxiv.org/abs/2004.08955
Title: 'ResNeSt: Split-Attention Networks'
README: configs/resnest/README.md
Code:
URL: https://github.com/open-mmlab/mmdetection/blob/v2.7.0/mmdet/models/backbones/resnest.py#L273
Version: v2.7.0
- Name: faster_rcnn_s101_fpn_syncbn-backbone+head_mstrain-range_1x_coco
In Collection: Faster R-CNN
Config: configs/resnest/faster_rcnn_s101_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py
Metadata:
Training Memory (GB): 7.1
Epochs: 12
Training Data: COCO
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Resources: 8x V100 GPUs
Architecture:
- ResNeSt
Results:
- Task: Object Detection
Dataset: COCO
Metrics:
box AP: 44.5
Weights: https://download.openmmlab.com/mmdetection/v2.0/resnest/faster_rcnn_s101_fpn_syncbn-backbone%2Bhead_mstrain-range_1x_coco/faster_rcnn_s101_fpn_syncbn-backbone%2Bhead_mstrain-range_1x_coco_20201006_021058-421517f1.pth
Paper:
URL: https://arxiv.org/abs/2004.08955
Title: 'ResNeSt: Split-Attention Networks'
README: configs/resnest/README.md
Code:
URL: https://github.com/open-mmlab/mmdetection/blob/v2.7.0/mmdet/models/backbones/resnest.py#L273
Version: v2.7.0
- Name: mask_rcnn_s50_fpn_syncbn-backbone+head_mstrain_1x_coco
In Collection: Mask R-CNN
Config: configs/resnest/mask_rcnn_s50_fpn_syncbn-backbone+head_mstrain_1x_coco.py
Metadata:
Training Memory (GB): 5.5
Epochs: 12
Training Data: COCO
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Resources: 8x V100 GPUs
Architecture:
- ResNeSt
Results:
- Task: Object Detection
Dataset: COCO
Metrics:
box AP: 42.6
- Task: Instance Segmentation
Dataset: COCO
Metrics:
mask AP: 38.1
Weights: https://download.openmmlab.com/mmdetection/v2.0/resnest/mask_rcnn_s50_fpn_syncbn-backbone%2Bhead_mstrain_1x_coco/mask_rcnn_s50_fpn_syncbn-backbone%2Bhead_mstrain_1x_coco_20200926_125503-8a2c3d47.pth
Paper:
URL: https://arxiv.org/abs/2004.08955
Title: 'ResNeSt: Split-Attention Networks'
README: configs/resnest/README.md
Code:
URL: https://github.com/open-mmlab/mmdetection/blob/v2.7.0/mmdet/models/backbones/resnest.py#L273
Version: v2.7.0
- Name: mask_rcnn_s101_fpn_syncbn-backbone+head_mstrain_1x_coco
In Collection: Mask R-CNN
Config: configs/resnest/mask_rcnn_s101_fpn_syncbn-backbone+head_mstrain_1x_coco.py
Metadata:
Training Memory (GB): 7.8
Epochs: 12
Training Data: COCO
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Resources: 8x V100 GPUs
Architecture:
- ResNeSt
Results:
- Task: Object Detection
Dataset: COCO
Metrics:
box AP: 45.2
- Task: Instance Segmentation
Dataset: COCO
Metrics:
mask AP: 40.2
Weights: https://download.openmmlab.com/mmdetection/v2.0/resnest/mask_rcnn_s101_fpn_syncbn-backbone%2Bhead_mstrain_1x_coco/mask_rcnn_s101_fpn_syncbn-backbone%2Bhead_mstrain_1x_coco_20201005_215831-af60cdf9.pth
Paper:
URL: https://arxiv.org/abs/2004.08955
Title: 'ResNeSt: Split-Attention Networks'
README: configs/resnest/README.md
Code:
URL: https://github.com/open-mmlab/mmdetection/blob/v2.7.0/mmdet/models/backbones/resnest.py#L273
Version: v2.7.0
- Name: cascade_rcnn_s50_fpn_syncbn-backbone+head_mstrain-range_1x_coco
In Collection: Cascade R-CNN
Config: configs/resnest/cascade_rcnn_s50_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py
Metadata:
Epochs: 12
Training Data: COCO
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Resources: 8x V100 GPUs
Architecture:
- ResNeSt
Results:
- Task: Object Detection
Dataset: COCO
Metrics:
box AP: 44.5
Weights: https://download.openmmlab.com/mmdetection/v2.0/resnest/cascade_rcnn_s50_fpn_syncbn-backbone%2Bhead_mstrain-range_1x_coco/cascade_rcnn_s50_fpn_syncbn-backbone%2Bhead_mstrain-range_1x_coco_20201122_213640-763cc7b5.pth
Paper:
URL: https://arxiv.org/abs/2004.08955
Title: 'ResNeSt: Split-Attention Networks'
README: configs/resnest/README.md
Code:
URL: https://github.com/open-mmlab/mmdetection/blob/v2.7.0/mmdet/models/backbones/resnest.py#L273
Version: v2.7.0
- Name: cascade_rcnn_s101_fpn_syncbn-backbone+head_mstrain-range_1x_coco
In Collection: Cascade R-CNN
Config: configs/resnest/cascade_rcnn_s101_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py
Metadata:
Training Memory (GB): 8.4
Epochs: 12
Training Data: COCO
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Resources: 8x V100 GPUs
Architecture:
- ResNeSt
Results:
- Task: Object Detection
Dataset: COCO
Metrics:
box AP: 46.8
Weights: https://download.openmmlab.com/mmdetection/v2.0/resnest/cascade_rcnn_s101_fpn_syncbn-backbone%2Bhead_mstrain-range_1x_coco/cascade_rcnn_s101_fpn_syncbn-backbone%2Bhead_mstrain-range_1x_coco_20201005_113242-b9459f8f.pth
Paper:
URL: https://arxiv.org/abs/2004.08955
Title: 'ResNeSt: Split-Attention Networks'
README: configs/resnest/README.md
Code:
URL: https://github.com/open-mmlab/mmdetection/blob/v2.7.0/mmdet/models/backbones/resnest.py#L273
Version: v2.7.0
- Name: cascade_mask_rcnn_s50_fpn_syncbn-backbone+head_mstrain_1x_coco
In Collection: Cascade R-CNN
Config: configs/resnest/cascade_mask_rcnn_s50_fpn_syncbn-backbone+head_mstrain_1x_coco.py
Metadata:
Epochs: 12
Training Data: COCO
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Resources: 8x V100 GPUs
Architecture:
- ResNeSt
Results:
- Task: Object Detection
Dataset: COCO
Metrics:
box AP: 45.4
- Task: Instance Segmentation
Dataset: COCO
Metrics:
mask AP: 39.5
Weights: https://download.openmmlab.com/mmdetection/v2.0/resnest/cascade_mask_rcnn_s50_fpn_syncbn-backbone%2Bhead_mstrain_1x_coco/cascade_mask_rcnn_s50_fpn_syncbn-backbone%2Bhead_mstrain_1x_coco_20201122_104428-99eca4c7.pth
Paper:
URL: https://arxiv.org/abs/2004.08955
Title: 'ResNeSt: Split-Attention Networks'
README: configs/resnest/README.md
Code:
URL: https://github.com/open-mmlab/mmdetection/blob/v2.7.0/mmdet/models/backbones/resnest.py#L273
Version: v2.7.0
- Name: cascade_mask_rcnn_s101_fpn_syncbn-backbone+head_mstrain_1x_coco
In Collection: Cascade R-CNN
Config: configs/resnest/cascade_mask_rcnn_s101_fpn_syncbn-backbone+head_mstrain_1x_coco.py
Metadata:
Training Memory (GB): 10.5
Epochs: 12
Training Data: COCO
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Resources: 8x V100 GPUs
Architecture:
- ResNeSt
Results:
- Task: Object Detection
Dataset: COCO
Metrics:
box AP: 47.7
- Task: Instance Segmentation
Dataset: COCO
Metrics:
mask AP: 41.4
Weights: https://download.openmmlab.com/mmdetection/v2.0/resnest/cascade_mask_rcnn_s101_fpn_syncbn-backbone%2Bhead_mstrain_1x_coco/cascade_mask_rcnn_s101_fpn_syncbn-backbone%2Bhead_mstrain_1x_coco_20201005_113243-42607475.pth
Paper:
URL: https://arxiv.org/abs/2004.08955
Title: 'ResNeSt: Split-Attention Networks'
README: configs/resnest/README.md
Code:
URL: https://github.com/open-mmlab/mmdetection/blob/v2.7.0/mmdet/models/backbones/resnest.py#L273
Version: v2.7.0
| 8,748 | 36.874459 | 231 | yml |
mmdetection | mmdetection-master/configs/resnet_strikes_back/README.md | # ResNet strikes back
> [ResNet strikes back: An improved training procedure in timm](https://arxiv.org/abs/2110.00476)
<!-- [OTHERS] -->
## Abstract
The influential Residual Networks designed by He et al. remain the gold-standard architecture in numerous scientific publications. They typically serve as the default architecture in studies, or as baselines when new architectures are proposed. Yet there has been significant progress on best practices for training neural networks since the inception of the ResNet architecture in 2015. Novel optimization & dataaugmentation have increased the effectiveness of the training recipes.
In this paper, we re-evaluate the performance of the vanilla ResNet-50 when trained with a procedure that integrates such advances. We share competitive training settings and pre-trained models in the timm open-source library, with the hope that they will serve as better baselines for future work. For instance, with our more demanding training setting, a vanilla ResNet-50 reaches 80.4% top-1 accuracy at resolution 224×224 on ImageNet-val without extra data or distillation. We also report the performance achieved with popular models with our training procedure.
<div align=center>
<img src="https://user-images.githubusercontent.com/12907710/149324625-4546a5a7-704f-406c-982f-0376a20d03d8.png"/>
</div>
## Results and Models
| Method | Backbone | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download |
| :----------------: | :------: | :-----: | :------: | :------------: | :---------: | :---------: | :-----------------------------------------------------------: | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
| Faster R-CNN | R-50 rsb | 1x | 3.9 | - | 40.8 (+3.4) | - | [Config](./faster_rcnn_r50_fpn_rsb-pretrain_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/resnet_strikes_back/faster_rcnn_r50_fpn_rsb-pretrain_1x_coco/faster_rcnn_r50_fpn_rsb-pretrain_1x_coco_20220113_162229-32ae82a9.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/resnet_strikes_back/faster_rcnn_r50_fpn_rsb-pretrain_1x_coco/faster_rcnn_r50_fpn_rsb-pretrain_1x_coco_20220113_162229.log.json) |
| Mask R-CNN | R-50 rsb | 1x | 4.5 | - | 41.2 (+3.0) | 38.2 (+3.0) | [Config](./mask_rcnn_r50_fpn_rsb-pretrain_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/resnet_strikes_back/mask_rcnn_r50_fpn_rsb-pretrain_1x_coco/mask_rcnn_r50_fpn_rsb-pretrain_1x_coco_20220113_174054-06ce8ba0.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/resnet_strikes_back/mask_rcnn_r50_fpn_rsb-pretrain_1x_coco/mask_rcnn_r50_fpn_rsb-pretrain_1x_coco_20220113_174054.log.json) |
| Cascade Mask R-CNN | R-50 rsb | 1x | 6.2 | - | 44.8 (+3.6) | 39.9 (+3.6) | [Config](./cascade_mask_rcnn_r50_fpn_rsb-pretrain_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/resnet_strikes_back/cascade_mask_rcnn_r50_fpn_rsb-pretrain_1x_coco/cascade_mask_rcnn_r50_fpn_rsb-pretrain_1x_coco_20220113_193636-8b9ad50f.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/resnet_strikes_back/cascade_mask_rcnn_r50_fpn_rsb-pretrain_1x_coco/cascade_mask_rcnn_r50_fpn_rsb-pretrain_1x_coco_20220113_193636.log.json) |
| RetinaNet | R-50 rsb | 1x | 3.8 | - | 39.0 (+2.5) | - | [Config](./retinanet_r50_fpn_rsb-pretrain_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/resnet_strikes_back/retinanet_r50_fpn_rsb-pretrain_1x_coco/retinanet_r50_fpn_rsb-pretrain_1x_coco_20220113_175432-bd24aae9.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/resnet_strikes_back/retinanet_r50_fpn_rsb-pretrain_1x_coco/retinanet_r50_fpn_rsb-pretrain_1x_coco_20220113_175432.log.json) |
**Notes:**
- 'rsb' is short for 'resnet strikes back'
- We have done some grid searches on learning rate and weight decay and get these optimal hyper-parameters.
## Citation
```latex
@article{wightman2021resnet,
title={Resnet strikes back: An improved training procedure in timm},
author={Ross Wightman, Hugo Touvron, Hervé Jégou},
journal={arXiv preprint arXiv:2110.00476},
year={2021}
}
```
| 5,147 | 124.560976 | 566 | md |
mmdetection | mmdetection-master/configs/resnet_strikes_back/cascade_mask_rcnn_r50_fpn_rsb-pretrain_1x_coco.py | _base_ = [
'../_base_/models/cascade_mask_rcnn_r50_fpn.py',
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
checkpoint = 'https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb256-rsb-a1-600e_in1k_20211228-20e21305.pth' # noqa
model = dict(
backbone=dict(
init_cfg=dict(
type='Pretrained', prefix='backbone.', checkpoint=checkpoint)))
optimizer = dict(
_delete_=True,
type='AdamW',
lr=0.0002,
weight_decay=0.05,
paramwise_cfg=dict(norm_decay_mult=0., bypass_duplicate=True))
| 612 | 31.263158 | 135 | py |
mmdetection | mmdetection-master/configs/resnet_strikes_back/faster_rcnn_r50_fpn_rsb-pretrain_1x_coco.py | _base_ = [
'../_base_/models/faster_rcnn_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
checkpoint = 'https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb256-rsb-a1-600e_in1k_20211228-20e21305.pth' # noqa
model = dict(
backbone=dict(
init_cfg=dict(
type='Pretrained', prefix='backbone.', checkpoint=checkpoint)))
optimizer = dict(
_delete_=True,
type='AdamW',
lr=0.0002,
weight_decay=0.05,
paramwise_cfg=dict(norm_decay_mult=0., bypass_duplicate=True))
| 607 | 31 | 135 | py |
mmdetection | mmdetection-master/configs/resnet_strikes_back/mask_rcnn_r50_fpn_rsb-pretrain_1x_coco.py | _base_ = [
'../_base_/models/mask_rcnn_r50_fpn.py',
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
checkpoint = 'https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb256-rsb-a1-600e_in1k_20211228-20e21305.pth' # noqa
model = dict(
backbone=dict(
init_cfg=dict(
type='Pretrained', prefix='backbone.', checkpoint=checkpoint)))
optimizer = dict(
_delete_=True,
type='AdamW',
lr=0.0002,
weight_decay=0.05,
paramwise_cfg=dict(norm_decay_mult=0., bypass_duplicate=True))
| 604 | 30.842105 | 135 | py |
mmdetection | mmdetection-master/configs/resnet_strikes_back/metafile.yml | Models:
- Name: faster_rcnn_r50_fpn_rsb-pretrain_1x_coco
In Collection: Faster R-CNN
Config: configs/resnet_strikes_back/faster_rcnn_r50_fpn_rsb-pretrain_1x_coco.py
Metadata:
Training Memory (GB): 3.9
Epochs: 12
Training Data: COCO
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Resources: 8x V100 GPUs
Architecture:
- ResNet
Results:
- Task: Object Detection
Dataset: COCO
Metrics:
box AP: 40.8
Weights: https://download.openmmlab.com/mmdetection/v2.0/resnet_strikes_back/faster_rcnn_r50_fpn_rsb-pretrain_1x_coco/faster_rcnn_r50_fpn_rsb-pretrain_1x_coco_20220113_162229-32ae82a9.pth
Paper:
URL: https://arxiv.org/abs/2110.00476
Title: 'ResNet strikes back: An improved training procedure in timm'
README: configs/resnet_strikes_back/README.md
Code:
URL: https://github.com/open-mmlab/mmdetection/blob/v2.22.0/configs/resnet_strikes_back/README.md
Version: v2.22.0
- Name: cascade_mask_rcnn_r50_fpn_rsb-pretrain_1x_coco
In Collection: Cascade R-CNN
Config: configs/resnet_strikes_back/cascade_mask_rcnn_r50_fpn_rsb-pretrain_1x_coco.py
Metadata:
Training Memory (GB): 6.2
Epochs: 12
Training Data: COCO
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Resources: 8x V100 GPUs
Architecture:
- ResNet
Results:
- Task: Object Detection
Dataset: COCO
Metrics:
box AP: 44.8
- Task: Instance Segmentation
Dataset: COCO
Metrics:
mask AP: 39.9
Weights: https://download.openmmlab.com/mmdetection/v2.0/resnet_strikes_back/cascade_mask_rcnn_r50_fpn_rsb-pretrain_1x_coco/cascade_mask_rcnn_r50_fpn_rsb-pretrain_1x_coco_20220113_193636-8b9ad50f.pth
Paper:
URL: https://arxiv.org/abs/2110.00476
Title: 'ResNet strikes back: An improved training procedure in timm'
README: configs/resnet_strikes_back/README.md
Code:
URL: https://github.com/open-mmlab/mmdetection/blob/v2.22.0/configs/resnet_strikes_back/README.md
Version: v2.22.0
- Name: retinanet_r50_fpn_rsb-pretrain_1x_coco
In Collection: RetinaNet
Config: configs/resnet_strikes_back/retinanet_r50_fpn_rsb-pretrain_1x_coco.py
Metadata:
Training Memory (GB): 3.8
Epochs: 12
Training Data: COCO
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Resources: 8x V100 GPUs
Architecture:
- ResNet
Results:
- Task: Object Detection
Dataset: COCO
Metrics:
box AP: 39.0
Weights: https://download.openmmlab.com/mmdetection/v2.0/resnet_strikes_back/retinanet_r50_fpn_rsb-pretrain_1x_coco/retinanet_r50_fpn_rsb-pretrain_1x_coco_20220113_175432-bd24aae9.pth
Paper:
URL: https://arxiv.org/abs/2110.00476
Title: 'ResNet strikes back: An improved training procedure in timm'
README: configs/resnet_strikes_back/README.md
Code:
URL: https://github.com/open-mmlab/mmdetection/blob/v2.22.0/configs/resnet_strikes_back/README.md
Version: v2.22.0
- Name: mask_rcnn_r50_fpn_rsb-pretrain_1x_coco
In Collection: Mask R-CNN
Config: configs/resnet_strikes_back/mask_rcnn_r50_fpn_rsb-pretrain_1x_coco.py
Metadata:
Training Memory (GB): 4.5
Epochs: 12
Training Data: COCO
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Resources: 8x V100 GPUs
Architecture:
- ResNet
Results:
- Task: Object Detection
Dataset: COCO
Metrics:
box AP: 41.2
- Task: Instance Segmentation
Dataset: COCO
Metrics:
mask AP: 38.2
Weights: https://download.openmmlab.com/mmdetection/v2.0/resnet_strikes_back/mask_rcnn_r50_fpn_rsb-pretrain_1x_coco/mask_rcnn_r50_fpn_rsb-pretrain_1x_coco_20220113_174054-06ce8ba0.pth
Paper:
URL: https://arxiv.org/abs/2110.00476
Title: 'ResNet strikes back: An improved training procedure in timm'
README: configs/resnet_strikes_back/README.md
Code:
URL: https://github.com/open-mmlab/mmdetection/blob/v2.22.0/configs/resnet_strikes_back/README.md
Version: v2.22.0
| 4,313 | 35.871795 | 203 | yml |
mmdetection | mmdetection-master/configs/resnet_strikes_back/retinanet_r50_fpn_rsb-pretrain_1x_coco.py | _base_ = [
'../_base_/models/retinanet_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
checkpoint = 'https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb256-rsb-a1-600e_in1k_20211228-20e21305.pth' # noqa
model = dict(
backbone=dict(
init_cfg=dict(
type='Pretrained', prefix='backbone.', checkpoint=checkpoint)))
optimizer = dict(
_delete_=True,
type='AdamW',
lr=0.0001,
weight_decay=0.05,
paramwise_cfg=dict(norm_decay_mult=0., bypass_duplicate=True))
| 605 | 30.894737 | 135 | py |
mmdetection | mmdetection-master/configs/retinanet/README.md | # RetinaNet
> [Focal Loss for Dense Object Detection](https://arxiv.org/abs/1708.02002)
<!-- [ALGORITHM] -->
## Abstract
The highest accuracy object detectors to date are based on a two-stage approach popularized by R-CNN, where a classifier is applied to a sparse set of candidate object locations. In contrast, one-stage detectors that are applied over a regular, dense sampling of possible object locations have the potential to be faster and simpler, but have trailed the accuracy of two-stage detectors thus far. In this paper, we investigate why this is the case. We discover that the extreme foreground-background class imbalance encountered during training of dense detectors is the central cause. We propose to address this class imbalance by reshaping the standard cross entropy loss such that it down-weights the loss assigned to well-classified examples. Our novel Focal Loss focuses training on a sparse set of hard examples and prevents the vast number of easy negatives from overwhelming the detector during training. To evaluate the effectiveness of our loss, we design and train a simple dense detector we call RetinaNet. Our results show that when trained with the focal loss, RetinaNet is able to match the speed of previous one-stage detectors while surpassing the accuracy of all existing state-of-the-art two-stage detectors.
<div align=center>
<img src="https://user-images.githubusercontent.com/40661020/143973551-2b8e766a-1677-4f6d-953d-2e6d2a3c67b5.png" height="300"/>
</div>
## Results and Models
| Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download |
| :-------------: | :-----: | :----------: | :------: | :------------: | :----: | :-------------------------------------------------------------------------------------------------------------------: | :-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
| R-18-FPN | pytorch | 1x | 1.7 | | 31.7 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/retinanet/retinanet_r18_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r18_fpn_1x_coco/retinanet_r18_fpn_1x_coco_20220407_171055-614fd399.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r18_fpn_1x_coco/retinanet_r18_fpn_1x_coco_20220407_171055.log.json) |
| R-18-FPN | pytorch | 1x(1 x 8 BS) | 5.0 | | 31.7 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/retinanet/retinanet_r18_fpn_1x8_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r18_fpn_1x8_1x_coco/retinanet_r18_fpn_1x8_1x_coco_20220407_171255-4ea310d7.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r18_fpn_1x8_1x_coco/retinanet_r18_fpn_1x8_1x_coco_20220407_171255.log.json) |
| R-50-FPN | caffe | 1x | 3.5 | 18.6 | 36.3 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/retinanet/retinanet_r50_caffe_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r50_caffe_fpn_1x_coco/retinanet_r50_caffe_fpn_1x_coco_20200531-f11027c5.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r50_caffe_fpn_1x_coco/retinanet_r50_caffe_fpn_1x_coco_20200531_012518.log.json) |
| R-50-FPN | pytorch | 1x | 3.8 | 19.0 | 36.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/retinanet/retinanet_r50_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r50_fpn_1x_coco/retinanet_r50_fpn_1x_coco_20200130-c2398f9e.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r50_fpn_1x_coco/retinanet_r50_fpn_1x_coco_20200130_002941.log.json) |
| R-50-FPN (FP16) | pytorch | 1x | 2.8 | 31.6 | 36.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/retinanet/retinanet_r50_fpn_fp16_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/fp16/retinanet_r50_fpn_fp16_1x_coco/retinanet_r50_fpn_fp16_1x_coco_20200702-0dbfb212.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/fp16/retinanet_r50_fpn_fp16_1x_coco/retinanet_r50_fpn_fp16_1x_coco_20200702_020127.log.json) |
| R-50-FPN | pytorch | 2x | - | - | 37.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/retinanet/retinanet_r50_fpn_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r50_fpn_2x_coco/retinanet_r50_fpn_2x_coco_20200131-fdb43119.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r50_fpn_2x_coco/retinanet_r50_fpn_2x_coco_20200131_114738.log.json) |
| R-101-FPN | caffe | 1x | 5.5 | 14.7 | 38.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/retinanet/retinanet_r101_caffe_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r101_caffe_fpn_1x_coco/retinanet_r101_caffe_fpn_1x_coco_20200531-b428fa0f.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r101_caffe_fpn_1x_coco/retinanet_r101_caffe_fpn_1x_coco_20200531_012536.log.json) |
| R-101-FPN | pytorch | 1x | 5.7 | 15.0 | 38.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/retinanet/retinanet_r101_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r101_fpn_1x_coco/retinanet_r101_fpn_1x_coco_20200130-7a93545f.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r101_fpn_1x_coco/retinanet_r101_fpn_1x_coco_20200130_003055.log.json) |
| R-101-FPN | pytorch | 2x | - | - | 38.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/retinanet/retinanet_r101_fpn_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r101_fpn_2x_coco/retinanet_r101_fpn_2x_coco_20200131-5560aee8.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r101_fpn_2x_coco/retinanet_r101_fpn_2x_coco_20200131_114859.log.json) |
| X-101-32x4d-FPN | pytorch | 1x | 7.0 | 12.1 | 39.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/retinanet/retinanet_x101_32x4d_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_x101_32x4d_fpn_1x_coco/retinanet_x101_32x4d_fpn_1x_coco_20200130-5c8b7ec4.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_x101_32x4d_fpn_1x_coco/retinanet_x101_32x4d_fpn_1x_coco_20200130_003004.log.json) |
| X-101-32x4d-FPN | pytorch | 2x | - | - | 40.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/retinanet/retinanet_x101_32x4d_fpn_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_x101_32x4d_fpn_2x_coco/retinanet_x101_32x4d_fpn_2x_coco_20200131-237fc5e1.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_x101_32x4d_fpn_2x_coco/retinanet_x101_32x4d_fpn_2x_coco_20200131_114812.log.json) |
| X-101-64x4d-FPN | pytorch | 1x | 10.0 | 8.7 | 41.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/retinanet/retinanet_x101_64x4d_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_x101_64x4d_fpn_1x_coco/retinanet_x101_64x4d_fpn_1x_coco_20200130-366f5af1.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_x101_64x4d_fpn_1x_coco/retinanet_x101_64x4d_fpn_1x_coco_20200130_003008.log.json) |
| X-101-64x4d-FPN | pytorch | 2x | - | - | 40.8 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/retinanet/retinanet_x101_64x4d_fpn_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_x101_64x4d_fpn_2x_coco/retinanet_x101_64x4d_fpn_2x_coco_20200131-bca068ab.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_x101_64x4d_fpn_2x_coco/retinanet_x101_64x4d_fpn_2x_coco_20200131_114833.log.json) |
## Pre-trained Models
We also train some models with longer schedules and multi-scale training. The users could finetune them for downstream tasks.
| Backbone | Style | Lr schd | Mem (GB) | box AP | Config | Download |
| :-------------: | :-----: | :-----: | :------: | :----: | :-----------------------------------------------------------------------------------------------------------------------------------: | :-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
| R-50-FPN | pytorch | 3x | 3.5 | 39.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/retinanet/retinanet_r50_fpn_mstrain_640-800_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r50_fpn_mstrain_3x_coco/retinanet_r50_fpn_mstrain_3x_coco_20210718_220633-88476508.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r50_fpn_mstrain_3x_coco/retinanet_r50_fpn_mstrain_3x_coco_20210718_220633-88476508.log.json) |
| R-101-FPN | caffe | 3x | 5.4 | 40.7 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/retinanet/retinanet_r101_caffe_fpn_mstrain_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r101_caffe_fpn_mstrain_3x_coco/retinanet_r101_caffe_fpn_mstrain_3x_coco_20210721_063439-88a8a944.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r101_caffe_fpn_mstrain_3x_coco/retinanet_r101_caffe_fpn_mstrain_3x_coco_20210721_063439-88a8a944.log.json) |
| R-101-FPN | pytorch | 3x | 5.4 | 41 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/retinanet/retinanet_r101_fpn_mstrain_640-800_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r101_fpn_mstrain_3x_coco/retinanet_r101_fpn_mstrain_3x_coco_20210720_214650-7ee888e0.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r101_fpn_mstrain_3x_coco/retinanet_r101_fpn_mstrain_3x_coco_20210720_214650-7ee888e0.log.json) |
| X-101-64x4d-FPN | pytorch | 3x | 9.8 | 41.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/retinanet/retinanet_x101_64x4d_fpn_mstrain_640-800_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_x101_64x4d_fpn_mstrain_3x_coco/retinanet_x101_64x4d_fpn_mstrain_3x_coco_20210719_051838-022c2187.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_x101_64x4d_fpn_mstrain_3x_coco/retinanet_x101_64x4d_fpn_mstrain_3x_coco_20210719_051838-022c2187.log.json) |
## Citation
```latex
@inproceedings{lin2017focal,
title={Focal loss for dense object detection},
author={Lin, Tsung-Yi and Goyal, Priya and Girshick, Ross and He, Kaiming and Doll{\'a}r, Piotr},
booktitle={Proceedings of the IEEE international conference on computer vision},
year={2017}
}
```
| 13,116 | 241.907407 | 1,226 | md |
mmdetection | mmdetection-master/configs/retinanet/ascend_retinanet_r18_fpn_1x8_1x_coco.py | _base_ = [
'../_base_/models/ascend_retinanet_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
# data
data = dict(samples_per_gpu=8)
# optimizer
model = dict(
backbone=dict(
depth=18,
init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet18')),
neck=dict(in_channels=[64, 128, 256, 512]))
# Note: If the learning rate is set to 0.0025, the mAP will be 32.4.
optimizer = dict(type='SGD', lr=0.005, momentum=0.9, weight_decay=0.0001)
# NOTE: `auto_scale_lr` is for automatically scaling LR,
# USER SHOULD NOT CHANGE ITS VALUES.
# base_batch_size = (1 GPUs) x (8 samples per GPU)
auto_scale_lr = dict(base_batch_size=8)
| 743 | 30 | 79 | py |
mmdetection | mmdetection-master/configs/retinanet/metafile.yml | Collections:
- Name: RetinaNet
Metadata:
Training Data: COCO
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Resources: 8x V100 GPUs
Architecture:
- Focal Loss
- FPN
- ResNet
Paper:
URL: https://arxiv.org/abs/1708.02002
Title: "Focal Loss for Dense Object Detection"
README: configs/retinanet/README.md
Code:
URL: https://github.com/open-mmlab/mmdetection/blob/v2.0.0/mmdet/models/detectors/retinanet.py#L6
Version: v2.0.0
Models:
- Name: retinanet_r18_fpn_1x_coco
In Collection: RetinaNet
Config: configs/retinanet/retinanet_r18_fpn_1x_coco.py
Metadata:
Training Memory (GB): 1.7
Training Resources: 8x V100 GPUs
Epochs: 12
Results:
- Task: Object Detection
Dataset: COCO
Metrics:
box AP: 31.7
Weights: https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r18_fpn_1x_coco/retinanet_r18_fpn_1x_coco_20220407_171055-614fd399.pth
- Name: retinanet_r18_fpn_1x8_1x_coco
In Collection: RetinaNet
Config: configs/retinanet/retinanet_r18_fpn_1x8_1x_coco.py
Metadata:
Training Memory (GB): 5.0
Training Resources: 1x V100 GPUs
Epochs: 12
Results:
- Task: Object Detection
Dataset: COCO
Metrics:
box AP: 31.7
Weights: https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r18_fpn_1x8_1x_coco/retinanet_r18_fpn_1x8_1x_coco_20220407_171255-4ea310d7.pth
- Name: retinanet_r50_caffe_fpn_1x_coco
In Collection: RetinaNet
Config: configs/retinanet/retinanet_r50_caffe_fpn_1x_coco.py
Metadata:
Training Memory (GB): 3.5
inference time (ms/im):
- value: 53.76
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (800, 1333)
Epochs: 12
Results:
- Task: Object Detection
Dataset: COCO
Metrics:
box AP: 36.3
Weights: https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r50_caffe_fpn_1x_coco/retinanet_r50_caffe_fpn_1x_coco_20200531-f11027c5.pth
- Name: retinanet_r50_fpn_1x_coco
In Collection: RetinaNet
Config: configs/retinanet/retinanet_r50_fpn_1x_coco.py
Metadata:
Training Memory (GB): 3.8
inference time (ms/im):
- value: 52.63
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (800, 1333)
Epochs: 12
Results:
- Task: Object Detection
Dataset: COCO
Metrics:
box AP: 36.5
Weights: https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r50_fpn_1x_coco/retinanet_r50_fpn_1x_coco_20200130-c2398f9e.pth
- Name: retinanet_r50_fpn_fp16_1x_coco
In Collection: RetinaNet
Config: configs/retinanet/retinanet_r50_fpn_fp16_1x_coco.py
Metadata:
Training Memory (GB): 2.8
Training Techniques:
- SGD with Momentum
- Weight Decay
- Mixed Precision Training
inference time (ms/im):
- value: 31.65
hardware: V100
backend: PyTorch
batch size: 1
mode: FP16
resolution: (800, 1333)
Epochs: 12
Results:
- Task: Object Detection
Dataset: COCO
Metrics:
box AP: 36.4
Weights: https://download.openmmlab.com/mmdetection/v2.0/fp16/retinanet_r50_fpn_fp16_1x_coco/retinanet_r50_fpn_fp16_1x_coco_20200702-0dbfb212.pth
- Name: retinanet_r50_fpn_2x_coco
In Collection: RetinaNet
Config: configs/retinanet/retinanet_r50_fpn_2x_coco.py
Metadata:
Epochs: 24
Results:
- Task: Object Detection
Dataset: COCO
Metrics:
box AP: 37.4
Weights: https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r50_fpn_2x_coco/retinanet_r50_fpn_2x_coco_20200131-fdb43119.pth
- Name: retinanet_r50_fpn_mstrain_640-800_3x_coco
In Collection: RetinaNet
Config: configs/retinanet/retinanet_r50_fpn_mstrain_640-800_3x_coco.py
Metadata:
Epochs: 36
Results:
- Task: Object Detection
Dataset: COCO
Metrics:
box AP: 39.5
Weights: https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r50_fpn_mstrain_3x_coco/retinanet_r50_fpn_mstrain_3x_coco_20210718_220633-88476508.pth
- Name: retinanet_r101_caffe_fpn_1x_coco
In Collection: RetinaNet
Config: configs/retinanet/retinanet_r101_caffe_fpn_1x_coco.py
Metadata:
Training Memory (GB): 5.5
inference time (ms/im):
- value: 68.03
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (800, 1333)
Epochs: 12
Results:
- Task: Object Detection
Dataset: COCO
Metrics:
box AP: 38.5
Weights: https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r101_caffe_fpn_1x_coco/retinanet_r101_caffe_fpn_1x_coco_20200531-b428fa0f.pth
- Name: retinanet_r101_caffe_fpn_mstrain_3x_coco
In Collection: RetinaNet
Config: configs/retinanet/retinanet_r101_caffe_fpn_mstrain_3x_coco.py
Metadata:
Epochs: 36
Results:
- Task: Object Detection
Dataset: COCO
Metrics:
box AP: 40.7
Weights: https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r101_caffe_fpn_mstrain_3x_coco/retinanet_r101_caffe_fpn_mstrain_3x_coco_20210721_063439-88a8a944.pth
- Name: retinanet_r101_fpn_1x_coco
In Collection: RetinaNet
Config: configs/retinanet/retinanet_r101_fpn_1x_coco.py
Metadata:
Training Memory (GB): 5.7
inference time (ms/im):
- value: 66.67
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (800, 1333)
Epochs: 12
Results:
- Task: Object Detection
Dataset: COCO
Metrics:
box AP: 38.5
Weights: https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r101_fpn_1x_coco/retinanet_r101_fpn_1x_coco_20200130-7a93545f.pth
- Name: retinanet_r101_fpn_2x_coco
In Collection: RetinaNet
Config: configs/retinanet/retinanet_r101_fpn_2x_coco.py
Metadata:
Training Memory (GB): 5.7
inference time (ms/im):
- value: 66.67
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (800, 1333)
Epochs: 24
Results:
- Task: Object Detection
Dataset: COCO
Metrics:
box AP: 38.9
Weights: https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r101_fpn_2x_coco/retinanet_r101_fpn_2x_coco_20200131-5560aee8.pth
- Name: retinanet_r101_fpn_mstrain_640-800_3x_coco
In Collection: RetinaNet
Config: configs/retinanet/retinanet_r101_fpn_mstrain_640-800_3x_coco.py
Metadata:
Epochs: 36
Results:
- Task: Object Detection
Dataset: COCO
Metrics:
box AP: 41
Weights: https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r101_fpn_mstrain_3x_coco/retinanet_r101_fpn_mstrain_3x_coco_20210720_214650-7ee888e0.pth
- Name: retinanet_x101_32x4d_fpn_1x_coco
In Collection: RetinaNet
Config: configs/retinanet/retinanet_x101_32x4d_fpn_1x_coco.py
Metadata:
Training Memory (GB): 7.0
inference time (ms/im):
- value: 82.64
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (800, 1333)
Epochs: 12
Results:
- Task: Object Detection
Dataset: COCO
Metrics:
box AP: 39.9
Weights: https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_x101_32x4d_fpn_1x_coco/retinanet_x101_32x4d_fpn_1x_coco_20200130-5c8b7ec4.pth
- Name: retinanet_x101_32x4d_fpn_2x_coco
In Collection: RetinaNet
Config: configs/retinanet/retinanet_x101_32x4d_fpn_2x_coco.py
Metadata:
Training Memory (GB): 7.0
inference time (ms/im):
- value: 82.64
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (800, 1333)
Epochs: 24
Results:
- Task: Object Detection
Dataset: COCO
Metrics:
box AP: 40.1
Weights: https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_x101_32x4d_fpn_2x_coco/retinanet_x101_32x4d_fpn_2x_coco_20200131-237fc5e1.pth
- Name: retinanet_x101_64x4d_fpn_1x_coco
In Collection: RetinaNet
Config: configs/retinanet/retinanet_x101_64x4d_fpn_1x_coco.py
Metadata:
Training Memory (GB): 10.0
inference time (ms/im):
- value: 114.94
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (800, 1333)
Epochs: 12
Results:
- Task: Object Detection
Dataset: COCO
Metrics:
box AP: 41.0
Weights: https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_x101_64x4d_fpn_1x_coco/retinanet_x101_64x4d_fpn_1x_coco_20200130-366f5af1.pth
- Name: retinanet_x101_64x4d_fpn_2x_coco
In Collection: RetinaNet
Config: configs/retinanet/retinanet_x101_64x4d_fpn_2x_coco.py
Metadata:
Training Memory (GB): 10.0
inference time (ms/im):
- value: 114.94
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (800, 1333)
Epochs: 24
Results:
- Task: Object Detection
Dataset: COCO
Metrics:
box AP: 40.8
Weights: https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_x101_64x4d_fpn_2x_coco/retinanet_x101_64x4d_fpn_2x_coco_20200131-bca068ab.pth
- Name: retinanet_x101_64x4d_fpn_mstrain_640-800_3x_coco
In Collection: RetinaNet
Config: configs/retinanet/retinanet_x101_64x4d_fpn_mstrain_640-800_3x_coco.py
Metadata:
Epochs: 36
Results:
- Task: Object Detection
Dataset: COCO
Metrics:
box AP: 41.6
Weights: https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_x101_64x4d_fpn_mstrain_3x_coco/retinanet_x101_64x4d_fpn_mstrain_3x_coco_20210719_051838-022c2187.pth
| 10,372 | 32.140575 | 181 | yml |
mmdetection | mmdetection-master/configs/retinanet/retinanet_r101_caffe_fpn_1x_coco.py | _base_ = './retinanet_r50_caffe_fpn_1x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(
type='Pretrained',
checkpoint='open-mmlab://detectron2/resnet101_caffe')))
| 222 | 26.875 | 67 | py |
mmdetection | mmdetection-master/configs/retinanet/retinanet_r101_caffe_fpn_mstrain_3x_coco.py | _base_ = './retinanet_r50_caffe_fpn_mstrain_1x_coco.py'
# learning policy
model = dict(
pretrained='open-mmlab://detectron2/resnet101_caffe',
backbone=dict(depth=101))
lr_config = dict(step=[28, 34])
runner = dict(type='EpochBasedRunner', max_epochs=36)
| 262 | 31.875 | 57 | py |
mmdetection | mmdetection-master/configs/retinanet/retinanet_r101_fpn_1x_coco.py | _base_ = './retinanet_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 197 | 27.285714 | 61 | py |
mmdetection | mmdetection-master/configs/retinanet/retinanet_r101_fpn_2x_coco.py | _base_ = './retinanet_r50_fpn_2x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 197 | 27.285714 | 61 | py |
mmdetection | mmdetection-master/configs/retinanet/retinanet_r101_fpn_mstrain_640-800_3x_coco.py | _base_ = [
'../_base_/models/retinanet_r50_fpn.py', '../common/mstrain_3x_coco.py'
]
# optimizer
model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101))
optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
| 251 | 35 | 76 | py |
mmdetection | mmdetection-master/configs/retinanet/retinanet_r18_fpn_1x8_1x_coco.py | _base_ = [
'../_base_/models/retinanet_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
# data
data = dict(samples_per_gpu=8)
# optimizer
model = dict(
backbone=dict(
depth=18,
init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet18')),
neck=dict(in_channels=[64, 128, 256, 512]))
# Note: If the learning rate is set to 0.0025, the mAP will be 32.4.
optimizer = dict(type='SGD', lr=0.005, momentum=0.9, weight_decay=0.0001)
# NOTE: `auto_scale_lr` is for automatically scaling LR,
# USER SHOULD NOT CHANGE ITS VALUES.
# base_batch_size = (1 GPUs) x (8 samples per GPU)
auto_scale_lr = dict(base_batch_size=8)
| 736 | 29.708333 | 79 | py |
mmdetection | mmdetection-master/configs/retinanet/retinanet_r18_fpn_1x_coco.py | _base_ = [
'../_base_/models/retinanet_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
# optimizer
model = dict(
backbone=dict(
depth=18,
init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet18')),
neck=dict(in_channels=[64, 128, 256, 512]))
optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
# NOTE: `auto_scale_lr` is for automatically scaling LR,
# USER SHOULD NOT CHANGE ITS VALUES.
# base_batch_size = (8 GPUs) x (2 samples per GPU)
auto_scale_lr = dict(base_batch_size=16)
| 627 | 32.052632 | 79 | py |
mmdetection | mmdetection-master/configs/retinanet/retinanet_r50_caffe_fpn_1x_coco.py | _base_ = './retinanet_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
norm_cfg=dict(requires_grad=False),
norm_eval=True,
style='caffe',
init_cfg=dict(
type='Pretrained',
checkpoint='open-mmlab://detectron2/resnet50_caffe')))
# use caffe img_norm
img_norm_cfg = dict(
mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True),
dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
dict(type='RandomFlip', flip_ratio=0.5),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(1333, 800),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='RandomFlip'),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img']),
])
]
data = dict(
train=dict(pipeline=train_pipeline),
val=dict(pipeline=test_pipeline),
test=dict(pipeline=test_pipeline))
| 1,408 | 32.547619 | 72 | py |
mmdetection | mmdetection-master/configs/retinanet/retinanet_r50_caffe_fpn_mstrain_1x_coco.py | _base_ = './retinanet_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
norm_cfg=dict(requires_grad=False),
norm_eval=True,
style='caffe',
init_cfg=dict(
type='Pretrained',
checkpoint='open-mmlab://detectron2/resnet50_caffe')))
# use caffe img_norm
img_norm_cfg = dict(
mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True),
dict(
type='Resize',
img_scale=[(1333, 640), (1333, 672), (1333, 704), (1333, 736),
(1333, 768), (1333, 800)],
multiscale_mode='value',
keep_ratio=True),
dict(type='RandomFlip', flip_ratio=0.5),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(1333, 800),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='RandomFlip'),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img']),
])
]
data = dict(
train=dict(pipeline=train_pipeline),
val=dict(pipeline=test_pipeline),
test=dict(pipeline=test_pipeline))
| 1,552 | 32.042553 | 72 | py |
mmdetection | mmdetection-master/configs/retinanet/retinanet_r50_caffe_fpn_mstrain_2x_coco.py | _base_ = './retinanet_r50_caffe_fpn_mstrain_1x_coco.py'
# learning policy
lr_config = dict(step=[16, 23])
runner = dict(type='EpochBasedRunner', max_epochs=24)
| 160 | 31.2 | 55 | py |
mmdetection | mmdetection-master/configs/retinanet/retinanet_r50_caffe_fpn_mstrain_3x_coco.py | _base_ = './retinanet_r50_caffe_fpn_mstrain_1x_coco.py'
# learning policy
lr_config = dict(step=[28, 34])
runner = dict(type='EpochBasedRunner', max_epochs=36)
| 160 | 31.2 | 55 | py |
mmdetection | mmdetection-master/configs/retinanet/retinanet_r50_fpn_1x_coco.py | _base_ = [
'../_base_/models/retinanet_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
# optimizer
optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
| 260 | 31.625 | 72 | py |
mmdetection | mmdetection-master/configs/retinanet/retinanet_r50_fpn_2x_coco.py | _base_ = './retinanet_r50_fpn_1x_coco.py'
# learning policy
lr_config = dict(step=[16, 22])
runner = dict(type='EpochBasedRunner', max_epochs=24)
| 146 | 28.4 | 53 | py |
mmdetection | mmdetection-master/configs/retinanet/retinanet_r50_fpn_90k_coco.py | _base_ = 'retinanet_r50_fpn_1x_coco.py'
# learning policy
lr_config = dict(
policy='step',
warmup='linear',
warmup_iters=500,
warmup_ratio=0.001,
step=[60000, 80000])
# Runner type
runner = dict(_delete_=True, type='IterBasedRunner', max_iters=90000)
checkpoint_config = dict(interval=10000)
evaluation = dict(interval=10000, metric='bbox')
| 364 | 21.8125 | 69 | py |
mmdetection | mmdetection-master/configs/retinanet/retinanet_r50_fpn_fp16_1x_coco.py | _base_ = './retinanet_r50_fpn_1x_coco.py'
# fp16 settings
fp16 = dict(loss_scale=512.)
# set grad_norm for stability during mixed-precision training
optimizer_config = dict(
_delete_=True, grad_clip=dict(max_norm=35, norm_type=2))
| 236 | 28.625 | 61 | py |
mmdetection | mmdetection-master/configs/retinanet/retinanet_r50_fpn_mstrain_640-800_3x_coco.py | _base_ = [
'../_base_/models/retinanet_r50_fpn.py', '../common/mstrain_3x_coco.py'
]
# optimizer
optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
| 174 | 28.166667 | 75 | py |
mmdetection | mmdetection-master/configs/retinanet/retinanet_x101_32x4d_fpn_1x_coco.py | _base_ = './retinanet_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
type='ResNeXt',
depth=101,
groups=32,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
style='pytorch',
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://resnext101_32x4d')))
| 419 | 27 | 76 | py |
mmdetection | mmdetection-master/configs/retinanet/retinanet_x101_32x4d_fpn_2x_coco.py | _base_ = './retinanet_r50_fpn_2x_coco.py'
model = dict(
backbone=dict(
type='ResNeXt',
depth=101,
groups=32,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
style='pytorch',
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://resnext101_32x4d')))
| 419 | 27 | 76 | py |
mmdetection | mmdetection-master/configs/retinanet/retinanet_x101_64x4d_fpn_1x_coco.py | _base_ = './retinanet_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
type='ResNeXt',
depth=101,
groups=64,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
style='pytorch',
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d')))
| 419 | 27 | 76 | py |
mmdetection | mmdetection-master/configs/retinanet/retinanet_x101_64x4d_fpn_2x_coco.py | _base_ = './retinanet_r50_fpn_2x_coco.py'
model = dict(
backbone=dict(
type='ResNeXt',
depth=101,
groups=64,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
style='pytorch',
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d')))
| 419 | 27 | 76 | py |
mmdetection | mmdetection-master/configs/retinanet/retinanet_x101_64x4d_fpn_mstrain_640-800_3x_coco.py | _base_ = [
'../_base_/models/retinanet_r50_fpn.py', '../common/mstrain_3x_coco.py'
]
# optimizer
model = dict(
pretrained='open-mmlab://resnext101_64x4d',
backbone=dict(type='ResNeXt', depth=101, groups=64, base_width=4))
optimizer = dict(type='SGD', lr=0.01)
| 272 | 29.333333 | 75 | py |
mmdetection | mmdetection-master/configs/rfnext/README.md | # RF-Next: Efficient Receptive Field Search for CNN
> [RF-Next: Efficient Receptive Field Search for Convolutional Neural Networks](http://mftp.mmcheng.net/Papers/22TPAMI-ActionSeg.pdf)
<!-- [ALGORITHM] -->
## Abstract
Temporal/spatial receptive fields of models play an important role in sequential/spatial tasks. Large receptive fields facilitate long-term relations, while small receptive fields help to capture the local details. Existing methods construct models with hand-designed receptive fields in layers. Can we effectively search for receptive field combinations to replace hand-designed patterns? To answer this question, we propose to find better receptive field combinations through a global-to-local search scheme. Our search scheme exploits both global search to find the coarse combinations and local search to get the refined receptive field combinations further. The global search finds possible coarse combinations other than human-designed patterns. On top of the global search, we propose an expectation-guided iterative local search scheme to refine combinations effectively. Our RF-Next models, plugging receptive field search to various models, boost the performance on many tasks, e.g., temporal action segmentation, object detection, instance segmentation, and speech synthesis.
The source code is publicly available on [http://mmcheng.net/rfnext](http://mmcheng.net/rfnext).
## Results and Models
### ConvNext on COCO
| Backbone | Method | RFNext | Lr Schd | box mAP | mask mAP | Config | Download |
| :-----------: | :----------------: | :-------------: | :-----: | :-----: | :------: | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
| ConvNeXt-T | Cascade Mask R-CNN | NO | 3x | 50.3 | 43.6 | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/convnext/mask_rcnn_convnext-t_p4_w7_fpn_fp16_ms-crop_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/convnext/mask_rcnn_convnext-t_p4_w7_fpn_fp16_ms-crop_3x_coco/mask_rcnn_convnext-t_p4_w7_fpn_fp16_ms-crop_3x_coco_20220426_154953-050731f4.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/convnext/mask_rcnn_convnext-t_p4_w7_fpn_fp16_ms-crop_3x_coco/mask_rcnn_convnext-t_p4_w7_fpn_fp16_ms-crop_3x_coco_20220426_154953.log.json) |
| RF-ConvNeXt-T | Cascade Mask R-CNN | Single-Branch | 3x | 50.6 | 44.0 | [search](https://github.com/open-mmlab/mmdetection/tree/master/configs/rfnext/rfnext_search_cascade_mask_rcnn_convnext-t_p4_w7_fpn_giou_4conv1f_fp16_ms-crop_3x_coco.py) [retrain](https://github.com/open-mmlab/mmdetection/tree/master/configs/rfnext/rfnext_fixed_single_branch_cascade_mask_rcnn_convnext-t_p4_w7_fpn_giou_4conv1f_fp16_ms-crop_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/rfnext/rfnext_fixed_single_branch_cascade_mask_rcnn_convnext_tiny_patch4_window7_mstrain_480-800_giou_4conv1f_adamw_3x_coco_in1k/rfnext_fixed_single_branch_cascade_mask_rcnn_convnext_tiny_patch4_window7_mstrain_480-800_giou_4conv1f_adamw_3x_coco_in1k-71aeb991.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/rfnext/rfnext_fixed_single_branch_cascade_mask_rcnn_convnext_tiny_patch4_window7_mstrain_480-800_giou_4conv1f_adamw_3x_coco_in1k/rfnext_fixed_single_branch_cascade_mask_rcnn_convnext_tiny_patch4_window7_mstrain_480-800_giou_4conv1f_adamw_3x_coco_in1k_20220131_091748.log.json) |
| RF-ConvNeXt-T | Cascade Mask R-CNN | Multiple-Branch | 3x | 50.9 | 44.3 | [search](https://github.com/open-mmlab/mmdetection/tree/master/configs/rfnext/rfnext_search_cascade_mask_rcnn_convnext-t_p4_w7_fpn_giou_4conv1f_fp16_ms-crop_3x_coco.py) [retrain](https://github.com/open-mmlab/mmdetection/tree/master/configs/rfnext/rfnext_fixed_multi_branch_cascade_mask_rcnn_convnext-t_p4_w7_fpn_giou_4conv1f_fp16_ms-crop_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/rfnext/rfnext_search_cascade_mask_rcnn_convnext_tiny_patch4_window7_mstrain_480-800_giou_4conv1f_adamw_3x_coco_in1k/rfnext_search_cascade_mask_rcnn_convnext_tiny_patch4_window7_mstrain_480-800_giou_4conv1f_adamw_3x_coco_in1k-f47db42b.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/rfnext/rfnext_search_cascade_mask_rcnn_convnext_tiny_patch4_window7_mstrain_480-800_giou_4conv1f_adamw_3x_coco_in1k/rfnext_search_cascade_mask_rcnn_convnext_tiny_patch4_window7_mstrain_480-800_giou_4conv1f_adamw_3x_coco_in1k_20220128_200900.log.json) |
### PVTv2 on COCO
| Backbone | Method | RFNext | Lr Schd | box mAP | mask mAP | Config | Download |
| :---------: | :--------: | :-------------: | :-----: | :-----: | :------: | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
| PVTv2-b0 | Mask R-CNN | NO | 1x | 38.2 | 36.2 | - | - |
| RF-PVTv2-b0 | Mask R-CNN | Single-Branch | 1x | 38.9 | 36.8 | [search](https://github.com/open-mmlab/mmdetection/tree/master/configs/rfnext/rfnext_search_mask_rcnn_pvtv2-b0_fpn_1x_coco.py) [retrain](https://github.com/open-mmlab/mmdetection/tree/master/configs/rfnext/rfnext_fixed_single_branch_mask_rcnn_pvtv2-b0_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/rfnext/rfnext_fixed_single_branch_mask_rcnn_pvtv2-b0_fpn_1x_coco/rfnext_fixed_single_branch_mask_rcnn_pvtv2-b0_fpn_1x_coco-7b25d72e.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/rfnext/rfnext_fixed_single_branch_mask_rcnn_pvtv2-b0_fpn_1x_coco/rfnext_fixed_single_branch_mask_rcnn_pvtv2-b0_fpn_1x_coco_20221120_213845.log.json) |
| RF-PVTv2-b0 | Mask R-CNN | Multiple-Branch | 1x | 39.3 | 37.1 | [search](https://github.com/open-mmlab/mmdetection/tree/master/configs/rfnext/rfnext_search_mask_rcnn_pvtv2-b0_fpn_1x_coco.py) [retrain](https://github.com/open-mmlab/mmdetection/tree/master/configs/rfnext/rfnext_fixed_multi_branch_mask_rcnn_pvtv2-b0_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/rfnext/rfnext_search_mask_rcnn_pvtv2-b0_fpn_1x_coco/rfnext_search_mask_rcnn_pvtv2-b0_fpn_1x_coco-dc8fd5de.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/rfnext/rfnext_search_mask_rcnn_pvtv2-b0_fpn_1x_coco/rfnext_search_mask_rcnn_pvtv2-b0_fpn_1x_coco_20221119_204703.log.json) |
The results of PVTv2-b0 are from [PVT](https://github.com/whai362/PVT/tree/v2/detection).
### Res2Net on COCO
| Backbone | Method | RFNext | Lr Schd | box mAP | mask mAP | Config | Download |
| :------------: | :----------------: | :-------------: | :-----: | :-----: | :------: | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
| Res2Net-101 | Cascade Mask R-CNN | NO | 20e | 46.4 | 40.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/res2net/cascade_mask_rcnn_r2_101_fpn_20e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/res2net/cascade_mask_rcnn_r2_101_fpn_20e_coco/cascade_mask_rcnn_r2_101_fpn_20e_coco-8a7b41e1.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/res2net/cascade_mask_rcnn_r2_101_fpn_20e_coco/cascade_mask_rcnn_r2_101_fpn_20e_coco_20200515_091645.log.json) |
| RF-Res2Net-101 | Cascade Mask R-CNN | Single-Branch | 20e | 46.9 | 40.7 | [search](https://github.com/open-mmlab/mmdetection/tree/master/configs/rfnext/rfnext_search_cascade_mask_rcnn_r2_101_fpn_20e_coco.py) [retrain](https://github.com/open-mmlab/mmdetection/tree/master/configs/rfnext/rfnext_fixed_single_branch_cascade_mask_rcnn_r2_101_fpn_20e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/rfnext/rfnext_fixed_single_branch_cascade_mask_rcnn_r2_101_fpn_20e_coco/rfnext_fixed_single_branch_cascade_mask_rcnn_r2_101_fpn_20e_coco-e22d5257.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/rfnext/rfnext_fixed_single_branch_cascade_mask_rcnn_r2_101_fpn_20e_coco/rfnext_fixed_single_branch_cascade_mask_rcnn_r2_101_fpn_20e_coco_20220402_141321.log.json) |
| RF-Res2Net-101 | Cascade Mask R-CNN | Multiple-Branch | 20e | 47.9 | 41.5 | [search](https://github.com/open-mmlab/mmdetection/tree/master/configs/rfnext/rfnext_search_cascade_mask_rcnn_r2_101_fpn_20e_coco.py) [retrain](https://github.com/open-mmlab/mmdetection/tree/master/configs/rfnext/rfnext_fixed_multi_branch_cascade_mask_rcnn_r2_101_fpn_20e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/rfnext/rfnext_search_cascade_mask_rcnn_r2_101_fpn_20e_coco/rfnext_search_cascade_mask_rcnn_r2_101_fpn_20e_coco-e17510a0.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/rfnext/rfnext_search_cascade_mask_rcnn_r2_101_fpn_20e_coco/rfnext_search_cascade_mask_rcnn_r2_101_fpn_20e_coco_20220327_221419.log.json) |
### HRNet on COCO
| Backbone | Method | RFNext | Lr Schd | box mAP | mask mAP | Config | Download |
| :-------------: | :----------------: | :-------------: | :-----: | :-----: | :------: | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
| HRNetV2p-W18 | Cascade Mask R-CNN | NO | 20e | 41.6 | 36.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/cascade_mask_rcnn_hrnetv2p_w18_20e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/hrnet/cascade_mask_rcnn_hrnetv2p_w18_20e_coco/cascade_mask_rcnn_hrnetv2p_w18_20e_coco_20200210-b543cd2b.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/hrnet/cascade_mask_rcnn_hrnetv2p_w18_20e_coco/cascade_mask_rcnn_hrnetv2p_w18_20e_coco_20200210_093149.log.json) |
| RF-HRNetV2p-W18 | Cascade Mask R-CNN | Single-Branch | 20e | 43.0 | 37.6 | [search](https://github.com/open-mmlab/mmdetection/tree/master/configs/rfnext/rfsearched_search_cascade_mask_rcnn_hrnetv2p_w18_20e_coco.py) [retrain](https://github.com/open-mmlab/mmdetection/tree/master/configs/rfnext/rfsearched_fixed_single_branch_cascade_mask_rcnn_hrnetv2p_w18_20e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/rfnext/rfnext_fixed_single_branch_cascade_mask_rcnn_hrnetv2p_w18_20e_coco/rfnext_fixed_single_branch_cascade_mask_rcnn_hrnetv2p_w18_20e_coco-682f121d.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/rfnext/rfnext_fixed_single_branch_cascade_mask_rcnn_hrnetv2p_w18_20e_coco/rfnext_fixed_single_branch_cascade_mask_rcnn_hrnetv2p_w18_20e_coco_20221118_141400.log.json) |
| RF-HRNetV2p-W18 | Cascade Mask R-CNN | Multiple-Branch | 20e | 43.7 | 38.2 | [search](https://github.com/open-mmlab/mmdetection/tree/master/configs/rfnext/rfsearched_search_cascade_mask_rcnn_hrnetv2p_w18_20e_coco.py) [retrain](https://github.com/open-mmlab/mmdetection/tree/master/configs/rfnext/rfsearched_fixed_multi_branch_cascade_mask_rcnn_hrnetv2p_w18_20e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/rfnext/rfnext_search_cascade_mask_rcnn_hrnetv2p_w18_20e_coco/rfnext_search_cascade_mask_rcnn_hrnetv2p_w18_20e_coco-7b9c7885.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/rfnext/rfnext_search_cascade_mask_rcnn_hrnetv2p_w18_20e_coco/rfnext_search_cascade_mask_rcnn_hrnetv2p_w18_20e_coco_20221115_230113.log.json) |
Note: the performance of multi-branch models listed above are evaluated during searching to save computional cost, retraining would achieve similar or better performance.
### Res2Net on COCO panoptic
| Backbone | Method | RFNext | Lr schd | PQ | SQ | RQ | Config | Download |
| :-----------: | :----------: | :-------------: | :-----: | :--: | :--: | :--: | :-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
| Res2Net-50 | Panoptic FPN | NO | 1x | 42.5 | 78.0 | 51.8 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/panoptic_fpn/panoptic_fpn_r2_50_fpn_fp16_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/rfnext/panoptic_fpn_r2_50_fpn_fp16_1x_coco/panoptic_fpn_r2_50_fpn_fp16_1x_coco-fa6c51f0.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/rfnext/panoptic_fpn_r2_50_fpn_fp16_1x_coco/panoptic_fpn_r2_50_fpn_fp16_1x_coco_20221114_224729.log.json) |
| RF-Res2Net-50 | Panoptic FPN | Single-Branch | 1x | 44.0 | 78.7 | 53.6 | [search](https://github.com/open-mmlab/mmdetection/tree/master/configs/rfnext/rfnext_search_panoptic_fpn_r2_50_fpn_fp16_1x_coco.py) [retrain](https://github.com/open-mmlab/mmdetection/tree/master/configs/rfnext/rfnext_fixed_single_branch_panoptic_fpn_r2_50_fpn_fp16_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/rfnext/rfnext_fixed_single_branch_panoptic_fpn_r2_50_fpn_fp16_1x_coco/rfnext_fixed_single_branch_panoptic_fpn_r2_50_fpn_fp16_1x_coco-52181d5b.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/rfnext/rfnext_fixed_single_branch_panoptic_fpn_r2_50_fpn_fp16_1x_coco/rfnext_fixed_single_branch_panoptic_fpn_r2_50_fpn_fp16_1x_coco_20221115_152436.log.json) |
| RF-Res2Net-50 | Panoptic FPN | Multiple-Branch | 1x | 44.3 | 79.0 | 53.9 | [search](https://github.com/open-mmlab/mmdetection/tree/master/configs/rfnext/rfnext_search_panoptic_fpn_r2_50_fpn_fp16_1x_coco.py) [retrain](https://github.com/open-mmlab/mmdetection/tree/master/configs/rfnext/rfnext_fixed_multi_branch_panoptic_fpn_r2_50_fpn_fp16_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/rfnext/rfnext_search_panoptic_fpn_r2_50_fpn_fp16_1x_coco/rfnext_search_panoptic_fpn_r2_50_fpn_fp16_1x_coco-34a893a0.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/rfnext/rfnext_search_panoptic_fpn_r2_50_fpn_fp16_1x_coco/rfnext_search_panoptic_fpn_r2_50_fpn_fp16_1x_coco_20221114_224722.log.json) |
## Configs
If you want to search receptive fields on an existing model, you need to define a `RFSearchHook` in the `custom_hooks` of config file.
```python
custom_hooks = [
dict(
type='RFSearchHook',
mode='search',
rfstructure_file=None,
verbose=True,
by_epoch=True,
config=dict(
search=dict(
step=0,
max_step=11,
search_interval=1,
exp_rate=0.5,
init_alphas=0.01,
mmin=1,
mmax=24,
num_branches=2,
skip_layer=[]))
),
]
```
Arguments:
- `max_step`: The maximum number of steps to update the structures.
- `search_interval`: The interval (epoch) between two updates.
- `exp_rate`: The controller of the sparsity of search space. For a conv with an initial dilation rate of `D`, dilation rates will be sampled with an interval of `exp_rate * D`.
- `num_branches`: The controller of the size of search space (the number of branches). If you set `S=3`, the dilations are `[D - exp_rate * D, D, D + exp_rate * D]` for three branches. If you set `num_branches=2`, the dilations are `[D - exp_rate * D, D + exp_rate * D]`. With `num_branches=2`, you can achieve similar performance with less MEMORY and FLOPS.
- `skip_layer`: The modules in skip_layer will be ignored during the receptive field search.
## Training
### 1. Searching Jobs
You can launch searching jobs by using config files with prefix `rfnext_search`. The json files of searched structures will be saved to `work_dir`.
If you want to further search receptive fields upon a searched structure, please set `rfsearch_cfg.rfstructure_file` in config file to the corresponding json file.
### 2. Training Jobs
Setting `rfsearch_cfg.rfstructure_file` to the searched structure file (.json) and setting `rfsearch_cfg.mode` to `fixed_single_branch` or `fixed_multi_branch`, you can retrain a model with the searched structure.
You can launch fixed_single_branch/fixed_multi_branch training jobs by using config files with prefix `rfnext_fixed_single_branch` or `rfnext_fixed_multi_branch`.
Note that the models after the searching stage is ready a `fixed_multi_branch` version, which achieves better performance than `fixed_single_branch`, without any retraining.
## Inference
`rfsearch_cfg.rfstructure_file` and `rfsearch_cfg.mode` should be set for inferencing stage.
**Note:For the models trained with modes of `fixed_single_branch` or `fixed_multi_branch`, you can just use the training config for inferencing.**
**But If you want to inference the models trained with the mode of `search`, please use the config with prefix of `rfnext_fixed_multi_branch` to inference the models. (Otherwise, you should set `rfsearch_cfg.mode` to `fixed_multi_branch` and set the searched rfstructure_file.)**
## Citation
```
@article{gao2022rfnext,
title={RF-Next: Efficient Receptive Field Search for Convolutional Neural Networks},
author={Gao, Shanghua and Li, Zhong-Yu and Han, Qi and Cheng, Ming-Ming and Wang, Liang},
journal=TPAMI,
year={2022}
}
@inproceedings{gao2021global2local,
title = {Global2Local: Efficient Structure Search for Video Action Segmentation},
author = {Gao, Shanghua and Han, Qi and Li, Zhong-Yu and Peng, Pai and Wang, Liang and Cheng, Ming-Ming},
booktitle = CVPR,
year = {2021}
}
```
| 26,501 | 199.772727 | 1,098 | md |
mmdetection | mmdetection-master/configs/rfnext/metafile.yml | Collections:
- Name: RF-Next
Metadata:
Training Data: COCO
Training Techniques:
- RF-Next
Training Resources: 8x V100 GPUs
Architecture:
- RF-Next
Paper:
URL: http://mftp.mmcheng.net/Papers/22TPAMI-ActionSeg.pdf
Title: "RF-Next: Efficient Receptive Field Search for Convolutional Neural Networks"
README: configs/rfnext/README.md
Code:
URL: https://github.com/open-mmlab/mmdetection/blob/mmdet/utils/rfnext.py
Version: v2.27.0
Models:
- Name: rfnext_search_cascade_mask_rcnn_convnext_tiny_patch4_window7_mstrain_480-800_giou_4conv1f_adamw_3x_coco_in1k
In Collection: RF-Next
Config: configs/rfnext/rfnext_search_cascade_mask_rcnn_convnext-t_p4_w7_fpn_giou_4conv1f_fp16_ms-crop_3x_coco.py
Metadata:
Training Memory (GB): 11.9
Epochs: 36
Training Data: COCO
Training Techniques:
RF-Next (search)
Results:
- Task: Object Detection
Dataset: COCO
Metrics:
box AP: 50.9
- Task: Instance Segmentation
Dataset: COCO
Metrics:
mask AP: 44.3
Weights: https://download.openmmlab.com/mmdetection/v2.0/rfnext/rfnext_search_cascade_mask_rcnn_convnext_tiny_patch4_window7_mstrain_480-800_giou_4conv1f_adamw_3x_coco_in1k/rfnext_search_cascade_mask_rcnn_convnext_tiny_patch4_window7_mstrain_480-800_giou_4conv1f_adamw_3x_coco_in1k-f47db42b.pth
Paper:
URL: https://arxiv.org/pdf/2206.06637.pdf
Title: 'RF-Next: Efficient Receptive Field Search for CNN'
README: configs/rfnext/README.md
- Name: rfnext_fixed_single_branch_cascade_mask_rcnn_convnext_tiny_patch4_window7_mstrain_480-800_giou_4conv1f_adamw_3x_coco_in1k
In Collection: RF-Next
Config: configs/rfnext/rfnext_fixed_single_branch_cascade_mask_rcnn_convnext-t_p4_w7_fpn_giou_4conv1f_fp16_ms-crop_3x_coco.py
Metadata:
Training Memory (GB): 9.4
Epochs: 36
Training Data: COCO
Training Techniques:
RF-Next (fixed_single_branch)
Results:
- Task: Object Detection
Dataset: COCO
Metrics:
box AP: 50.6
- Task: Instance Segmentation
Dataset: COCO
Metrics:
mask AP: 44.0
Weights: https://download.openmmlab.com/mmdetection/v2.0/rfnext/rfnext_fixed_single_branch_cascade_mask_rcnn_convnext_tiny_patch4_window7_mstrain_480-800_giou_4conv1f_adamw_3x_coco_in1k/rfnext_fixed_single_branch_cascade_mask_rcnn_convnext_tiny_patch4_window7_mstrain_480-800_giou_4conv1f_adamw_3x_coco_in1k-71aeb991.pth
Paper:
URL: https://arxiv.org/pdf/2206.06637.pdf
Title: 'RF-Next: Efficient Receptive Field Search for CNN'
README: configs/rfnext/README.md
- Name: rfnext_search_cascade_mask_rcnn_hrnetv2p_w18_20e_coco
In Collection: RF-Next
Config: configs/rfnext/rfnext_search_cascade_mask_rcnn_hrnetv2p_w18_20e_coco.py
Metadata:
Training Memory (GB): 12.9
Epochs: 20
Training Data: COCO
Training Techniques:
RF-Next (search)
Results:
- Task: Object Detection
Dataset: COCO
Metrics:
box AP: 43.7
- Task: Instance Segmentation
Dataset: COCO
Metrics:
mask AP: 38.2
Weights: https://download.openmmlab.com/mmdetection/v2.0/rfnext/rfnext_search_cascade_mask_rcnn_hrnetv2p_w18_20e_coco/rfnext_search_cascade_mask_rcnn_hrnetv2p_w18_20e_coco-7b9c7885.pth
Paper:
URL: https://arxiv.org/pdf/2206.06637.pdf
Title: 'RF-Next: Efficient Receptive Field Search for CNN'
README: configs/rfnext/README.md
- Name: rfnext_fixed_single_branch_cascade_mask_rcnn_hrnetv2p_w18_20e_coco
In Collection: RF-Next
Config: configs/rfnext/rfnext_fixed_single_branch_cascade_mask_rcnn_hrnetv2p_w18_20e_coco.py
Metadata:
Training Memory (GB): 8.4
Epochs: 20
Training Data: COCO
Training Techniques:
RF-Next (fixed_single_branch)
Results:
- Task: Object Detection
Dataset: COCO
Metrics:
box AP: 43.0
- Task: Instance Segmentation
Dataset: COCO
Metrics:
mask AP: 37.6
Weights: https://download.openmmlab.com/mmdetection/v2.0/rfnext/rfnext_fixed_single_branch_cascade_mask_rcnn_hrnetv2p_w18_20e_coco/rfnext_fixed_single_branch_cascade_mask_rcnn_hrnetv2p_w18_20e_coco-682f121d.pth
Paper:
URL: https://arxiv.org/pdf/2206.06637.pdf
Title: 'RF-Next: Efficient Receptive Field Search for CNN'
README: configs/rfnext/README.md
- Name: rfnext_search_cascade_mask_rcnn_r2_101_fpn_20e_coco
In Collection: RF-Next
Config: configs/rfnext/rfnext_search_cascade_mask_rcnn_r2_101_fpn_20e_coco.py
Metadata:
Training Memory (GB): 11.9
Epochs: 20
Training Data: COCO
Training Techniques:
RF-Next (search)
Results:
- Task: Object Detection
Dataset: COCO
Metrics:
box AP: 47.9
- Task: Instance Segmentation
Dataset: COCO
Metrics:
mask AP: 41.5
Weights: https://download.openmmlab.com/mmdetection/v2.0/rfnext/rfnext_search_cascade_mask_rcnn_r2_101_fpn_20e_coco/rfnext_search_cascade_mask_rcnn_r2_101_fpn_20e_coco-e17510a0.pth
Paper:
URL: https://arxiv.org/pdf/2206.06637.pdf
Title: 'RF-Next: Efficient Receptive Field Search for CNN'
README: configs/rfnext/README.md
- Name: rfnext_fixed_single_branch_cascade_mask_rcnn_r2_101_fpn_20e_coco
In Collection: RF-Next
Config: configs/rfnext/rfnext_fixed_single_branch_cascade_mask_rcnn_r2_101_fpn_20e_coco.py
Metadata:
Training Memory (GB): 9.3
Epochs: 20
Training Data: COCO
Training Techniques:
RF-Next (fixed_single_branch)
Results:
- Task: Object Detection
Dataset: COCO
Metrics:
box AP: 46.9
- Task: Instance Segmentation
Dataset: COCO
Metrics:
mask AP: 40.7
Weights: https://download.openmmlab.com/mmdetection/v2.0/rfnext/rfnext_fixed_single_branch_cascade_mask_rcnn_r2_101_fpn_20e_coco/rfnext_fixed_single_branch_cascade_mask_rcnn_r2_101_fpn_20e_coco-e22d5257.pth
Paper:
URL: https://arxiv.org/pdf/2206.06637.pdf
Title: 'RF-Next: Efficient Receptive Field Search for CNN'
README: configs/rfnext/README.md
- Name: rfnext_search_mask_rcnn_pvtv2-b0_fpn_1x_coco
In Collection: RF-Next
Config: configs/rfnext/rfnext_search_mask_rcnn_pvtv2-b0_fpn_1x_coco.py
Metadata:
Training Memory (GB): 10.3
Epochs: 12
Training Data: COCO
Training Techniques:
RF-Next (search)
Results:
- Task: Object Detection
Dataset: COCO
Metrics:
box AP: 39.3
- Task: Instance Segmentation
Dataset: COCO
Metrics:
mask AP: 37.1
Weights: https://download.openmmlab.com/mmdetection/v2.0/rfnext/rfnext_search_mask_rcnn_pvtv2-b0_fpn_1x_coco/rfnext_search_mask_rcnn_pvtv2-b0_fpn_1x_coco-dc8fd5de.pth
Paper:
URL: https://arxiv.org/pdf/2206.06637.pdf
Title: 'RF-Next: Efficient Receptive Field Search for CNN'
README: configs/rfnext/README.md
- Name: rfnext_fixed_single_branch_mask_rcnn_pvtv2-b0_fpn_1x_coco
In Collection: RF-Next
Config: configs/rfnext/rfnext_fixed_single_branch_mask_rcnn_pvtv2-b0_fpn_1x_coco.py
Metadata:
Training Memory (GB): 8.3
Epochs: 12
Training Data: COCO
Training Techniques:
RF-Next (fixed_single_branch)
Results:
- Task: Object Detection
Dataset: COCO
Metrics:
box AP: 38.9
- Task: Instance Segmentation
Dataset: COCO
Metrics:
mask AP: 36.8
Weights: https://download.openmmlab.com/mmdetection/v2.0/rfnext/rfnext_fixed_single_branch_mask_rcnn_pvtv2-b0_fpn_1x_coco/rfnext_fixed_single_branch_mask_rcnn_pvtv2-b0_fpn_1x_coco-7b25d72e.pth
Paper:
URL: https://arxiv.org/pdf/2206.06637.pdf
Title: 'RF-Next: Efficient Receptive Field Search for CNN'
README: configs/rfnext/README.md
- Name: rfnext_search_panoptic_fpn_r2_50_fpn_fp16_1x_coco
In Collection: RF-Next
Config: configs/rfnext/rfnext_search_panoptic_fpn_r2_50_fpn_fp16_1x_coco.py
Metadata:
Training Memory (GB): 4.3
Epochs: 12
Training Data: COCO
Training Techniques:
RF-Next (search)
Results:
- Task: Panoptic Segmentation
Dataset: COCO
Metrics:
box AP: 44.3
Weights: https://download.openmmlab.com/mmdetection/v2.0/rfnext/rfnext_search_panoptic_fpn_r2_50_fpn_fp16_1x_coco/rfnext_search_panoptic_fpn_r2_50_fpn_fp16_1x_coco-34a893a0.pth
Paper:
URL: https://arxiv.org/pdf/2206.06637.pdf
Title: 'RF-Next: Efficient Receptive Field Search for CNN'
README: configs/rfnext/README.md
- Name: rfnext_fixed_single_branch_panoptic_fpn_r2_50_fpn_fp16_1x_coco
In Collection: RF-Next
Config: configs/rfnext/rfnext_fixed_single_branch_panoptic_fpn_r2_50_fpn_fp16_1x_coco.py
Metadata:
Training Memory (GB): 3.5
Epochs: 12
Training Data: COCO
Training Techniques:
RF-Next (fixed_single_branch)
Results:
- Task: Panoptic Segmentation
Dataset: COCO
Metrics:
box AP: 44.0
Weights: https://download.openmmlab.com/mmdetection/v2.0/rfnext/rfnext_fixed_single_branch_panoptic_fpn_r2_50_fpn_fp16_1x_coco/rfnext_fixed_single_branch_panoptic_fpn_r2_50_fpn_fp16_1x_coco-52181d5b.pth
Paper:
URL: https://arxiv.org/pdf/2206.06637.pdf
Title: 'RF-Next: Efficient Receptive Field Search for CNN'
README: configs/rfnext/README.md
| 9,654 | 37.62 | 324 | yml |
mmdetection | mmdetection-master/configs/rfnext/rfnext_fixed_multi_branch_cascade_mask_rcnn_convnext-t_p4_w7_fpn_giou_4conv1f_fp16_ms-crop_3x_coco.py | _base_ = '../convnext/cascade_mask_rcnn_convnext-t_p4_w7_fpn_giou_4conv1f_fp16_ms-crop_3x_coco.py' # noqa
custom_hooks = [
dict(
type='RFSearchHook',
mode='fixed_multi_branch',
rfstructure_file= # noqa
'./configs/rfnext/search_log/convnext_cascade_maskrcnn/local_search_config_step11.json', # noqa
verbose=True,
by_epoch=True,
config=dict(
search=dict(
step=0,
max_step=12,
search_interval=1,
exp_rate=0.5,
init_alphas=0.01,
normlize='absavg',
mmin=1,
mmax=24,
num_branches=2,
skip_layer=[])))
]
| 732 | 29.541667 | 106 | py |
mmdetection | mmdetection-master/configs/rfnext/rfnext_fixed_multi_branch_cascade_mask_rcnn_hrnetv2p_w18_20e_coco.py | _base_ = '../hrnet/cascade_mask_rcnn_hrnetv2p_w32_20e_coco.py'
custom_hooks = [
dict(
mode='fixed_multi_branch',
rfstructure_file= # noqa
'./configs/rfnext/search_log/cascade_mask_rcnn_hrnetv2p_w18_20e_coco/local_search_config_step11.json', # noqa
verbose=True,
by_epoch=True,
config=dict(
search=dict(
step=0,
max_step=12,
search_interval=1,
exp_rate=0.5,
init_alphas=0.01,
mmin=1,
mmax=24,
num_branches=2,
skip_layer=[])))
]
| 638 | 28.045455 | 118 | py |
mmdetection | mmdetection-master/configs/rfnext/rfnext_fixed_multi_branch_cascade_mask_rcnn_r2_101_fpn_20e_coco.py | _base_ = '../res2net/cascade_mask_rcnn_r2_101_fpn_20e_coco.py'
custom_hooks = [
dict(type='NumClassCheckHook'),
dict(
type='RFSearchHook',
mode='fixed_multi_branch',
rfstructure_file= # noqa
'./configs/rfnext/search_log/cascade_mask_rcnn_r2_101_fpn_20e_coco/local_search_config_step11.json', # noqa
verbose=True,
by_epoch=True,
config=dict(
search=dict(
step=0,
max_step=12,
search_interval=1,
exp_rate=0.5,
init_alphas=0.01,
mmin=1,
mmax=24,
num_branches=2,
skip_layer=['stem', 'layer1'])))
]
| 717 | 28.916667 | 116 | py |
mmdetection | mmdetection-master/configs/rfnext/rfnext_fixed_multi_branch_mask_rcnn_pvtv2-b0_fpn_1x_coco.py | _base_ = [
'../_base_/models/mask_rcnn_r50_fpn.py',
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
# model setting
model = dict(
backbone=dict(
_delete_=True,
type='PyramidVisionTransformerV2',
embed_dims=32,
num_layers=[2, 2, 2, 2],
init_cfg=dict(
checkpoint= # noqa
'https://github.com/whai362/PVT/releases/download/v2/pvt_v2_b0.pth' # noqa
)),
neck=dict(
type='FPN',
in_channels=[32, 64, 160, 256],
out_channels=256,
num_outs=5))
# optimizer
optimizer = dict(_delete_=True, type='AdamW', lr=0.0002, weight_decay=0.0001)
optimizer_config = dict(grad_clip=None)
custom_hooks = [
dict(
type='RFSearchHook',
mode='fixed_multi_branch',
rfstructure_file= # noqa
'./configs/rfnext/search_log/mask_rcnn_pvtv2-b0_fpn_1x_coco/local_search_config_step10.json', # noqa
verbose=True,
by_epoch=True,
config=dict(
search=dict(
step=0,
max_step=11,
search_interval=1,
exp_rate=0.5,
init_alphas=0.01,
mmin=1,
mmax=24,
num_branches=2,
skip_layer=[])))
]
| 1,348 | 27.702128 | 109 | py |
mmdetection | mmdetection-master/configs/rfnext/rfnext_fixed_multi_branch_panoptic_fpn_r2_50_fpn_fp16_1x_coco.py | _base_ = '../panoptic_fpn/panoptic_fpn_r2_50_fpn_fp16_1x_coco.py'
custom_hooks = [
dict(
type='RFSearchHook',
mode='fixed_multi_branch',
rfstructure_file= # noqa
'./configs/rfnext/search_log/panoptic_fpn_r2_50_fpn_fp16_1x_coco/local_search_config_step10.json', # noqa
verbose=True,
by_epoch=True,
config=dict(
search=dict(
step=0,
max_step=11,
search_interval=1,
exp_rate=0.5,
init_alphas=0.01,
mmin=1,
mmax=24,
num_branches=2,
skip_layer=['stem', 'layer1'])))
]
| 682 | 28.695652 | 114 | py |
mmdetection | mmdetection-master/configs/rfnext/rfnext_fixed_single_branch_cascade_mask_rcnn_convnext-t_p4_w7_fpn_giou_4conv1f_fp16_ms-crop_3x_coco.py | _base_ = '../convnext/cascade_mask_rcnn_convnext-t_p4_w7_fpn_giou_4conv1f_fp16_ms-crop_3x_coco.py' # noqa
custom_hooks = [
dict(
type='RFSearchHook',
mode='fixed_single_branch',
rfstructure_file= # noqa
'./configs/rfnext/search_log/convnext_cascade_maskrcnn/local_search_config_step11.json', # noqa
verbose=True,
by_epoch=True,
config=dict(
search=dict(
step=0,
max_step=12,
search_interval=1,
exp_rate=0.5,
init_alphas=0.01,
mmin=1,
mmax=24,
num_branches=2,
skip_layer=[])))
]
| 698 | 29.391304 | 106 | py |
mmdetection | mmdetection-master/configs/rfnext/rfnext_fixed_single_branch_cascade_mask_rcnn_hrnetv2p_w18_20e_coco.py | _base_ = '../hrnet/cascade_mask_rcnn_hrnetv2p_w32_20e_coco.py'
custom_hooks = [
dict(
type='RFSearchHook',
mode='fixed_single_branch',
rfstructure_file= # noqa
'./configs/rfnext/search_log/cascade_mask_rcnn_hrnetv2p_w18_20e_coco/local_search_config_step11.json', # noqa
verbose=True,
by_epoch=True,
config=dict(
search=dict(
step=0,
max_step=12,
search_interval=1,
exp_rate=0.5,
init_alphas=0.01,
mmin=1,
mmax=24,
num_branches=2,
skip_layer=[])))
]
| 668 | 28.086957 | 118 | py |
mmdetection | mmdetection-master/configs/rfnext/rfnext_fixed_single_branch_cascade_mask_rcnn_r2_101_fpn_20e_coco.py | _base_ = '../res2net/cascade_mask_rcnn_r2_101_fpn_20e_coco.py'
custom_hooks = [
dict(type='NumClassCheckHook'),
dict(
type='RFSearchHook',
mode='fixed_single_branch',
rfstructure_file= # noqa
'./configs/rfnext/search_log/cascade_mask_rcnn_r2_101_fpn_20e_coco/local_search_config_step11.json', # noqa
verbose=True,
by_epoch=True,
config=dict(
search=dict(
step=0,
max_step=12,
search_interval=1,
exp_rate=0.5,
init_alphas=0.01,
mmin=1,
mmax=24,
num_branches=2,
skip_layer=['stem', 'layer1'])))
]
| 718 | 28.958333 | 116 | py |
mmdetection | mmdetection-master/configs/rfnext/rfnext_fixed_single_branch_mask_rcnn_pvtv2-b0_fpn_1x_coco.py | _base_ = [
'../_base_/models/mask_rcnn_r50_fpn.py',
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
# model setting
model = dict(
backbone=dict(
_delete_=True,
type='PyramidVisionTransformerV2',
embed_dims=32,
num_layers=[2, 2, 2, 2],
init_cfg=dict(
checkpoint= # noqa
'https://github.com/whai362/PVT/releases/download/v2/pvt_v2_b0.pth' # noqa
)),
neck=dict(
type='FPN',
in_channels=[32, 64, 160, 256],
out_channels=256,
num_outs=5))
# optimizer
optimizer = dict(_delete_=True, type='AdamW', lr=0.0002, weight_decay=0.0001)
optimizer_config = dict(grad_clip=None)
custom_hooks = [
dict(
type='RFSearchHook',
mode='fixed_single_branch',
rfstructure_file= # noqa
'./configs/rfnext/search_log/mask_rcnn_pvtv2-b0_fpn_1x_coco/local_search_config_step10.json', # noqa
verbose=True,
by_epoch=True,
config=dict(
search=dict(
step=0,
max_step=11,
search_interval=1,
exp_rate=0.5,
init_alphas=0.01,
mmin=1,
mmax=24,
num_branches=2,
skip_layer=[])))
]
| 1,349 | 27.723404 | 109 | py |
mmdetection | mmdetection-master/configs/rfnext/rfnext_fixed_single_branch_panoptic_fpn_r2_50_fpn_fp16_1x_coco.py | _base_ = '../panoptic_fpn/panoptic_fpn_r2_50_fpn_fp16_1x_coco.py'
custom_hooks = [
dict(
type='RFSearchHook',
mode='fixed_single_branch',
rfstructure_file= # noqa
'./configs/rfnext/search_log/panoptic_fpn_r2_50_fpn_fp16_1x_coco/local_search_config_step10.json', # noqa
verbose=True,
by_epoch=True,
config=dict(
search=dict(
step=0,
max_step=11,
search_interval=1,
exp_rate=0.5,
init_alphas=0.01,
mmin=1,
mmax=24,
num_branches=2,
skip_layer=['stem', 'layer1'])))
]
| 683 | 28.73913 | 114 | py |
mmdetection | mmdetection-master/configs/rfnext/rfnext_search_cascade_mask_rcnn_convnext-t_p4_w7_fpn_giou_4conv1f_fp16_ms-crop_3x_coco.py | _base_ = '../convnext/cascade_mask_rcnn_convnext-t_p4_w7_fpn_giou_4conv1f_fp16_ms-crop_3x_coco.py' # noqa
custom_hooks = [
dict(
type='RFSearchHook',
mode='search',
rfstructure_file=None,
verbose=True,
by_epoch=True,
config=dict(
search=dict(
step=0,
max_step=12,
search_interval=1,
exp_rate=0.5,
init_alphas=0.01,
mmin=1,
mmax=24,
num_branches=2,
skip_layer=[])))
]
| 577 | 25.272727 | 106 | py |
mmdetection | mmdetection-master/configs/rfnext/rfnext_search_cascade_mask_rcnn_hrnetv2p_w18_20e_coco.py | _base_ = '../hrnet/cascade_mask_rcnn_hrnetv2p_w32_20e_coco.py'
custom_hooks = [
dict(
type='RFSearchHook',
mode='search',
rfstructure_file=None,
verbose=True,
by_epoch=True,
config=dict(
search=dict(
step=0,
max_step=12,
search_interval=1,
exp_rate=0.5,
init_alphas=0.01,
mmin=1,
mmax=24,
num_branches=2,
skip_layer=[])))
]
| 533 | 23.272727 | 62 | py |
mmdetection | mmdetection-master/configs/rfnext/rfnext_search_cascade_mask_rcnn_r2_101_fpn_20e_coco.py | _base_ = '../res2net/cascade_mask_rcnn_r2_101_fpn_20e_coco.py'
custom_hooks = [
dict(type='NumClassCheckHook'),
dict(
type='RFSearchHook',
mode='search',
rfstructure_file=None,
verbose=True,
by_epoch=True,
config=dict(
search=dict(
step=0,
max_step=12,
search_interval=1,
exp_rate=0.5,
init_alphas=0.01,
mmin=1,
mmax=24,
num_branches=2,
skip_layer=['stem', 'layer1'])))
]
| 585 | 24.478261 | 62 | py |
mmdetection | mmdetection-master/configs/rfnext/rfnext_search_mask_rcnn_pvtv2-b0_fpn_1x_coco.py | _base_ = [
'../_base_/models/mask_rcnn_r50_fpn.py',
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
# model setting
model = dict(
backbone=dict(
_delete_=True,
type='PyramidVisionTransformerV2',
embed_dims=32,
num_layers=[2, 2, 2, 2],
init_cfg=dict(
checkpoint= # noqa
'https://github.com/whai362/PVT/releases/download/v2/pvt_v2_b0.pth' # noqa
)),
neck=dict(
type='FPN',
in_channels=[32, 64, 160, 256],
out_channels=256,
num_outs=5))
# optimizer
optimizer = dict(_delete_=True, type='AdamW', lr=0.0002, weight_decay=0.0001)
optimizer_config = dict(grad_clip=None)
custom_hooks = [
dict(
type='RFSearchHook',
mode='search',
rfstructure_file=None,
verbose=True,
by_epoch=True,
config=dict(
search=dict(
step=0,
max_step=11,
search_interval=1,
exp_rate=0.5,
init_alphas=0.01,
mmin=1,
mmax=24,
num_branches=2,
skip_layer=[])))
]
| 1,223 | 25.608696 | 87 | py |
mmdetection | mmdetection-master/configs/rfnext/rfnext_search_panoptic_fpn_r2_50_fpn_fp16_1x_coco.py | _base_ = '../panoptic_fpn/panoptic_fpn_r2_50_fpn_fp16_1x_coco.py'
custom_hooks = [
dict(
type='RFSearchHook',
mode='search',
rfstructure_file=None,
verbose=True,
by_epoch=True,
config=dict(
search=dict(
step=0,
max_step=11,
search_interval=1,
exp_rate=0.5,
init_alphas=0.01,
mmin=1,
mmax=24,
num_branches=2,
skip_layer=['stem', 'layer1'])))
]
| 552 | 24.136364 | 65 | py |
mmdetection | mmdetection-master/configs/rpn/README.md | # RPN
> [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://arxiv.org/abs/1506.01497)
<!-- [ALGORITHM] -->
## Abstract
State-of-the-art object detection networks depend on region proposal algorithms to hypothesize object locations. Advances like SPPnet and Fast R-CNN have reduced the running time of these detection networks, exposing region proposal computation as a bottleneck. In this work, we introduce a Region Proposal Network (RPN) that shares full-image convolutional features with the detection network, thus enabling nearly cost-free region proposals. An RPN is a fully convolutional network that simultaneously predicts object bounds and objectness scores at each position. The RPN is trained end-to-end to generate high-quality region proposals, which are used by Fast R-CNN for detection. We further merge RPN and Fast R-CNN into a single network by sharing their convolutional features---using the recently popular terminology of neural networks with 'attention' mechanisms, the RPN component tells the unified network where to look. For the very deep VGG-16 model, our detection system has a frame rate of 5fps (including all steps) on a GPU, while achieving state-of-the-art object detection accuracy on PASCAL VOC 2007, 2012, and MS COCO datasets with only 300 proposals per image. In ILSVRC and COCO 2015 competitions, Faster R-CNN and RPN are the foundations of the 1st-place winning entries in several tracks.
<div align=center>
<img src="https://user-images.githubusercontent.com/40661020/143973617-387c7561-82f4-40b2-b78e-4776394b1b8b.png" height="300"/>
</div>
## Results and Models
| Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | AR1000 | Config | Download |
| :-------------: | :-----: | :-----: | :------: | :------------: | :----: | :-------------------------------------------------------------------------------------------------------: | :-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
| R-50-FPN | caffe | 1x | 3.5 | 22.6 | 58.7 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/rpn/rpn_r50_caffe_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/rpn/rpn_r50_caffe_fpn_1x_coco/rpn_r50_caffe_fpn_1x_coco_20200531-5b903a37.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/rpn/rpn_r50_caffe_fpn_1x_coco/rpn_r50_caffe_fpn_1x_coco_20200531_012334.log.json) |
| R-50-FPN | pytorch | 1x | 3.8 | 22.3 | 58.2 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/rpn/rpn_r50_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/rpn/rpn_r50_fpn_1x_coco/rpn_r50_fpn_1x_coco_20200218-5525fa2e.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/rpn/rpn_r50_fpn_1x_coco/rpn_r50_fpn_1x_coco_20200218_151240.log.json) |
| R-50-FPN | pytorch | 2x | - | - | 58.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/rpn/rpn_r50_fpn_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/rpn/rpn_r50_fpn_2x_coco/rpn_r50_fpn_2x_coco_20200131-0728c9b3.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/rpn/rpn_r50_fpn_2x_coco/rpn_r50_fpn_2x_coco_20200131_190631.log.json) |
| R-101-FPN | caffe | 1x | 5.4 | 17.3 | 60.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/rpn/rpn_r101_caffe_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/rpn/rpn_r101_caffe_fpn_1x_coco/rpn_r101_caffe_fpn_1x_coco_20200531-0629a2e2.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/rpn/rpn_r101_caffe_fpn_1x_coco/rpn_r101_caffe_fpn_1x_coco_20200531_012345.log.json) |
| R-101-FPN | pytorch | 1x | 5.8 | 16.5 | 59.7 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/rpn/rpn_r101_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/rpn/rpn_r101_fpn_1x_coco/rpn_r101_fpn_1x_coco_20200131-2ace2249.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/rpn/rpn_r101_fpn_1x_coco/rpn_r101_fpn_1x_coco_20200131_191000.log.json) |
| R-101-FPN | pytorch | 2x | - | - | 60.2 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/rpn/rpn_r101_fpn_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/rpn/rpn_r101_fpn_2x_coco/rpn_r101_fpn_2x_coco_20200131-24e3db1a.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/rpn/rpn_r101_fpn_2x_coco/rpn_r101_fpn_2x_coco_20200131_191106.log.json) |
| X-101-32x4d-FPN | pytorch | 1x | 7.0 | 13.0 | 60.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/rpn/rpn_x101_32x4d_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/rpn/rpn_x101_32x4d_fpn_1x_coco/rpn_x101_32x4d_fpn_1x_coco_20200219-b02646c6.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/rpn/rpn_x101_32x4d_fpn_1x_coco/rpn_x101_32x4d_fpn_1x_coco_20200219_012037.log.json) |
| X-101-32x4d-FPN | pytorch | 2x | - | - | 61.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/rpn/rpn_x101_32x4d_fpn_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/rpn/rpn_x101_32x4d_fpn_2x_coco/rpn_x101_32x4d_fpn_2x_coco_20200208-d22bd0bb.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/rpn/rpn_x101_32x4d_fpn_2x_coco/rpn_x101_32x4d_fpn_2x_coco_20200208_200752.log.json) |
| X-101-64x4d-FPN | pytorch | 1x | 10.1 | 9.1 | 61.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/rpn/rpn_x101_64x4d_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/rpn/rpn_x101_64x4d_fpn_1x_coco/rpn_x101_64x4d_fpn_1x_coco_20200208-cde6f7dd.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/rpn/rpn_x101_64x4d_fpn_1x_coco/rpn_x101_64x4d_fpn_1x_coco_20200208_200752.log.json) |
| X-101-64x4d-FPN | pytorch | 2x | - | - | 61.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/rpn/rpn_x101_64x4d_fpn_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/rpn/rpn_x101_64x4d_fpn_2x_coco/rpn_x101_64x4d_fpn_2x_coco_20200208-c65f524f.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/rpn/rpn_x101_64x4d_fpn_2x_coco/rpn_x101_64x4d_fpn_2x_coco_20200208_200752.log.json) |
## Citation
```latex
@inproceedings{ren2015faster,
title={Faster r-cnn: Towards real-time object detection with region proposal networks},
author={Ren, Shaoqing and He, Kaiming and Girshick, Ross and Sun, Jian},
booktitle={Advances in neural information processing systems},
year={2015}
}
```
| 7,534 | 187.375 | 1,311 | md |
mmdetection | mmdetection-master/configs/rpn/rpn_r101_caffe_fpn_1x_coco.py | _base_ = './rpn_r50_caffe_fpn_1x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(
type='Pretrained',
checkpoint='open-mmlab://detectron2/resnet101_caffe')))
| 216 | 26.125 | 67 | py |
mmdetection | mmdetection-master/configs/rpn/rpn_r101_fpn_1x_coco.py | _base_ = './rpn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 191 | 26.428571 | 61 | py |
mmdetection | mmdetection-master/configs/rpn/rpn_r101_fpn_2x_coco.py | _base_ = './rpn_r50_fpn_2x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 191 | 26.428571 | 61 | py |
mmdetection | mmdetection-master/configs/rpn/rpn_r50_caffe_c4_1x_coco.py | _base_ = [
'../_base_/models/rpn_r50_caffe_c4.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
# dataset settings
img_norm_cfg = dict(
mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True, with_label=False),
dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
dict(type='RandomFlip', flip_ratio=0.5),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img', 'gt_bboxes']),
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(1333, 800),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='RandomFlip'),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img']),
])
]
data = dict(
train=dict(pipeline=train_pipeline),
val=dict(pipeline=test_pipeline),
test=dict(pipeline=test_pipeline))
evaluation = dict(interval=1, metric='proposal_fast')
| 1,352 | 33.692308 | 72 | py |
mmdetection | mmdetection-master/configs/rpn/rpn_r50_caffe_fpn_1x_coco.py | _base_ = './rpn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
norm_cfg=dict(requires_grad=False),
norm_eval=True,
style='caffe',
init_cfg=dict(
type='Pretrained',
checkpoint='open-mmlab://detectron2/resnet50_caffe')))
# use caffe img_norm
img_norm_cfg = dict(
mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True, with_label=False),
dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
dict(type='RandomFlip', flip_ratio=0.5),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img', 'gt_bboxes']),
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(1333, 800),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='RandomFlip'),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img']),
])
]
data = dict(
train=dict(pipeline=train_pipeline),
val=dict(pipeline=test_pipeline),
test=dict(pipeline=test_pipeline))
| 1,407 | 32.52381 | 72 | py |
mmdetection | mmdetection-master/configs/rpn/rpn_r50_fpn_1x_coco.py | _base_ = [
'../_base_/models/rpn_r50_fpn.py', '../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True, with_label=False),
dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
dict(type='RandomFlip', flip_ratio=0.5),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img', 'gt_bboxes']),
]
data = dict(train=dict(pipeline=train_pipeline))
evaluation = dict(interval=1, metric='proposal_fast')
| 776 | 39.894737 | 78 | py |
mmdetection | mmdetection-master/configs/rpn/rpn_r50_fpn_2x_coco.py | _base_ = './rpn_r50_fpn_1x_coco.py'
# learning policy
lr_config = dict(step=[16, 22])
runner = dict(type='EpochBasedRunner', max_epochs=24)
| 141 | 22.666667 | 53 | py |
mmdetection | mmdetection-master/configs/rpn/rpn_x101_32x4d_fpn_1x_coco.py | _base_ = './rpn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
type='ResNeXt',
depth=101,
groups=32,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
style='pytorch',
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://resnext101_32x4d')))
| 413 | 26.6 | 76 | py |
mmdetection | mmdetection-master/configs/rpn/rpn_x101_32x4d_fpn_2x_coco.py | _base_ = './rpn_r50_fpn_2x_coco.py'
model = dict(
backbone=dict(
type='ResNeXt',
depth=101,
groups=32,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
style='pytorch',
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://resnext101_32x4d')))
| 413 | 26.6 | 76 | py |
mmdetection | mmdetection-master/configs/rpn/rpn_x101_64x4d_fpn_1x_coco.py | _base_ = './rpn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
type='ResNeXt',
depth=101,
groups=64,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
style='pytorch',
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d')))
| 413 | 26.6 | 76 | py |
mmdetection | mmdetection-master/configs/rpn/rpn_x101_64x4d_fpn_2x_coco.py | _base_ = './rpn_r50_fpn_2x_coco.py'
model = dict(
backbone=dict(
type='ResNeXt',
depth=101,
groups=64,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
style='pytorch',
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d')))
| 413 | 26.6 | 76 | py |
mmdetection | mmdetection-master/configs/sabl/README.md | # SABL
> [Side-Aware Boundary Localization for More Precise Object Detection](https://arxiv.org/abs/1912.04260)
<!-- [ALGORITHM] -->
## Abstract
Current object detection frameworks mainly rely on bounding box regression to localize objects. Despite the remarkable progress in recent years, the precision of bounding box regression remains unsatisfactory, hence limiting performance in object detection. We observe that precise localization requires careful placement of each side of the bounding box. However, the mainstream approach, which focuses on predicting centers and sizes, is not the most effective way to accomplish this task, especially when there exists displacements with large variance between the anchors and the targets. In this paper, we propose an alternative approach, named as Side-Aware Boundary Localization (SABL), where each side of the bounding box is respectively localized with a dedicated network branch. To tackle the difficulty of precise localization in the presence of displacements with large variance, we further propose a two-step localization scheme, which first predicts a range of movement through bucket prediction and then pinpoints the precise position within the predicted bucket. We test the proposed method on both two-stage and single-stage detection frameworks. Replacing the standard bounding box regression branch with the proposed design leads to significant improvements on Faster R-CNN, RetinaNet, and Cascade R-CNN, by 3.0%, 1.7%, and 0.9%, respectively.
<div align=center>
<img src="https://user-images.githubusercontent.com/40661020/143973698-3dfaea91-4415-4818-9781-5017183e7489.png"/>
</div>
## Results and Models
The results on COCO 2017 val is shown in the below table. (results on test-dev are usually slightly higher than val).
Single-scale testing (1333x800) is adopted in all results.
| Method | Backbone | Lr schd | ms-train | box AP | Config | Download |
| :----------------: | :-------: | :-----: | :------: | :----: | :----------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
| SABL Faster R-CNN | R-50-FPN | 1x | N | 39.9 | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/sabl/sabl_faster_rcnn_r50_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/sabl/sabl_faster_rcnn_r50_fpn_1x_coco/sabl_faster_rcnn_r50_fpn_1x_coco-e867595b.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/sabl/sabl_faster_rcnn_r50_fpn_1x_coco/20200830_130324.log.json) |
| SABL Faster R-CNN | R-101-FPN | 1x | N | 41.7 | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/sabl/sabl_faster_rcnn_r101_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/sabl/sabl_faster_rcnn_r101_fpn_1x_coco/sabl_faster_rcnn_r101_fpn_1x_coco-f804c6c1.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/sabl/sabl_faster_rcnn_r101_fpn_1x_coco/20200830_183949.log.json) |
| SABL Cascade R-CNN | R-50-FPN | 1x | N | 41.6 | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/sabl/sabl_cascade_rcnn_r50_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/sabl/sabl_cascade_rcnn_r50_fpn_1x_coco/sabl_cascade_rcnn_r50_fpn_1x_coco-e1748e5e.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/sabl/sabl_cascade_rcnn_r50_fpn_1x_coco/20200831_033726.log.json) |
| SABL Cascade R-CNN | R-101-FPN | 1x | N | 43.0 | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/sabl/sabl_cascade_rcnn_r101_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/sabl/sabl_cascade_rcnn_r101_fpn_1x_coco/sabl_cascade_rcnn_r101_fpn_1x_coco-2b83e87c.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/sabl/sabl_cascade_rcnn_r101_fpn_1x_coco/20200831_141745.log.json) |
| Method | Backbone | GN | Lr schd | ms-train | box AP | Config | Download |
| :------------: | :-------: | :-: | :-----: | :---------: | :----: | :---------------------------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
| SABL RetinaNet | R-50-FPN | N | 1x | N | 37.7 | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/sabl/sabl_retinanet_r50_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/sabl/sabl_retinanet_r50_fpn_1x_coco/sabl_retinanet_r50_fpn_1x_coco-6c54fd4f.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/sabl/sabl_retinanet_r50_fpn_1x_coco/20200830_053451.log.json) |
| SABL RetinaNet | R-50-FPN | Y | 1x | N | 38.8 | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/sabl/sabl_retinanet_r50_fpn_gn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/sabl/sabl_retinanet_r50_fpn_gn_1x_coco/sabl_retinanet_r50_fpn_gn_1x_coco-e16dfcf1.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/sabl/sabl_retinanet_r50_fpn_gn_1x_coco/20200831_141955.log.json) |
| SABL RetinaNet | R-101-FPN | N | 1x | N | 39.7 | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/sabl/sabl_retinanet_r101_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/sabl/sabl_retinanet_r101_fpn_1x_coco/sabl_retinanet_r101_fpn_1x_coco-42026904.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/sabl/sabl_retinanet_r101_fpn_1x_coco/20200831_034256.log.json) |
| SABL RetinaNet | R-101-FPN | Y | 1x | N | 40.5 | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/sabl/sabl_retinanet_r101_fpn_gn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/sabl/sabl_retinanet_r101_fpn_gn_1x_coco/sabl_retinanet_r101_fpn_gn_1x_coco-40a893e8.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/sabl/sabl_retinanet_r101_fpn_gn_1x_coco/20200830_201422.log.json) |
| SABL RetinaNet | R-101-FPN | Y | 2x | Y (640~800) | 42.9 | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/sabl/sabl_retinanet_r101_fpn_gn_2x_ms_640_800_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/sabl/sabl_retinanet_r101_fpn_gn_2x_ms_640_800_coco/sabl_retinanet_r101_fpn_gn_2x_ms_640_800_coco-1e63382c.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/sabl/sabl_retinanet_r101_fpn_gn_2x_ms_640_800_coco/20200830_144807.log.json) |
| SABL RetinaNet | R-101-FPN | Y | 2x | Y (480~960) | 43.6 | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/sabl/sabl_retinanet_r101_fpn_gn_2x_ms_480_960_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/sabl/sabl_retinanet_r101_fpn_gn_2x_ms_480_960_coco/sabl_retinanet_r101_fpn_gn_2x_ms_480_960_coco-5342f857.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/sabl/sabl_retinanet_r101_fpn_gn_2x_ms_480_960_coco/20200830_164537.log.json) |
## Citation
We provide config files to reproduce the object detection results in the ECCV 2020 Spotlight paper for [Side-Aware Boundary Localization for More Precise Object Detection](https://arxiv.org/abs/1912.04260).
```latex
@inproceedings{Wang_2020_ECCV,
title = {Side-Aware Boundary Localization for More Precise Object Detection},
author = {Jiaqi Wang and Wenwei Zhang and Yuhang Cao and Kai Chen and Jiangmiao Pang and Tao Gong and Jianping Shi and Chen Change Loy and Dahua Lin},
booktitle = {ECCV},
year = {2020}
}
```
| 9,123 | 189.083333 | 1,361 | md |
mmdetection | mmdetection-master/configs/sabl/metafile.yml | Collections:
- Name: SABL
Metadata:
Training Data: COCO
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Resources: 8x V100 GPUs
Architecture:
- FPN
- ResNet
- SABL
Paper:
URL: https://arxiv.org/abs/1912.04260
Title: 'Side-Aware Boundary Localization for More Precise Object Detection'
README: configs/sabl/README.md
Code:
URL: https://github.com/open-mmlab/mmdetection/blob/v2.4.0/mmdet/models/roi_heads/bbox_heads/sabl_head.py#L14
Version: v2.4.0
Models:
- Name: sabl_faster_rcnn_r50_fpn_1x_coco
In Collection: SABL
Config: configs/sabl/sabl_faster_rcnn_r50_fpn_1x_coco.py
Metadata:
Epochs: 12
Results:
- Task: Object Detection
Dataset: COCO
Metrics:
box AP: 39.9
Weights: https://download.openmmlab.com/mmdetection/v2.0/sabl/sabl_faster_rcnn_r50_fpn_1x_coco/sabl_faster_rcnn_r50_fpn_1x_coco-e867595b.pth
- Name: sabl_faster_rcnn_r101_fpn_1x_coco
In Collection: SABL
Config: configs/sabl/sabl_faster_rcnn_r101_fpn_1x_coco.py
Metadata:
Epochs: 12
Results:
- Task: Object Detection
Dataset: COCO
Metrics:
box AP: 41.7
Weights: https://download.openmmlab.com/mmdetection/v2.0/sabl/sabl_faster_rcnn_r101_fpn_1x_coco/sabl_faster_rcnn_r101_fpn_1x_coco-f804c6c1.pth
- Name: sabl_cascade_rcnn_r50_fpn_1x_coco
In Collection: SABL
Config: configs/sabl/sabl_cascade_rcnn_r50_fpn_1x_coco.py
Metadata:
Epochs: 12
Results:
- Task: Object Detection
Dataset: COCO
Metrics:
box AP: 41.6
Weights: https://download.openmmlab.com/mmdetection/v2.0/sabl/sabl_cascade_rcnn_r50_fpn_1x_coco/sabl_cascade_rcnn_r50_fpn_1x_coco-e1748e5e.pth
- Name: sabl_cascade_rcnn_r101_fpn_1x_coco
In Collection: SABL
Config: configs/sabl/sabl_cascade_rcnn_r101_fpn_1x_coco.py
Metadata:
Epochs: 12
Results:
- Task: Object Detection
Dataset: COCO
Metrics:
box AP: 43.0
Weights: https://download.openmmlab.com/mmdetection/v2.0/sabl/sabl_cascade_rcnn_r101_fpn_1x_coco/sabl_cascade_rcnn_r101_fpn_1x_coco-2b83e87c.pth
- Name: sabl_retinanet_r50_fpn_1x_coco
In Collection: SABL
Config: configs/sabl/sabl_retinanet_r50_fpn_1x_coco.py
Metadata:
Epochs: 12
Results:
- Task: Object Detection
Dataset: COCO
Metrics:
box AP: 37.7
Weights: https://download.openmmlab.com/mmdetection/v2.0/sabl/sabl_retinanet_r50_fpn_1x_coco/sabl_retinanet_r50_fpn_1x_coco-6c54fd4f.pth
- Name: sabl_retinanet_r50_fpn_gn_1x_coco
In Collection: SABL
Config: configs/sabl/sabl_retinanet_r50_fpn_gn_1x_coco.py
Metadata:
Epochs: 12
Results:
- Task: Object Detection
Dataset: COCO
Metrics:
box AP: 38.8
Weights: https://download.openmmlab.com/mmdetection/v2.0/sabl/sabl_retinanet_r50_fpn_gn_1x_coco/sabl_retinanet_r50_fpn_gn_1x_coco-e16dfcf1.pth
- Name: sabl_retinanet_r101_fpn_1x_coco
In Collection: SABL
Config: configs/sabl/sabl_retinanet_r101_fpn_1x_coco.py
Metadata:
Epochs: 12
Results:
- Task: Object Detection
Dataset: COCO
Metrics:
box AP: 39.7
Weights: https://download.openmmlab.com/mmdetection/v2.0/sabl/sabl_retinanet_r101_fpn_1x_coco/sabl_retinanet_r101_fpn_1x_coco-42026904.pth
- Name: sabl_retinanet_r101_fpn_gn_1x_coco
In Collection: SABL
Config: configs/sabl/sabl_retinanet_r101_fpn_gn_1x_coco.py
Metadata:
Epochs: 12
Results:
- Task: Object Detection
Dataset: COCO
Metrics:
box AP: 40.5
Weights: https://download.openmmlab.com/mmdetection/v2.0/sabl/sabl_retinanet_r101_fpn_gn_1x_coco/sabl_retinanet_r101_fpn_gn_1x_coco-40a893e8.pth
- Name: sabl_retinanet_r101_fpn_gn_2x_ms_640_800_coco
In Collection: SABL
Config: configs/sabl/sabl_retinanet_r101_fpn_gn_2x_ms_640_800_coco.py
Metadata:
Epochs: 24
Results:
- Task: Object Detection
Dataset: COCO
Metrics:
box AP: 42.9
Weights: https://download.openmmlab.com/mmdetection/v2.0/sabl/sabl_retinanet_r101_fpn_gn_2x_ms_640_800_coco/sabl_retinanet_r101_fpn_gn_2x_ms_640_800_coco-1e63382c.pth
- Name: sabl_retinanet_r101_fpn_gn_2x_ms_480_960_coco
In Collection: SABL
Config: configs/sabl/sabl_retinanet_r101_fpn_gn_2x_ms_480_960_coco.py
Metadata:
Epochs: 24
Results:
- Task: Object Detection
Dataset: COCO
Metrics:
box AP: 43.6
Weights: https://download.openmmlab.com/mmdetection/v2.0/sabl/sabl_retinanet_r101_fpn_gn_2x_ms_480_960_coco/sabl_retinanet_r101_fpn_gn_2x_ms_480_960_coco-5342f857.pth
| 4,809 | 33.113475 | 170 | yml |
mmdetection | mmdetection-master/configs/sabl/sabl_cascade_rcnn_r101_fpn_1x_coco.py | _base_ = [
'../_base_/models/cascade_rcnn_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
# model settings
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')),
roi_head=dict(bbox_head=[
dict(
type='SABLHead',
num_classes=80,
cls_in_channels=256,
reg_in_channels=256,
roi_feat_size=7,
reg_feat_up_ratio=2,
reg_pre_kernel=3,
reg_post_kernel=3,
reg_pre_num=2,
reg_post_num=1,
cls_out_channels=1024,
reg_offset_out_channels=256,
reg_cls_out_channels=256,
num_cls_fcs=1,
num_reg_fcs=0,
reg_class_agnostic=True,
norm_cfg=None,
bbox_coder=dict(
type='BucketingBBoxCoder', num_buckets=14, scale_factor=1.7),
loss_cls=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
loss_bbox_cls=dict(
type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
loss_bbox_reg=dict(type='SmoothL1Loss', beta=0.1,
loss_weight=1.0)),
dict(
type='SABLHead',
num_classes=80,
cls_in_channels=256,
reg_in_channels=256,
roi_feat_size=7,
reg_feat_up_ratio=2,
reg_pre_kernel=3,
reg_post_kernel=3,
reg_pre_num=2,
reg_post_num=1,
cls_out_channels=1024,
reg_offset_out_channels=256,
reg_cls_out_channels=256,
num_cls_fcs=1,
num_reg_fcs=0,
reg_class_agnostic=True,
norm_cfg=None,
bbox_coder=dict(
type='BucketingBBoxCoder', num_buckets=14, scale_factor=1.5),
loss_cls=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
loss_bbox_cls=dict(
type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
loss_bbox_reg=dict(type='SmoothL1Loss', beta=0.1,
loss_weight=1.0)),
dict(
type='SABLHead',
num_classes=80,
cls_in_channels=256,
reg_in_channels=256,
roi_feat_size=7,
reg_feat_up_ratio=2,
reg_pre_kernel=3,
reg_post_kernel=3,
reg_pre_num=2,
reg_post_num=1,
cls_out_channels=1024,
reg_offset_out_channels=256,
reg_cls_out_channels=256,
num_cls_fcs=1,
num_reg_fcs=0,
reg_class_agnostic=True,
norm_cfg=None,
bbox_coder=dict(
type='BucketingBBoxCoder', num_buckets=14, scale_factor=1.3),
loss_cls=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
loss_bbox_cls=dict(
type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
loss_bbox_reg=dict(type='SmoothL1Loss', beta=0.1, loss_weight=1.0))
]))
| 3,296 | 35.230769 | 79 | py |
mmdetection | mmdetection-master/configs/sabl/sabl_cascade_rcnn_r50_fpn_1x_coco.py | _base_ = [
'../_base_/models/cascade_rcnn_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
# model settings
model = dict(
roi_head=dict(bbox_head=[
dict(
type='SABLHead',
num_classes=80,
cls_in_channels=256,
reg_in_channels=256,
roi_feat_size=7,
reg_feat_up_ratio=2,
reg_pre_kernel=3,
reg_post_kernel=3,
reg_pre_num=2,
reg_post_num=1,
cls_out_channels=1024,
reg_offset_out_channels=256,
reg_cls_out_channels=256,
num_cls_fcs=1,
num_reg_fcs=0,
reg_class_agnostic=True,
norm_cfg=None,
bbox_coder=dict(
type='BucketingBBoxCoder', num_buckets=14, scale_factor=1.7),
loss_cls=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
loss_bbox_cls=dict(
type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
loss_bbox_reg=dict(type='SmoothL1Loss', beta=0.1,
loss_weight=1.0)),
dict(
type='SABLHead',
num_classes=80,
cls_in_channels=256,
reg_in_channels=256,
roi_feat_size=7,
reg_feat_up_ratio=2,
reg_pre_kernel=3,
reg_post_kernel=3,
reg_pre_num=2,
reg_post_num=1,
cls_out_channels=1024,
reg_offset_out_channels=256,
reg_cls_out_channels=256,
num_cls_fcs=1,
num_reg_fcs=0,
reg_class_agnostic=True,
norm_cfg=None,
bbox_coder=dict(
type='BucketingBBoxCoder', num_buckets=14, scale_factor=1.5),
loss_cls=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
loss_bbox_cls=dict(
type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
loss_bbox_reg=dict(type='SmoothL1Loss', beta=0.1,
loss_weight=1.0)),
dict(
type='SABLHead',
num_classes=80,
cls_in_channels=256,
reg_in_channels=256,
roi_feat_size=7,
reg_feat_up_ratio=2,
reg_pre_kernel=3,
reg_post_kernel=3,
reg_pre_num=2,
reg_post_num=1,
cls_out_channels=1024,
reg_offset_out_channels=256,
reg_cls_out_channels=256,
num_cls_fcs=1,
num_reg_fcs=0,
reg_class_agnostic=True,
norm_cfg=None,
bbox_coder=dict(
type='BucketingBBoxCoder', num_buckets=14, scale_factor=1.3),
loss_cls=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
loss_bbox_cls=dict(
type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
loss_bbox_reg=dict(type='SmoothL1Loss', beta=0.1, loss_weight=1.0))
]))
| 3,155 | 35.275862 | 79 | py |
mmdetection | mmdetection-master/configs/sabl/sabl_faster_rcnn_r101_fpn_1x_coco.py | _base_ = [
'../_base_/models/faster_rcnn_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')),
roi_head=dict(
bbox_head=dict(
_delete_=True,
type='SABLHead',
num_classes=80,
cls_in_channels=256,
reg_in_channels=256,
roi_feat_size=7,
reg_feat_up_ratio=2,
reg_pre_kernel=3,
reg_post_kernel=3,
reg_pre_num=2,
reg_post_num=1,
cls_out_channels=1024,
reg_offset_out_channels=256,
reg_cls_out_channels=256,
num_cls_fcs=1,
num_reg_fcs=0,
reg_class_agnostic=True,
norm_cfg=None,
bbox_coder=dict(
type='BucketingBBoxCoder', num_buckets=14, scale_factor=1.7),
loss_cls=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
loss_bbox_cls=dict(
type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
loss_bbox_reg=dict(type='SmoothL1Loss', beta=0.1,
loss_weight=1.0))))
| 1,369 | 34.128205 | 77 | py |
mmdetection | mmdetection-master/configs/sabl/sabl_faster_rcnn_r50_fpn_1x_coco.py | _base_ = [
'../_base_/models/faster_rcnn_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
roi_head=dict(
bbox_head=dict(
_delete_=True,
type='SABLHead',
num_classes=80,
cls_in_channels=256,
reg_in_channels=256,
roi_feat_size=7,
reg_feat_up_ratio=2,
reg_pre_kernel=3,
reg_post_kernel=3,
reg_pre_num=2,
reg_post_num=1,
cls_out_channels=1024,
reg_offset_out_channels=256,
reg_cls_out_channels=256,
num_cls_fcs=1,
num_reg_fcs=0,
reg_class_agnostic=True,
norm_cfg=None,
bbox_coder=dict(
type='BucketingBBoxCoder', num_buckets=14, scale_factor=1.7),
loss_cls=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
loss_bbox_cls=dict(
type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
loss_bbox_reg=dict(type='SmoothL1Loss', beta=0.1,
loss_weight=1.0))))
| 1,228 | 34.114286 | 77 | py |
mmdetection | mmdetection-master/configs/sabl/sabl_retinanet_r101_fpn_1x_coco.py | _base_ = [
'../_base_/models/retinanet_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
# model settings
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')),
bbox_head=dict(
_delete_=True,
type='SABLRetinaHead',
num_classes=80,
in_channels=256,
stacked_convs=4,
feat_channels=256,
approx_anchor_generator=dict(
type='AnchorGenerator',
octave_base_scale=4,
scales_per_octave=3,
ratios=[0.5, 1.0, 2.0],
strides=[8, 16, 32, 64, 128]),
square_anchor_generator=dict(
type='AnchorGenerator',
ratios=[1.0],
scales=[4],
strides=[8, 16, 32, 64, 128]),
bbox_coder=dict(
type='BucketingBBoxCoder', num_buckets=14, scale_factor=3.0),
loss_cls=dict(
type='FocalLoss',
use_sigmoid=True,
gamma=2.0,
alpha=0.25,
loss_weight=1.0),
loss_bbox_cls=dict(
type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.5),
loss_bbox_reg=dict(
type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.5)),
# training and testing settings
train_cfg=dict(
assigner=dict(
type='ApproxMaxIoUAssigner',
pos_iou_thr=0.5,
neg_iou_thr=0.4,
min_pos_iou=0.0,
ignore_iof_thr=-1),
allowed_border=-1,
pos_weight=-1,
debug=False))
# optimizer
optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
| 1,760 | 31.018182 | 73 | py |
mmdetection | mmdetection-master/configs/sabl/sabl_retinanet_r101_fpn_gn_1x_coco.py | _base_ = [
'../_base_/models/retinanet_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
# model settings
norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')),
bbox_head=dict(
_delete_=True,
type='SABLRetinaHead',
num_classes=80,
in_channels=256,
stacked_convs=4,
feat_channels=256,
approx_anchor_generator=dict(
type='AnchorGenerator',
octave_base_scale=4,
scales_per_octave=3,
ratios=[0.5, 1.0, 2.0],
strides=[8, 16, 32, 64, 128]),
square_anchor_generator=dict(
type='AnchorGenerator',
ratios=[1.0],
scales=[4],
strides=[8, 16, 32, 64, 128]),
norm_cfg=norm_cfg,
bbox_coder=dict(
type='BucketingBBoxCoder', num_buckets=14, scale_factor=3.0),
loss_cls=dict(
type='FocalLoss',
use_sigmoid=True,
gamma=2.0,
alpha=0.25,
loss_weight=1.0),
loss_bbox_cls=dict(
type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.5),
loss_bbox_reg=dict(
type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.5)),
# training and testing settings
train_cfg=dict(
assigner=dict(
type='ApproxMaxIoUAssigner',
pos_iou_thr=0.5,
neg_iou_thr=0.4,
min_pos_iou=0.0,
ignore_iof_thr=-1),
allowed_border=-1,
pos_weight=-1,
debug=False))
# optimizer
optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
| 1,849 | 31.45614 | 73 | py |
mmdetection | mmdetection-master/configs/sabl/sabl_retinanet_r101_fpn_gn_2x_ms_480_960_coco.py | _base_ = [
'../_base_/models/retinanet_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py'
]
# model settings
norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')),
bbox_head=dict(
_delete_=True,
type='SABLRetinaHead',
num_classes=80,
in_channels=256,
stacked_convs=4,
feat_channels=256,
approx_anchor_generator=dict(
type='AnchorGenerator',
octave_base_scale=4,
scales_per_octave=3,
ratios=[0.5, 1.0, 2.0],
strides=[8, 16, 32, 64, 128]),
square_anchor_generator=dict(
type='AnchorGenerator',
ratios=[1.0],
scales=[4],
strides=[8, 16, 32, 64, 128]),
norm_cfg=norm_cfg,
bbox_coder=dict(
type='BucketingBBoxCoder', num_buckets=14, scale_factor=3.0),
loss_cls=dict(
type='FocalLoss',
use_sigmoid=True,
gamma=2.0,
alpha=0.25,
loss_weight=1.0),
loss_bbox_cls=dict(
type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.5),
loss_bbox_reg=dict(
type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.5)),
# training and testing settings
train_cfg=dict(
assigner=dict(
type='ApproxMaxIoUAssigner',
pos_iou_thr=0.5,
neg_iou_thr=0.4,
min_pos_iou=0.0,
ignore_iof_thr=-1),
allowed_border=-1,
pos_weight=-1,
debug=False))
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True),
dict(
type='Resize',
img_scale=[(1333, 480), (1333, 960)],
multiscale_mode='range',
keep_ratio=True),
dict(type='RandomFlip', flip_ratio=0.5),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
]
data = dict(train=dict(pipeline=train_pipeline))
# optimizer
optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
| 2,474 | 32.445946 | 77 | py |
mmdetection | mmdetection-master/configs/sabl/sabl_retinanet_r101_fpn_gn_2x_ms_640_800_coco.py | _base_ = [
'../_base_/models/retinanet_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py'
]
# model settings
norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')),
bbox_head=dict(
_delete_=True,
type='SABLRetinaHead',
num_classes=80,
in_channels=256,
stacked_convs=4,
feat_channels=256,
approx_anchor_generator=dict(
type='AnchorGenerator',
octave_base_scale=4,
scales_per_octave=3,
ratios=[0.5, 1.0, 2.0],
strides=[8, 16, 32, 64, 128]),
square_anchor_generator=dict(
type='AnchorGenerator',
ratios=[1.0],
scales=[4],
strides=[8, 16, 32, 64, 128]),
norm_cfg=norm_cfg,
bbox_coder=dict(
type='BucketingBBoxCoder', num_buckets=14, scale_factor=3.0),
loss_cls=dict(
type='FocalLoss',
use_sigmoid=True,
gamma=2.0,
alpha=0.25,
loss_weight=1.0),
loss_bbox_cls=dict(
type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.5),
loss_bbox_reg=dict(
type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.5)),
# training and testing settings
train_cfg=dict(
assigner=dict(
type='ApproxMaxIoUAssigner',
pos_iou_thr=0.5,
neg_iou_thr=0.4,
min_pos_iou=0.0,
ignore_iof_thr=-1),
allowed_border=-1,
pos_weight=-1,
debug=False))
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True),
dict(
type='Resize',
img_scale=[(1333, 640), (1333, 800)],
multiscale_mode='range',
keep_ratio=True),
dict(type='RandomFlip', flip_ratio=0.5),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
]
data = dict(train=dict(pipeline=train_pipeline))
# optimizer
optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
| 2,474 | 32.445946 | 77 | py |
mmdetection | mmdetection-master/configs/sabl/sabl_retinanet_r50_fpn_1x_coco.py | _base_ = [
'../_base_/models/retinanet_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
# model settings
model = dict(
bbox_head=dict(
_delete_=True,
type='SABLRetinaHead',
num_classes=80,
in_channels=256,
stacked_convs=4,
feat_channels=256,
approx_anchor_generator=dict(
type='AnchorGenerator',
octave_base_scale=4,
scales_per_octave=3,
ratios=[0.5, 1.0, 2.0],
strides=[8, 16, 32, 64, 128]),
square_anchor_generator=dict(
type='AnchorGenerator',
ratios=[1.0],
scales=[4],
strides=[8, 16, 32, 64, 128]),
bbox_coder=dict(
type='BucketingBBoxCoder', num_buckets=14, scale_factor=3.0),
loss_cls=dict(
type='FocalLoss',
use_sigmoid=True,
gamma=2.0,
alpha=0.25,
loss_weight=1.0),
loss_bbox_cls=dict(
type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.5),
loss_bbox_reg=dict(
type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.5)),
# training and testing settings
train_cfg=dict(
assigner=dict(
type='ApproxMaxIoUAssigner',
pos_iou_thr=0.5,
neg_iou_thr=0.4,
min_pos_iou=0.0,
ignore_iof_thr=-1),
allowed_border=-1,
pos_weight=-1,
debug=False))
# optimizer
optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
| 1,619 | 30.764706 | 73 | py |
mmdetection | mmdetection-master/configs/sabl/sabl_retinanet_r50_fpn_gn_1x_coco.py | _base_ = [
'../_base_/models/retinanet_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
# model settings
norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
model = dict(
bbox_head=dict(
_delete_=True,
type='SABLRetinaHead',
num_classes=80,
in_channels=256,
stacked_convs=4,
feat_channels=256,
approx_anchor_generator=dict(
type='AnchorGenerator',
octave_base_scale=4,
scales_per_octave=3,
ratios=[0.5, 1.0, 2.0],
strides=[8, 16, 32, 64, 128]),
square_anchor_generator=dict(
type='AnchorGenerator',
ratios=[1.0],
scales=[4],
strides=[8, 16, 32, 64, 128]),
norm_cfg=norm_cfg,
bbox_coder=dict(
type='BucketingBBoxCoder', num_buckets=14, scale_factor=3.0),
loss_cls=dict(
type='FocalLoss',
use_sigmoid=True,
gamma=2.0,
alpha=0.25,
loss_weight=1.0),
loss_bbox_cls=dict(
type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.5),
loss_bbox_reg=dict(
type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.5)),
# training and testing settings
train_cfg=dict(
assigner=dict(
type='ApproxMaxIoUAssigner',
pos_iou_thr=0.5,
neg_iou_thr=0.4,
min_pos_iou=0.0,
ignore_iof_thr=-1),
allowed_border=-1,
pos_weight=-1,
debug=False))
# optimizer
optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
| 1,708 | 31.245283 | 73 | py |
mmdetection | mmdetection-master/configs/scnet/README.md | # SCNet
> [SCNet: Training Inference Sample Consistency for Instance Segmentation](https://arxiv.org/abs/2012.10150)
<!-- [ALGORITHM] -->
## Abstract
<!-- [ABSTRACT] -->
Cascaded architectures have brought significant performance improvement in object detection and instance segmentation. However, there are lingering issues regarding the disparity in the Intersection-over-Union (IoU) distribution of the samples between training and inference. This disparity can potentially exacerbate detection accuracy. This paper proposes an architecture referred to as Sample Consistency Network (SCNet) to ensure that the IoU distribution of the samples at training time is close to that at inference time. Furthermore, SCNet incorporates feature relay and utilizes global contextual information to further reinforce the reciprocal relationships among classifying, detecting, and segmenting sub-tasks. Extensive experiments on the standard COCO dataset reveal the effectiveness of the proposed method over multiple evaluation metrics, including box AP, mask AP, and inference speed. In particular, while running 38% faster, the proposed SCNet improves the AP of the box and mask predictions by respectively 1.3 and 2.3 points compared to the strong Cascade Mask R-CNN baseline.
<div align=center>
<img src="https://user-images.githubusercontent.com/40661020/143974840-8fed75f3-661e-4e2a-a210-acf4ab5f42a3.png"/>
</div>
## Dataset
SCNet requires COCO and [COCO-stuff](http://calvin.inf.ed.ac.uk/wp-content/uploads/data/cocostuffdataset/stuffthingmaps_trainval2017.zip) dataset for training. You need to download and extract it in the COCO dataset path.
The directory should be like this.
```none
mmdetection
├── mmdet
├── tools
├── configs
├── data
│ ├── coco
│ │ ├── annotations
│ │ ├── train2017
│ │ ├── val2017
│ │ ├── test2017
| | ├── stuffthingmaps
```
## Results and Models
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 speed (fps) | box AP | mask AP | TTA box AP | TTA mask AP | Config | Download |
| :-------------: | :-----: | :-----: | :------: | :-------------: | :----: | :-----: | :--------: | :---------: | :------------------------------------------------------------------------------------------------------------: | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
| R-50-FPN | pytorch | 1x | 7.0 | 6.2 | 43.5 | 39.2 | 44.8 | 40.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/scnet/scnet_r50_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/scnet/scnet_r50_fpn_1x_coco/scnet_r50_fpn_1x_coco-c3f09857.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/scnet/scnet_r50_fpn_1x_coco/scnet_r50_fpn_1x_coco_20210117_192725.log.json) |
| R-50-FPN | pytorch | 20e | 7.0 | 6.2 | 44.5 | 40.0 | 45.8 | 41.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/scnet/scnet_r50_fpn_20e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/scnet/scnet_r50_fpn_20e_coco/scnet_r50_fpn_20e_coco-a569f645.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/scnet/scnet_r50_fpn_20e_coco/scnet_r50_fpn_20e_coco_20210116_060148.log.json) |
| R-101-FPN | pytorch | 20e | 8.9 | 5.8 | 45.8 | 40.9 | 47.3 | 42.7 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/scnet/scnet_r101_fpn_20e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/scnet/scnet_r101_fpn_20e_coco/scnet_r101_fpn_20e_coco-294e312c.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/scnet/scnet_r101_fpn_20e_coco/scnet_r101_fpn_20e_coco_20210118_175824.log.json) |
| X-101-64x4d-FPN | pytorch | 20e | 13.2 | 4.9 | 47.5 | 42.3 | 48.9 | 44.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/scnet/scnet_x101_64x4d_fpn_20e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/scnet/scnet_x101_64x4d_fpn_20e_coco/scnet_x101_64x4d_fpn_20e_coco-fb09dec9.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/scnet/scnet_x101_64x4d_fpn_20e_coco/scnet_x101_64x4d_fpn_20e_coco_20210120_045959.log.json) |
### Notes
- Training hyper-parameters are identical to those of [HTC](https://github.com/open-mmlab/mmdetection/tree/master/configs/htc).
- TTA means Test Time Augmentation, which applies horizontal flip and multi-scale testing. Refer to [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/scnet/scnet_r50_fpn_1x_coco.py).
## Citation
We provide the code for reproducing experiment results of [SCNet](https://arxiv.org/abs/2012.10150).
```latex
@inproceedings{vu2019cascade,
title={SCNet: Training Inference Sample Consistency for Instance Segmentation},
author={Vu, Thang and Haeyong, Kang and Yoo, Chang D},
booktitle={AAAI},
year={2021}
}
```
| 5,789 | 89.46875 | 1,098 | md |
mmdetection | mmdetection-master/configs/scnet/metafile.yml | Collections:
- Name: SCNet
Metadata:
Training Data: COCO
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Resources: 8x V100 GPUs
Architecture:
- FPN
- ResNet
- SCNet
Paper:
URL: https://arxiv.org/abs/2012.10150
Title: 'SCNet: Training Inference Sample Consistency for Instance Segmentation'
README: configs/scnet/README.md
Code:
URL: https://github.com/open-mmlab/mmdetection/blob/v2.9.0/mmdet/models/detectors/scnet.py#L6
Version: v2.9.0
Models:
- Name: scnet_r50_fpn_1x_coco
In Collection: SCNet
Config: configs/scnet/scnet_r50_fpn_1x_coco.py
Metadata:
Training Memory (GB): 7.0
inference time (ms/im):
- value: 161.29
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (800, 1333)
Epochs: 12
Results:
- Task: Object Detection
Dataset: COCO
Metrics:
box AP: 43.5
- Task: Instance Segmentation
Dataset: COCO
Metrics:
mask AP: 39.2
Weights: https://download.openmmlab.com/mmdetection/v2.0/scnet/scnet_r50_fpn_1x_coco/scnet_r50_fpn_1x_coco-c3f09857.pth
- Name: scnet_r50_fpn_20e_coco
In Collection: SCNet
Config: configs/scnet/scnet_r50_fpn_20e_coco.py
Metadata:
Training Memory (GB): 7.0
inference time (ms/im):
- value: 161.29
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (800, 1333)
Epochs: 20
Results:
- Task: Object Detection
Dataset: COCO
Metrics:
box AP: 44.5
- Task: Instance Segmentation
Dataset: COCO
Metrics:
mask AP: 40.0
Weights: https://download.openmmlab.com/mmdetection/v2.0/scnet/scnet_r50_fpn_20e_coco/scnet_r50_fpn_20e_coco-a569f645.pth
- Name: scnet_r101_fpn_20e_coco
In Collection: SCNet
Config: configs/scnet/scnet_r101_fpn_20e_coco.py
Metadata:
Training Memory (GB): 8.9
inference time (ms/im):
- value: 172.41
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (800, 1333)
Epochs: 20
Results:
- Task: Object Detection
Dataset: COCO
Metrics:
box AP: 45.8
- Task: Instance Segmentation
Dataset: COCO
Metrics:
mask AP: 40.9
Weights: https://download.openmmlab.com/mmdetection/v2.0/scnet/scnet_r101_fpn_20e_coco/scnet_r101_fpn_20e_coco-294e312c.pth
- Name: scnet_x101_64x4d_fpn_20e_coco
In Collection: SCNet
Config: configs/scnet/scnet_x101_64x4d_fpn_20e_coco.py
Metadata:
Training Memory (GB): 13.2
inference time (ms/im):
- value: 204.08
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (800, 1333)
Epochs: 20
Results:
- Task: Object Detection
Dataset: COCO
Metrics:
box AP: 47.5
- Task: Instance Segmentation
Dataset: COCO
Metrics:
mask AP: 42.3
Weights: https://download.openmmlab.com/mmdetection/v2.0/scnet/scnet_x101_64x4d_fpn_20e_coco/scnet_x101_64x4d_fpn_20e_coco-fb09dec9.pth
| 3,359 | 27.717949 | 139 | yml |
mmdetection | mmdetection-master/configs/scnet/scnet_r101_fpn_20e_coco.py | _base_ = './scnet_r50_fpn_20e_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 194 | 26.857143 | 61 | py |
mmdetection | mmdetection-master/configs/scnet/scnet_r50_fpn_1x_coco.py | _base_ = '../htc/htc_r50_fpn_1x_coco.py'
# model settings
model = dict(
type='SCNet',
roi_head=dict(
_delete_=True,
type='SCNetRoIHead',
num_stages=3,
stage_loss_weights=[1, 0.5, 0.25],
bbox_roi_extractor=dict(
type='SingleRoIExtractor',
roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0),
out_channels=256,
featmap_strides=[4, 8, 16, 32]),
bbox_head=[
dict(
type='SCNetBBoxHead',
num_shared_fcs=2,
in_channels=256,
fc_out_channels=1024,
roi_feat_size=7,
num_classes=80,
bbox_coder=dict(
type='DeltaXYWHBBoxCoder',
target_means=[0., 0., 0., 0.],
target_stds=[0.1, 0.1, 0.2, 0.2]),
reg_class_agnostic=True,
loss_cls=dict(
type='CrossEntropyLoss',
use_sigmoid=False,
loss_weight=1.0),
loss_bbox=dict(type='SmoothL1Loss', beta=1.0,
loss_weight=1.0)),
dict(
type='SCNetBBoxHead',
num_shared_fcs=2,
in_channels=256,
fc_out_channels=1024,
roi_feat_size=7,
num_classes=80,
bbox_coder=dict(
type='DeltaXYWHBBoxCoder',
target_means=[0., 0., 0., 0.],
target_stds=[0.05, 0.05, 0.1, 0.1]),
reg_class_agnostic=True,
loss_cls=dict(
type='CrossEntropyLoss',
use_sigmoid=False,
loss_weight=1.0),
loss_bbox=dict(type='SmoothL1Loss', beta=1.0,
loss_weight=1.0)),
dict(
type='SCNetBBoxHead',
num_shared_fcs=2,
in_channels=256,
fc_out_channels=1024,
roi_feat_size=7,
num_classes=80,
bbox_coder=dict(
type='DeltaXYWHBBoxCoder',
target_means=[0., 0., 0., 0.],
target_stds=[0.033, 0.033, 0.067, 0.067]),
reg_class_agnostic=True,
loss_cls=dict(
type='CrossEntropyLoss',
use_sigmoid=False,
loss_weight=1.0),
loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0))
],
mask_roi_extractor=dict(
type='SingleRoIExtractor',
roi_layer=dict(type='RoIAlign', output_size=14, sampling_ratio=0),
out_channels=256,
featmap_strides=[4, 8, 16, 32]),
mask_head=dict(
type='SCNetMaskHead',
num_convs=12,
in_channels=256,
conv_out_channels=256,
num_classes=80,
conv_to_res=True,
loss_mask=dict(
type='CrossEntropyLoss', use_mask=True, loss_weight=1.0)),
semantic_roi_extractor=dict(
type='SingleRoIExtractor',
roi_layer=dict(type='RoIAlign', output_size=14, sampling_ratio=0),
out_channels=256,
featmap_strides=[8]),
semantic_head=dict(
type='SCNetSemanticHead',
num_ins=5,
fusion_level=1,
num_convs=4,
in_channels=256,
conv_out_channels=256,
num_classes=183,
loss_seg=dict(
type='CrossEntropyLoss', ignore_index=255, loss_weight=0.2),
conv_to_res=True),
glbctx_head=dict(
type='GlobalContextHead',
num_convs=4,
in_channels=256,
conv_out_channels=256,
num_classes=80,
loss_weight=3.0,
conv_to_res=True),
feat_relay_head=dict(
type='FeatureRelayHead',
in_channels=1024,
out_conv_channels=256,
roi_feat_size=7,
scale_factor=2)))
# uncomment below code to enable test time augmentations
# img_norm_cfg = dict(
# mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
# test_pipeline = [
# dict(type='LoadImageFromFile'),
# dict(
# type='MultiScaleFlipAug',
# img_scale=[(600, 900), (800, 1200), (1000, 1500), (1200, 1800),
# (1400, 2100)],
# flip=True,
# transforms=[
# dict(type='Resize', keep_ratio=True),
# dict(type='RandomFlip', flip_ratio=0.5),
# dict(type='Normalize', **img_norm_cfg),
# dict(type='Pad', size_divisor=32),
# dict(type='ImageToTensor', keys=['img']),
# dict(type='Collect', keys=['img']),
# ])
# ]
# data = dict(
# val=dict(pipeline=test_pipeline),
# test=dict(pipeline=test_pipeline))
| 5,020 | 35.649635 | 79 | py |
mmdetection | mmdetection-master/configs/scnet/scnet_r50_fpn_20e_coco.py | _base_ = './scnet_r50_fpn_1x_coco.py'
# learning policy
lr_config = dict(step=[16, 19])
runner = dict(type='EpochBasedRunner', max_epochs=20)
| 142 | 27.6 | 53 | py |
mmdetection | mmdetection-master/configs/scnet/scnet_x101_64x4d_fpn_20e_coco.py | _base_ = './scnet_r50_fpn_20e_coco.py'
model = dict(
backbone=dict(
type='ResNeXt',
depth=101,
groups=64,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style='pytorch',
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d')))
| 440 | 26.5625 | 76 | py |
mmdetection | mmdetection-master/configs/scnet/scnet_x101_64x4d_fpn_8x1_20e_coco.py | _base_ = './scnet_x101_64x4d_fpn_20e_coco.py'
data = dict(samples_per_gpu=1, workers_per_gpu=1)
optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
# NOTE: `auto_scale_lr` is for automatically scaling LR,
# USER SHOULD NOT CHANGE ITS VALUES.
# base_batch_size = (8 GPUs) x (1 samples per GPU)
auto_scale_lr = dict(base_batch_size=8)
| 355 | 38.555556 | 72 | py |
mmdetection | mmdetection-master/configs/scratch/README.md | # Scratch
> [Rethinking ImageNet Pre-training](https://arxiv.org/abs/1811.08883)
<!-- [ALGORITHM] -->
## Abstract
We report competitive results on object detection and instance segmentation on the COCO dataset using standard models trained from random initialization. The results are no worse than their ImageNet pre-training counterparts even when using the hyper-parameters of the baseline system (Mask R-CNN) that were optimized for fine-tuning pre-trained models, with the sole exception of increasing the number of training iterations so the randomly initialized models may converge. Training from random initialization is surprisingly robust; our results hold even when: (i) using only 10% of the training data, (ii) for deeper and wider models, and (iii) for multiple tasks and metrics. Experiments show that ImageNet pre-training speeds up convergence early in training, but does not necessarily provide regularization or improve final target task accuracy. To push the envelope we demonstrate 50.9 AP on COCO object detection without using any external data---a result on par with the top COCO 2017 competition results that used ImageNet pre-training. These observations challenge the conventional wisdom of ImageNet pre-training for dependent tasks and we expect these discoveries will encourage people to rethink the current de facto paradigm of \`pre-training and fine-tuning' in computer vision.
<div align=center>
<img src="https://user-images.githubusercontent.com/40661020/143974572-69c4f57d-0d6d-4f56-ba91-23f8a65a2a77.png" height="300"/>
</div>
## Results and Models
| Model | Backbone | Style | Lr schd | box AP | mask AP | Config | Download |
| :----------: | :------: | :-----: | :-----: | :----: | :-----: | :---------------------------------------------------------------------------------------------------------------------------: | :-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
| Faster R-CNN | R-50-FPN | pytorch | 6x | 40.7 | | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/scratch/faster_rcnn_r50_fpn_gn-all_scratch_6x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/scratch/faster_rcnn_r50_fpn_gn-all_scratch_6x_coco/scratch_faster_rcnn_r50_fpn_gn_6x_bbox_mAP-0.407_20200201_193013-90813d01.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/scratch/faster_rcnn_r50_fpn_gn-all_scratch_6x_coco/scratch_faster_rcnn_r50_fpn_gn_6x_20200201_193013.log.json) |
| Mask R-CNN | R-50-FPN | pytorch | 6x | 41.2 | 37.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/scratch/mask_rcnn_r50_fpn_gn-all_scratch_6x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/scratch/mask_rcnn_r50_fpn_gn-all_scratch_6x_coco/scratch_mask_rcnn_r50_fpn_gn_6x_bbox_mAP-0.412__segm_mAP-0.374_20200201_193051-1e190a40.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/scratch/mask_rcnn_r50_fpn_gn-all_scratch_6x_coco/scratch_mask_rcnn_r50_fpn_gn_6x_20200201_193051.log.json) |
Note:
- The above models are trained with 16 GPUs.
## Citation
```latex
@article{he2018rethinking,
title={Rethinking imagenet pre-training},
author={He, Kaiming and Girshick, Ross and Doll{\'a}r, Piotr},
journal={arXiv preprint arXiv:1811.08883},
year={2018}
}
```
| 4,105 | 113.055556 | 1,294 | md |
mmdetection | mmdetection-master/configs/scratch/faster_rcnn_r50_fpn_gn-all_scratch_6x_coco.py | _base_ = [
'../_base_/models/faster_rcnn_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
model = dict(
backbone=dict(
frozen_stages=-1,
zero_init_residual=False,
norm_cfg=norm_cfg,
init_cfg=None),
neck=dict(norm_cfg=norm_cfg),
roi_head=dict(
bbox_head=dict(
type='Shared4Conv1FCBBoxHead',
conv_out_channels=256,
norm_cfg=norm_cfg)))
# optimizer
optimizer = dict(paramwise_cfg=dict(norm_decay_mult=0))
optimizer_config = dict(_delete_=True, grad_clip=None)
# learning policy
lr_config = dict(warmup_ratio=0.1, step=[65, 71])
runner = dict(type='EpochBasedRunner', max_epochs=73)
| 816 | 31.68 | 72 | py |
mmdetection | mmdetection-master/configs/scratch/mask_rcnn_r50_fpn_gn-all_scratch_6x_coco.py | _base_ = [
'../_base_/models/mask_rcnn_r50_fpn.py',
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
model = dict(
backbone=dict(
frozen_stages=-1,
zero_init_residual=False,
norm_cfg=norm_cfg,
init_cfg=None),
neck=dict(norm_cfg=norm_cfg),
roi_head=dict(
bbox_head=dict(
type='Shared4Conv1FCBBoxHead',
conv_out_channels=256,
norm_cfg=norm_cfg),
mask_head=dict(norm_cfg=norm_cfg)))
# optimizer
optimizer = dict(paramwise_cfg=dict(norm_decay_mult=0))
optimizer_config = dict(_delete_=True, grad_clip=None)
# learning policy
lr_config = dict(warmup_ratio=0.1, step=[65, 71])
runner = dict(type='EpochBasedRunner', max_epochs=73)
| 856 | 31.961538 | 72 | py |
mmdetection | mmdetection-master/configs/scratch/metafile.yml | Collections:
- Name: Rethinking ImageNet Pre-training
Metadata:
Training Data: COCO
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Resources: 8x V100 GPUs
Architecture:
- FPN
- RPN
- ResNet
Paper:
URL: https://arxiv.org/abs/1811.08883
Title: 'Rethinking ImageNet Pre-training'
README: configs/scratch/README.md
Code:
URL: https://github.com/open-mmlab/mmdetection/blob/v2.0.0/configs/scratch/faster_rcnn_r50_fpn_gn-all_scratch_6x_coco.py
Version: v2.0.0
Models:
- Name: faster_rcnn_r50_fpn_gn-all_scratch_6x_coco
In Collection: Rethinking ImageNet Pre-training
Config: configs/scratch/faster_rcnn_r50_fpn_gn-all_scratch_6x_coco.py
Metadata:
Epochs: 72
Results:
- Task: Object Detection
Dataset: COCO
Metrics:
box AP: 40.7
Weights: https://download.openmmlab.com/mmdetection/v2.0/scratch/faster_rcnn_r50_fpn_gn-all_scratch_6x_coco/scratch_faster_rcnn_r50_fpn_gn_6x_bbox_mAP-0.407_20200201_193013-90813d01.pth
- Name: mask_rcnn_r50_fpn_gn-all_scratch_6x_coco
In Collection: Rethinking ImageNet Pre-training
Config: configs/scratch/mask_rcnn_r50_fpn_gn-all_scratch_6x_coco.py
Metadata:
Epochs: 72
Results:
- Task: Object Detection
Dataset: COCO
Metrics:
box AP: 41.2
- Task: Instance Segmentation
Dataset: COCO
Metrics:
mask AP: 37.4
Weights: https://download.openmmlab.com/mmdetection/v2.0/scratch/mask_rcnn_r50_fpn_gn-all_scratch_6x_coco/scratch_mask_rcnn_r50_fpn_gn_6x_bbox_mAP-0.412__segm_mAP-0.374_20200201_193051-1e190a40.pth
| 1,707 | 33.857143 | 201 | yml |
mmdetection | mmdetection-master/configs/seesaw_loss/README.md | # Seesaw Loss
> [Seesaw Loss for Long-Tailed Instance Segmentation](https://arxiv.org/abs/2008.10032)
<!-- [ALGORITHM] -->
## Abstract
Instance segmentation has witnessed a remarkable progress on class-balanced benchmarks. However, they fail to perform as accurately in real-world scenarios, where the category distribution of objects naturally comes with a long tail. Instances of head classes dominate a long-tailed dataset and they serve as negative samples of tail categories. The overwhelming gradients of negative samples on tail classes lead to a biased learning process for classifiers. Consequently, objects of tail categories are more likely to be misclassified as backgrounds or head categories. To tackle this problem, we propose Seesaw Loss to dynamically re-balance gradients of positive and negative samples for each category, with two complementary factors, i.e., mitigation factor and compensation factor. The mitigation factor reduces punishments to tail categories w.r.t. the ratio of cumulative training instances between different categories. Meanwhile, the compensation factor increases the penalty of misclassified instances to avoid false positives of tail categories. We conduct extensive experiments on Seesaw Loss with mainstream frameworks and different data sampling strategies. With a simple end-to-end training pipeline, Seesaw Loss obtains significant gains over Cross-Entropy Loss, and achieves state-of-the-art performance on LVIS dataset without bells and whistles.
<div align=center>
<img src="https://user-images.githubusercontent.com/40661020/143974715-d181abe5-d0a2-40d3-a2bd-17d8c60b89b8.png"/>
</div>
- Please setup [LVIS dataset](../lvis/README.md) for MMDetection.
- RFS indicates to use oversample strategy [here](../../docs/tutorials/customize_dataset.md#class-balanced-dataset) with oversample threshold `1e-3`.
## Results and models of Seasaw Loss on LVIS v1 dataset
| Method | Backbone | Style | Lr schd | Data Sampler | Norm Mask | box AP | mask AP | Config | Download |
| :----------------: | :-------: | :-----: | :-----: | :----------: | :-------: | :----: | :-----: | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
| Mask R-CNN | R-50-FPN | pytorch | 2x | random | N | 25.6 | 25.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/seesaw_loss/mask_rcnn_r50_fpn_random_seesaw_loss_mstrain_2x_lvis_v1.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/seesaw_loss/mask_rcnn_r50_fpn_random_seesaw_loss_mstrain_2x_lvis_v1-a698dd3d.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/seesaw_loss/mask_rcnn_r50_fpn_random_seesaw_loss_mstrain_2x_lvis_v1.log.json) |
| Mask R-CNN | R-50-FPN | pytorch | 2x | random | Y | 25.6 | 25.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/seesaw_loss/mask_rcnn_r50_fpn_random_seesaw_loss_normed_mask_mstrain_2x_lvis_v1.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/seesaw_loss/mask_rcnn_r50_fpn_random_seesaw_loss_normed_mask_mstrain_2x_lvis_v1-a1c11314.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/seesaw_loss/mask_rcnn_r50_fpn_random_seesaw_loss_normed_mask_mstrain_2x_lvis_v1.log.json) |
| Mask R-CNN | R-101-FPN | pytorch | 2x | random | N | 27.4 | 26.7 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/mask_rcnn_r101_fpn_random_seesaw_loss_mstrain_2x_lvis_v1.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/seesaw_loss/mask_rcnn_r101_fpn_random_seesaw_loss_mstrain_2x_lvis_v1-8e6e6dd5.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/seesaw_loss/mask_rcnn_r101_fpn_random_seesaw_loss_mstrain_2x_lvis_v1.log.json) |
| Mask R-CNN | R-101-FPN | pytorch | 2x | random | Y | 27.2 | 27.3 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/seesaw_loss/mask_rcnn_r101_fpn_random_seesaw_loss_normed_mask_mstrain_2x_lvis_v1.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/seesaw_loss/mask_rcnn_r101_fpn_random_seesaw_loss_normed_mask_mstrain_2x_lvis_v1-a0b59c42.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/seesaw_loss/mask_rcnn_r101_fpn_random_seesaw_loss_normed_mask_mstrain_2x_lvis_v1.log.json) |
| Mask R-CNN | R-50-FPN | pytorch | 2x | RFS | N | 27.6 | 26.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/seesaw_loss/mask_rcnn_r50_fpn_sample1e-3_seesaw_loss_mstrain_2x_lvis_v1.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/seesaw_loss/mask_rcnn_r50_fpn_sample1e-3_seesaw_loss_mstrain_2x_lvis_v1-392a804b.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/seesaw_loss/mask_rcnn_r50_fpn_sample1e-3_seesaw_loss_mstrain_2x_lvis_v1.log.json) |
| Mask R-CNN | R-50-FPN | pytorch | 2x | RFS | Y | 27.6 | 26.8 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/seesaw_loss/mask_rcnn_r50_fpn_sample1e-3_seesaw_loss_normed_mask_mstrain_2x_lvis_v1.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/seesaw_loss/mask_rcnn_r50_fpn_sample1e-3_seesaw_loss_normed_mask_mstrain_2x_lvis_v1-cd0f6a12.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/seesaw_loss/mask_rcnn_r50_fpn_sample1e-3_seesaw_loss_normed_mask_mstrain_2x_lvis_v1.log.json) |
| Mask R-CNN | R-101-FPN | pytorch | 2x | RFS | N | 28.9 | 27.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/seesaw_loss/mask_rcnn_r101_fpn_sample1e-3_seesaw_loss_mstrain_2x_lvis_v1.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/seesaw_loss/mask_rcnn_r101_fpn_sample1e-3_seesaw_loss_mstrain_2x_lvis_v1-e68eb464.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/seesaw_loss/mask_rcnn_r101_fpn_sample1e-3_seesaw_loss_mstrain_2x_lvis_v1.log.json) |
| Mask R-CNN | R-101-FPN | pytorch | 2x | RFS | Y | 28.9 | 28.2 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/seesaw_loss/mask_rcnn_r101_fpn_sample1e-3_seesaw_loss_normed_mask_mstrain_2x_lvis_v1.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/seesaw_loss/mask_rcnn_r101_fpn_sample1e-3_seesaw_loss_normed_mask_mstrain_2x_lvis_v1-1d817139.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/seesaw_loss/mask_rcnn_r101_fpn_sample1e-3_seesaw_loss_normed_mask_mstrain_2x_lvis_v1.log.json) |
| Cascade Mask R-CNN | R-101-FPN | pytorch | 2x | random | N | 33.1 | 29.2 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/seesaw_loss/cascade_mask_rcnn_r101_fpn_random_seesaw_loss_mstrain_2x_lvis_v1.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/seesaw_loss/cascade_mask_rcnn_r101_fpn_random_seesaw_loss_mstrain_2x_lvis_v1-71e2215e.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/seesaw_loss/cascade_mask_rcnn_r101_fpn_random_seesaw_loss_mstrain_2x_lvis_v1.log.json) |
| Cascade Mask R-CNN | R-101-FPN | pytorch | 2x | random | Y | 33.0 | 30.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/seesaw_loss/cascade_mask_rcnn_r101_fpn_random_seesaw_loss_normed_mask_mstrain_2x_lvis_v1.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/seesaw_loss/cascade_mask_rcnn_r101_fpn_random_seesaw_loss_normed_mask_mstrain_2x_lvis_v1-8b5a6745.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/seesaw_loss/cascade_mask_rcnn_r101_fpn_random_seesaw_loss_normed_mask_mstrain_2x_lvis_v1.log.json) |
| Cascade Mask R-CNN | R-101-FPN | pytorch | 2x | RFS | N | 30.0 | 29.3 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/seesaw_loss/cascade_mask_rcnn_r101_fpn_sample1e-3_seesaw_loss_mstrain_2x_lvis_v1.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/seesaw_loss/cascade_mask_rcnn_r101_fpn_sample1e-3_seesaw_loss_mstrain_2x_lvis_v1-5d8ca2a4.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/seesaw_loss/cascade_mask_rcnn_r101_fpn_sample1e-3_seesaw_loss_mstrain_2x_lvis_v1.log.json) |
| Cascade Mask R-CNN | R-101-FPN | pytorch | 2x | RFS | Y | 32.8 | 30.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/seesaw_loss/cascade_mask_rcnn_r101_fpn_sample1e-3_seesaw_loss_normed_mask_mstrain_2x_lvis_v1.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/seesaw_loss/cascade_mask_rcnn_r101_fpn_sample1e-3_seesaw_loss_normed_mask_mstrain_2x_lvis_v1-c8551505.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/seesaw_loss/cascade_mask_rcnn_r101_fpn_sample1e-3_seesaw_loss_normed_mask_mstrain_2x_lvis_v1.log.json) |
## Citation
We provide config files to reproduce the instance segmentation performance in the CVPR 2021 paper for [Seesaw Loss for Long-Tailed Instance Segmentation](https://arxiv.org/abs/2008.10032).
```latex
@inproceedings{wang2021seesaw,
title={Seesaw Loss for Long-Tailed Instance Segmentation},
author={Jiaqi Wang and Wenwei Zhang and Yuhang Zang and Yuhang Cao and Jiangmiao Pang and Tao Gong and Kai Chen and Ziwei Liu and Chen Change Loy and Dahua Lin},
booktitle={Proceedings of the {IEEE} Conference on Computer Vision and Pattern Recognition},
year={2021}
}
```
| 10,851 | 225.083333 | 1,365 | md |
mmdetection | mmdetection-master/configs/seesaw_loss/cascade_mask_rcnn_r101_fpn_random_seesaw_loss_mstrain_2x_lvis_v1.py | _base_ = [
'../_base_/models/cascade_mask_rcnn_r50_fpn.py',
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py'
]
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')),
roi_head=dict(
bbox_head=[
dict(
type='Shared2FCBBoxHead',
in_channels=256,
fc_out_channels=1024,
roi_feat_size=7,
num_classes=1203,
bbox_coder=dict(
type='DeltaXYWHBBoxCoder',
target_means=[0., 0., 0., 0.],
target_stds=[0.1, 0.1, 0.2, 0.2]),
reg_class_agnostic=True,
cls_predictor_cfg=dict(type='NormedLinear', tempearture=20),
loss_cls=dict(
type='SeesawLoss',
p=0.8,
q=2.0,
num_classes=1203,
loss_weight=1.0),
loss_bbox=dict(type='SmoothL1Loss', beta=1.0,
loss_weight=1.0)),
dict(
type='Shared2FCBBoxHead',
in_channels=256,
fc_out_channels=1024,
roi_feat_size=7,
num_classes=1203,
bbox_coder=dict(
type='DeltaXYWHBBoxCoder',
target_means=[0., 0., 0., 0.],
target_stds=[0.05, 0.05, 0.1, 0.1]),
reg_class_agnostic=True,
cls_predictor_cfg=dict(type='NormedLinear', tempearture=20),
loss_cls=dict(
type='SeesawLoss',
p=0.8,
q=2.0,
num_classes=1203,
loss_weight=1.0),
loss_bbox=dict(type='SmoothL1Loss', beta=1.0,
loss_weight=1.0)),
dict(
type='Shared2FCBBoxHead',
in_channels=256,
fc_out_channels=1024,
roi_feat_size=7,
num_classes=1203,
bbox_coder=dict(
type='DeltaXYWHBBoxCoder',
target_means=[0., 0., 0., 0.],
target_stds=[0.033, 0.033, 0.067, 0.067]),
reg_class_agnostic=True,
cls_predictor_cfg=dict(type='NormedLinear', tempearture=20),
loss_cls=dict(
type='SeesawLoss',
p=0.8,
q=2.0,
num_classes=1203,
loss_weight=1.0),
loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0))
],
mask_head=dict(num_classes=1203)),
test_cfg=dict(
rcnn=dict(
score_thr=0.0001,
# LVIS allows up to 300
max_per_img=300)))
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
dict(
type='Resize',
img_scale=[(1333, 640), (1333, 672), (1333, 704), (1333, 736),
(1333, 768), (1333, 800)],
multiscale_mode='value',
keep_ratio=True),
dict(type='RandomFlip', flip_ratio=0.5),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']),
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(1333, 800),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='RandomFlip'),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img']),
])
]
dataset_type = 'LVISV1Dataset'
data_root = 'data/lvis_v1/'
data = dict(
samples_per_gpu=2,
workers_per_gpu=2,
train=dict(
type=dataset_type,
ann_file=data_root + 'annotations/lvis_v1_train.json',
img_prefix=data_root,
pipeline=train_pipeline),
val=dict(
type=dataset_type,
ann_file=data_root + 'annotations/lvis_v1_val.json',
img_prefix=data_root,
pipeline=test_pipeline),
test=dict(
type=dataset_type,
ann_file=data_root + 'annotations/lvis_v1_val.json',
img_prefix=data_root,
pipeline=test_pipeline))
evaluation = dict(interval=24, metric=['bbox', 'segm'])
| 4,807 | 35.150376 | 79 | py |
mmdetection | mmdetection-master/configs/seesaw_loss/cascade_mask_rcnn_r101_fpn_random_seesaw_loss_normed_mask_mstrain_2x_lvis_v1.py | _base_ = './cascade_mask_rcnn_r101_fpn_random_seesaw_loss_mstrain_2x_lvis_v1.py' # noqa: E501
model = dict(
roi_head=dict(
mask_head=dict(
predictor_cfg=dict(type='NormedConv2d', tempearture=20))))
| 223 | 36.333333 | 94 | py |
mmdetection | mmdetection-master/configs/seesaw_loss/cascade_mask_rcnn_r101_fpn_sample1e-3_seesaw_loss_mstrain_2x_lvis_v1.py | _base_ = [
'../_base_/models/cascade_mask_rcnn_r50_fpn.py',
'../_base_/datasets/lvis_v1_instance.py',
'../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py'
]
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')),
roi_head=dict(
bbox_head=[
dict(
type='Shared2FCBBoxHead',
in_channels=256,
fc_out_channels=1024,
roi_feat_size=7,
num_classes=1203,
bbox_coder=dict(
type='DeltaXYWHBBoxCoder',
target_means=[0., 0., 0., 0.],
target_stds=[0.1, 0.1, 0.2, 0.2]),
reg_class_agnostic=True,
cls_predictor_cfg=dict(type='NormedLinear', tempearture=20),
loss_cls=dict(
type='SeesawLoss',
p=0.8,
q=2.0,
num_classes=1203,
loss_weight=1.0),
loss_bbox=dict(type='SmoothL1Loss', beta=1.0,
loss_weight=1.0)),
dict(
type='Shared2FCBBoxHead',
in_channels=256,
fc_out_channels=1024,
roi_feat_size=7,
num_classes=1203,
bbox_coder=dict(
type='DeltaXYWHBBoxCoder',
target_means=[0., 0., 0., 0.],
target_stds=[0.05, 0.05, 0.1, 0.1]),
reg_class_agnostic=True,
cls_predictor_cfg=dict(type='NormedLinear', tempearture=20),
loss_cls=dict(
type='SeesawLoss',
p=0.8,
q=2.0,
num_classes=1203,
loss_weight=1.0),
loss_bbox=dict(type='SmoothL1Loss', beta=1.0,
loss_weight=1.0)),
dict(
type='Shared2FCBBoxHead',
in_channels=256,
fc_out_channels=1024,
roi_feat_size=7,
num_classes=1203,
bbox_coder=dict(
type='DeltaXYWHBBoxCoder',
target_means=[0., 0., 0., 0.],
target_stds=[0.033, 0.033, 0.067, 0.067]),
reg_class_agnostic=True,
cls_predictor_cfg=dict(type='NormedLinear', tempearture=20),
loss_cls=dict(
type='SeesawLoss',
p=0.8,
q=2.0,
num_classes=1203,
loss_weight=1.0),
loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0))
],
mask_head=dict(num_classes=1203)),
test_cfg=dict(
rcnn=dict(
score_thr=0.0001,
# LVIS allows up to 300
max_per_img=300)))
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
dict(
type='Resize',
img_scale=[(1333, 640), (1333, 672), (1333, 704), (1333, 736),
(1333, 768), (1333, 800)],
multiscale_mode='value',
keep_ratio=True),
dict(type='RandomFlip', flip_ratio=0.5),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']),
]
data = dict(train=dict(dataset=dict(pipeline=train_pipeline)))
evaluation = dict(interval=24, metric=['bbox', 'segm'])
| 3,783 | 37.222222 | 79 | py |
mmdetection | mmdetection-master/configs/seesaw_loss/cascade_mask_rcnn_r101_fpn_sample1e-3_seesaw_loss_normed_mask_mstrain_2x_lvis_v1.py | _base_ = './cascade_mask_rcnn_r101_fpn_sample1e-3_seesaw_loss_mstrain_2x_lvis_v1.py' # noqa: E501
model = dict(
roi_head=dict(
mask_head=dict(
predictor_cfg=dict(type='NormedConv2d', tempearture=20))))
| 227 | 37 | 98 | py |
mmdetection | mmdetection-master/configs/seesaw_loss/mask_rcnn_r101_fpn_random_seesaw_loss_mstrain_2x_lvis_v1.py | _base_ = './mask_rcnn_r50_fpn_random_seesaw_loss_mstrain_2x_lvis_v1.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 227 | 31.571429 | 71 | py |
mmdetection | mmdetection-master/configs/seesaw_loss/mask_rcnn_r101_fpn_random_seesaw_loss_normed_mask_mstrain_2x_lvis_v1.py | _base_ = './mask_rcnn_r50_fpn_random_seesaw_loss_normed_mask_mstrain_2x_lvis_v1.py' # noqa: E501
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 253 | 35.285714 | 97 | py |
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