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添加openmmlab测试用例

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openmmlab_test/mmdetection-speed_xinpian/configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_poly_1x_coco_v1.py 0 → 100644
View file @ 85529f35
_base_ = './mask_rcnn_r50_fpn_1x_coco.py'
model = dict(
pretrained='open-mmlab://resnet50_caffe_bgr',
backbone=dict(norm_cfg=dict(requires_grad=False), style='caffe'),
rpn_head=dict(
loss_bbox=dict(type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.0)),
roi_head=dict(
bbox_roi_extractor=dict(
roi_layer=dict(
type='RoIAlign',
output_size=7,
sampling_ratio=2,
aligned=False)),
bbox_head=dict(
loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0)),
mask_roi_extractor=dict(
roi_layer=dict(
type='RoIAlign',
output_size=14,
sampling_ratio=2,
aligned=False))))
# 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_mask=True,
poly2mask=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', '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))
openmmlab_test/mmdetection-speed_xinpian/configs/mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py 0 → 100644
View file @ 85529f35
_base_ = [
'../_base_/models/mask_rcnn_r50_fpn.py',
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
openmmlab_test/mmdetection-speed_xinpian/configs/mask_rcnn/mask_rcnn_r50_fpn_2x_coco.py 0 → 100644
View file @ 85529f35
_base_ = [
'../_base_/models/mask_rcnn_r50_fpn.py',
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py'
]
openmmlab_test/mmdetection-speed_xinpian/configs/mask_rcnn/mask_rcnn_r50_fpn_mstrain-poly_3x_coco.py 0 → 100644
View file @ 85529f35
_base_ = [
'../common/mstrain-poly_3x_coco_instance.py',
'../_base_/models/mask_rcnn_r50_fpn.py'
]
openmmlab_test/mmdetection-speed_xinpian/configs/mask_rcnn/mask_rcnn_r50_fpn_poly_1x_coco.py 0 → 100644
View file @ 85529f35
_base_ = [
'../_base_/models/mask_rcnn_r50_fpn.py',
'../_base_/datasets/coco_instance.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_mask=True,
poly2mask=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', 'gt_labels', 'gt_masks']),
]
data = dict(train=dict(pipeline=train_pipeline))
openmmlab_test/mmdetection-speed_xinpian/configs/mask_rcnn/mask_rcnn_x101_32x4d_fpn_1x_coco.py 0 → 100644
View file @ 85529f35
_base_ = './mask_rcnn_r101_fpn_1x_coco.py'
model = dict(
pretrained='open-mmlab://resnext101_32x4d',
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'))
openmmlab_test/mmdetection-speed_xinpian/configs/mask_rcnn/mask_rcnn_x101_32x4d_fpn_2x_coco.py 0 → 100644
View file @ 85529f35
_base_ = './mask_rcnn_r101_fpn_2x_coco.py'
model = dict(
pretrained='open-mmlab://resnext101_32x4d',
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'))
openmmlab_test/mmdetection-speed_xinpian/configs/mask_rcnn/mask_rcnn_x101_32x4d_fpn_mstrain-poly_3x_coco.py 0 → 100644
View file @ 85529f35
_base_ = [
'../common/mstrain-poly_3x_coco_instance.py',
'../_base_/models/mask_rcnn_r50_fpn.py'
]
model = dict(
pretrained='open-mmlab://resnext101_32x4d',
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'))
openmmlab_test/mmdetection-speed_xinpian/configs/mask_rcnn/mask_rcnn_x101_32x8d_fpn_1x_coco.py 0 → 100644
View file @ 85529f35
_base_ = './mask_rcnn_r101_fpn_1x_coco.py'
model = dict(
pretrained='open-mmlab://detectron2/resnext101_32x8d',
backbone=dict(
type='ResNeXt',
depth=101,
groups=32,
base_width=8,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=False),
style='pytorch'))
dataset_type = 'CocoDataset'
data_root = 'data/coco/'
img_norm_cfg = dict(
mean=[103.530, 116.280, 123.675],
std=[57.375, 57.120, 58.395],
to_rgb=False)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True, with_mask=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', '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(
samples_per_gpu=2,
workers_per_gpu=2,
train=dict(
type=dataset_type,
ann_file=data_root + 'annotations/instances_train2017.json',
img_prefix=data_root + 'train2017/',
pipeline=train_pipeline),
val=dict(
type=dataset_type,
ann_file=data_root + 'annotations/instances_val2017.json',
img_prefix=data_root + 'val2017/',
pipeline=test_pipeline),
test=dict(
type=dataset_type,
ann_file=data_root + 'annotations/instances_val2017.json',
img_prefix=data_root + 'val2017/',
pipeline=test_pipeline))
openmmlab_test/mmdetection-speed_xinpian/configs/mask_rcnn/mask_rcnn_x101_32x8d_fpn_mstrain-poly_1x_coco.py 0 → 100644
View file @ 85529f35
_base_ = './mask_rcnn_r101_fpn_1x_coco.py'
model = dict(
pretrained='open-mmlab://detectron2/resnext101_32x8d',
backbone=dict(
type='ResNeXt',
depth=101,
groups=32,
base_width=8,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=False),
style='pytorch'))
dataset_type = 'CocoDataset'
data_root = 'data/coco/'
img_norm_cfg = dict(
mean=[103.530, 116.280, 123.675],
std=[57.375, 57.120, 58.395],
to_rgb=False)
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))
openmmlab_test/mmdetection-speed_xinpian/configs/mask_rcnn/mask_rcnn_x101_32x8d_fpn_mstrain-poly_3x_coco.py 0 → 100644
View file @ 85529f35
_base_ = [
'../common/mstrain-poly_3x_coco_instance.py',
'../_base_/models/mask_rcnn_r50_fpn.py'
]
model = dict(
pretrained='open-mmlab://detectron2/resnext101_32x8d',
backbone=dict(
type='ResNeXt',
depth=101,
groups=32,
base_width=8,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=False),
style='pytorch'))
dataset_type = 'CocoDataset'
data_root = 'data/coco/'
img_norm_cfg = dict(
mean=[103.530, 116.280, 123.675],
std=[57.375, 57.120, 58.395],
to_rgb=False)
# In mstrain 3x config, img_scale=[(1333, 640), (1333, 800)],
# multiscale_mode='range'
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='LoadAnnotations',
with_bbox=True,
with_mask=True,
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', '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']),
])
]
# Use RepeatDataset to speed up training
data = dict(
samples_per_gpu=2,
workers_per_gpu=2,
train=dict(
type='RepeatDataset',
times=3,
dataset=dict(
type=dataset_type,
ann_file=data_root + 'annotations/instances_train2017.json',
img_prefix=data_root + 'train2017/',
pipeline=train_pipeline)),
val=dict(
type=dataset_type,
ann_file=data_root + 'annotations/instances_val2017.json',
img_prefix=data_root + 'val2017/',
pipeline=test_pipeline),
test=dict(
type=dataset_type,
ann_file=data_root + 'annotations/instances_val2017.json',
img_prefix=data_root + 'val2017/',
pipeline=test_pipeline))
openmmlab_test/mmdetection-speed_xinpian/configs/mask_rcnn/mask_rcnn_x101_64x4d_fpn_1x_coco.py 0 → 100644
View file @ 85529f35
_base_ = './mask_rcnn_x101_32x4d_fpn_1x_coco.py'
model = dict(
pretrained='open-mmlab://resnext101_64x4d',
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'))
openmmlab_test/mmdetection-speed_xinpian/configs/mask_rcnn/mask_rcnn_x101_64x4d_fpn_2x_coco.py 0 → 100644
View file @ 85529f35
_base_ = './mask_rcnn_x101_32x4d_fpn_2x_coco.py'
model = dict(
pretrained='open-mmlab://resnext101_64x4d',
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'))
openmmlab_test/mmdetection-speed_xinpian/configs/mask_rcnn/mask_rcnn_x101_64x4d_fpn_mstrain-poly_3x_coco.py 0 → 100644
View file @ 85529f35
_base_ = [
'../common/mstrain-poly_3x_coco_instance.py',
'../_base_/models/mask_rcnn_r50_fpn.py'
]
model = dict(
pretrained='open-mmlab://resnext101_64x4d',
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'))
openmmlab_test/mmdetection-speed_xinpian/configs/mask_rcnn/metafile.yml 0 → 100644
View file @ 85529f35
Collections:
- Name: Mask R-CNN
Metadata:
Training Data: COCO
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Resources: 8x NVIDIA V100 GPUs
Architecture:
- Softmax
- RPN
- Convolution
- Dense Connections
- FPN
- ResNet
- RoIAlign
Paper: https://arxiv.org/abs/1703.06870v3
README: configs/mask_rcnn/README.md
Models:
- Name: mask_rcnn_r50_caffe_fpn_1x_coco
In Collection: Mask R-CNN
Config: configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_1x_coco.py
Metadata:
Training Memory (GB): 4.3
Epochs: 12
Results:
- Task: Object Detection
Dataset: COCO
Metrics:
box AP: 38.0
- Task: Instance Segmentation
Dataset: COCO
Metrics:
mask AP: 34.4
Weights: https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_caffe_fpn_1x_coco/mask_rcnn_r50_caffe_fpn_1x_coco_bbox_mAP-0.38__segm_mAP-0.344_20200504_231812-0ebd1859.pth
- Name: mask_rcnn_r50_fpn_1x_coco
In Collection: Mask R-CNN
Config: configs/mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py
Metadata:
Training Memory (GB): 4.4
inference time (s/im): 0.06211
Epochs: 12
Results:
- Task: Object Detection
Dataset: COCO
Metrics:
box AP: 38.2
- Task: Instance Segmentation
Dataset: COCO
Metrics:
mask AP: 34.7
Weights: https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_fpn_1x_coco/mask_rcnn_r50_fpn_1x_coco_20200205-d4b0c5d6.pth
- Name: mask_rcnn_r50_fpn_2x_coco
In Collection: Mask R-CNN
Config: configs/mask_rcnn/mask_rcnn_r50_fpn_2x_coco.py
Metadata:
Training Memory (GB): 4.4
inference time (s/im): 0.06211
Epochs: 24
Results:
- Task: Object Detection
Dataset: COCO
Metrics:
box AP: 39.2
- Task: Instance Segmentation
Dataset: COCO
Metrics:
mask AP: 35.4
Weights: https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_fpn_2x_coco/mask_rcnn_r50_fpn_2x_coco_bbox_mAP-0.392__segm_mAP-0.354_20200505_003907-3e542a40.pth
- Name: mask_rcnn_r101_caffe_fpn_1x_coco
In Collection: Mask R-CNN
Config: configs/mask_rcnn/mask_rcnn_r101_caffe_fpn_1x_coco.py
Metadata:
Epochs: 12
Results:
- Task: Object Detection
Dataset: COCO
Metrics:
box AP: 40.4
- Task: Instance Segmentation
Dataset: COCO
Metrics:
mask AP: 36.4
Weights: https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r101_caffe_fpn_1x_coco/mask_rcnn_r101_caffe_fpn_1x_coco_20200601_095758-805e06c1.pth
- Name: mask_rcnn_r101_fpn_1x_coco
In Collection: Mask R-CNN
Config: configs/mask_rcnn/mask_rcnn_r101_fpn_1x_coco.py
Metadata:
Training Memory (GB): 6.4
inference time (s/im): 0.07407
Epochs: 12
Results:
- Task: Object Detection
Dataset: COCO
Metrics:
box AP: 40.0
- Task: Instance Segmentation
Dataset: COCO
Metrics:
mask AP: 36.1
Weights: https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r101_fpn_1x_coco/mask_rcnn_r101_fpn_1x_coco_20200204-1efe0ed5.pth
- Name: mask_rcnn_r101_fpn_2x_coco
In Collection: Mask R-CNN
Config: configs/mask_rcnn/mask_rcnn_r101_fpn_2x_coco.py
Metadata:
Training Memory (GB): 6.4
inference time (s/im): 0.07407
Epochs: 24
Results:
- Task: Object Detection
Dataset: COCO
Metrics:
box AP: 40.8
- Task: Instance Segmentation
Dataset: COCO
Metrics:
mask AP: 36.6
Weights: https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r101_fpn_2x_coco/mask_rcnn_r101_fpn_2x_coco_bbox_mAP-0.408__segm_mAP-0.366_20200505_071027-14b391c7.pth
- Name: mask_rcnn_x101_32x4d_fpn_1x_coco
In Collection: Mask R-CNN
Config: configs/mask_rcnn/mask_rcnn_x101_32x4d_fpn_1x_coco.py
Metadata:
Training Memory (GB): 7.6
inference time (s/im): 0.0885
Epochs: 12
Results:
- Task: Object Detection
Dataset: COCO
Metrics:
box AP: 41.9
- Task: Instance Segmentation
Dataset: COCO
Metrics:
mask AP: 37.5
Weights: https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_x101_32x4d_fpn_1x_coco/mask_rcnn_x101_32x4d_fpn_1x_coco_20200205-478d0b67.pth
- Name: mask_rcnn_x101_32x4d_fpn_2x_coco
In Collection: Mask R-CNN
Config: configs/mask_rcnn/mask_rcnn_x101_32x4d_fpn_2x_coco.py
Metadata:
Training Memory (GB): 7.6
inference time (s/im): 0.0885
Epochs: 24
Results:
- Task: Object Detection
Dataset: COCO
Metrics:
box AP: 42.2
- Task: Instance Segmentation
Dataset: COCO
Metrics:
mask AP: 37.8
Weights: https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_x101_32x4d_fpn_2x_coco/mask_rcnn_x101_32x4d_fpn_2x_coco_bbox_mAP-0.422__segm_mAP-0.378_20200506_004702-faef898c.pth
- Name: mask_rcnn_x101_64x4d_fpn_1x_coco
In Collection: Mask R-CNN
Config: configs/mask_rcnn/mask_rcnn_x101_64x4d_fpn_1x_coco.py
Metadata:
Training Memory (GB): 10.7
inference time (s/im): 0.125
Epochs: 12
Results:
- Task: Object Detection
Dataset: COCO
Metrics:
box AP: 42.8
- Task: Instance Segmentation
Dataset: COCO
Metrics:
mask AP: 38.4
Weights: https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_x101_64x4d_fpn_1x_coco/mask_rcnn_x101_64x4d_fpn_1x_coco_20200201-9352eb0d.pth
- Name: mask_rcnn_x101_64x4d_fpn_2x_coco
In Collection: Mask R-CNN
Config: configs/mask_rcnn/mask_rcnn_x101_64x4d_fpn_2x_coco.py
Metadata:
Training Memory (GB): 10.7
inference time (s/im): 0.125
Epochs: 24
Results:
- Task: Object Detection
Dataset: COCO
Metrics:
box AP: 42.7
- Task: Instance Segmentation
Dataset: COCO
Metrics:
mask AP: 38.1
Weights: https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_x101_64x4d_fpn_2x_coco/mask_rcnn_x101_64x4d_fpn_2x_coco_20200509_224208-39d6f70c.pth
- Name: mask_rcnn_x101_32x8d_fpn_1x_coco
In Collection: Mask R-CNN
Config: configs/mask_rcnn/mask_rcnn_x101_32x8d_fpn_1x_coco.py
Metadata:
Training Memory (GB): 10.7
inference time (s/im): 0.125
Epochs: 12
Results:
- Task: Object Detection
Dataset: COCO
Metrics:
box AP: 42.8
- Task: Instance Segmentation
Dataset: COCO
Metrics:
mask AP: 38.3
- Name: mask_rcnn_r50_caffe_fpn_mstrain-poly_2x_coco
In Collection: Mask R-CNN
Config: configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_2x_coco.py
Metadata:
Training Memory (GB): 4.3
Epochs: 24
Results:
- Task: Object Detection
Dataset: COCO
Metrics:
box AP: 40.3
- Task: Instance Segmentation
Dataset: COCO
Metrics:
mask AP: 36.5
Weights: https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_2x_coco/mask_rcnn_r50_caffe_fpn_mstrain-poly_2x_coco_bbox_mAP-0.403__segm_mAP-0.365_20200504_231822-a75c98ce.pth
- Name: mask_rcnn_r50_caffe_fpn_mstrain-poly_3x_coco
In Collection: Mask R-CNN
Config: configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_3x_coco.py
Metadata:
Training Memory (GB): 4.3
Epochs: 36
Results:
- Task: Object Detection
Dataset: COCO
Metrics:
box AP: 40.8
- Task: Instance Segmentation
Dataset: COCO
Metrics:
mask AP: 37.0
Weights: https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_3x_coco/mask_rcnn_r50_caffe_fpn_mstrain-poly_3x_coco_bbox_mAP-0.408__segm_mAP-0.37_20200504_163245-42aa3d00.pth
- Name: mask_rcnn_r50_fpn_mstrain-poly_3x_coco
In Collection: Mask R-CNN
Config: configs/mask_rcnn/mask_rcnn_r50_fpn_mstrain-poly_3x_coco.py
Metadata:
Training Memory (GB): 4.1
Epochs: 36
Results:
- Task: Object Detection
Dataset: COCO
Metrics:
box AP: 40.9
- Task: Instance Segmentation
Dataset: COCO
Metrics:
mask AP: 37.1
Weights: https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_fpn_mstrain-poly_3x_coco/mask_rcnn_r50_fpn_mstrain-poly_3x_coco_20210524_201154-21b550bb.pth
- Name: mask_rcnn_r101_fpn_mstrain-poly_3x_coco
In Collection: Mask R-CNN
Config: configs/mask_rcnn/mask_rcnn_r101_fpn_mstrain-poly_3x_coco.py
Metadata:
Training Memory (GB): 6.1
Epochs: 36
Results:
- Task: Object Detection
Dataset: COCO
Metrics:
box AP: 42.7
- Task: Instance Segmentation
Dataset: COCO
Metrics:
mask AP: 38.5
Weights: https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r101_fpn_mstrain-poly_3x_coco/mask_rcnn_r101_fpn_mstrain-poly_3x_coco_20210524_200244-5675c317.pth
- Name: mask_rcnn_r101_caffe_fpn_mstrain-poly_3x_coco
In Collection: Mask R-CNN
Config: configs/mask_rcnn/mask_rcnn_r101_caffe_fpn_mstrain-poly_3x_coco.py
Metadata:
Training Memory (GB): 5.9
Epochs: 36
Results:
- Task: Object Detection
Dataset: COCO
Metrics:
box AP: 42.9
- Task: Instance Segmentation
Dataset: COCO
Metrics:
mask AP: 38.5
Weights: https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r101_caffe_fpn_mstrain-poly_3x_coco/mask_rcnn_r101_caffe_fpn_mstrain-poly_3x_coco_20210526_132339-3c33ce02.pth
- Name: mask_rcnn_x101_32x4d_fpn_mstrain-poly_3x_coco
In Collection: Mask R-CNN
Config: configs/mask_rcnn/mask_rcnn_x101_32x4d_fpn_mstrain-poly_3x_coco.py
Metadata:
Training Memory (GB): 7.3
Epochs: 36
Results:
- Task: Object Detection
Dataset: COCO
Metrics:
box AP: 43.6
- Task: Instance Segmentation
Dataset: COCO
Metrics:
mask AP: 39.0
Weights: https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_x101_32x4d_fpn_mstrain-poly_3x_coco/mask_rcnn_x101_32x4d_fpn_mstrain-poly_3x_coco_20210524_201410-abcd7859.pth
- Name: mask_rcnn_x101_32x8d_fpn_mstrain-poly_1x_coco
In Collection: Mask R-CNN
Config: configs/mask_rcnn/mask_rcnn_x101_32x8d_fpn_mstrain-poly_1x_coco.py
Metadata:
Epochs: 12
Results:
- Task: Object Detection
Dataset: COCO
Metrics:
box AP: 43.6
- Task: Instance Segmentation
Dataset: COCO
Metrics:
mask AP: 39.0
- Name: mask_rcnn_x101_32x8d_fpn_mstrain-poly_3x_coco
In Collection: Mask R-CNN
Config: configs/mask_rcnn/mask_rcnn_x101_32x8d_fpn_mstrain-poly_3x_coco
Metadata:
Training Memory (GB): 10.3
Epochs: 36
Results:
- Task: Object Detection
Dataset: COCO
Metrics:
box AP: 44.3
Weights: https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_x101_32x8d_fpn_mstrain-poly_3x_coco/mask_rcnn_x101_32x8d_fpn_mstrain-poly_3x_coco_20210607_161042-8bd2c639.pth
- Name: mask_rcnn_x101_64x4d_fpn_mstrain-poly_3x_coco
In Collection: Mask R-CNN
Config: configs/mask_rcnn/mask_rcnn_x101_64x4d_fpn_mstrain-poly_3x_coco.py
Metadata:
Epochs: 36
Training Memory (GB): 10.4
Results:
- Task: Object Detection
Dataset: COCO
Metrics:
box AP: 44.5
- Task: Instance Segmentation
Dataset: COCO
Metrics:
mask AP: 39.7
Weights: https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_x101_64x4d_fpn_mstrain-poly_3x_coco/mask_rcnn_x101_64x4d_fpn_mstrain-poly_3x_coco_20210526_120447-c376f129.pth
openmmlab_test/mmdetection-speed_xinpian/configs/ms_rcnn/README.md 0 → 100644
View file @ 85529f35
# Mask Scoring R-CNN
## Introduction
<!-- [ALGORITHM] -->
```
@inproceedings{huang2019msrcnn,
title={Mask Scoring R-CNN},
author={Zhaojin Huang and Lichao Huang and Yongchao Gong and Chang Huang and Xinggang Wang},
booktitle={IEEE Conference on Computer Vision and Pattern Recognition},
year={2019},
}
```
## Results and Models
| Backbone | style | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download |
|:-------------:|:----------:|:-------:|:--------:|:--------------:|:------:|:-------:|:------:|:--------:|
| R-50-FPN | caffe | 1x | 4.5 | | 38.2 | 36.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/ms_rcnn/ms_rcnn_r50_caffe_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/ms_rcnn/ms_rcnn_r50_caffe_fpn_1x_coco/ms_rcnn_r50_caffe_fpn_1x_coco_20200702_180848-61c9355e.pth) &#124; [log](https://download.openmmlab.com/mmdetection/v2.0/ms_rcnn/ms_rcnn_r50_caffe_fpn_1x_coco/ms_rcnn_r50_caffe_fpn_1x_coco_20200702_180848.log.json) |
| R-50-FPN | caffe | 2x | - | - | 38.8 | 36.3 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/ms_rcnn/ms_rcnn_r50_caffe_fpn_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/ms_rcnn/ms_rcnn_r50_caffe_fpn_2x_coco/ms_rcnn_r50_caffe_fpn_2x_coco_bbox_mAP-0.388__segm_mAP-0.363_20200506_004738-ee87b137.pth) &#124; [log](https://download.openmmlab.com/mmdetection/v2.0/ms_rcnn/ms_rcnn_r50_caffe_fpn_2x_coco/ms_rcnn_r50_caffe_fpn_2x_coco_20200506_004738.log.json) |
| R-101-FPN | caffe | 1x | 6.5 | | 40.4 | 37.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/ms_rcnn/ms_rcnn_r101_caffe_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/ms_rcnn/ms_rcnn_r101_caffe_fpn_1x_coco/ms_rcnn_r101_caffe_fpn_1x_coco_bbox_mAP-0.404__segm_mAP-0.376_20200506_004755-b9b12a37.pth) &#124; [log](https://download.openmmlab.com/mmdetection/v2.0/ms_rcnn/ms_rcnn_r101_caffe_fpn_1x_coco/ms_rcnn_r101_caffe_fpn_1x_coco_20200506_004755.log.json) |
| R-101-FPN | caffe | 2x | - | - | 41.1 | 38.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/ms_rcnn/ms_rcnn_r101_caffe_fpn_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/ms_rcnn/ms_rcnn_r101_caffe_fpn_2x_coco/ms_rcnn_r101_caffe_fpn_2x_coco_bbox_mAP-0.411__segm_mAP-0.381_20200506_011134-5f3cc74f.pth) &#124; [log](https://download.openmmlab.com/mmdetection/v2.0/ms_rcnn/ms_rcnn_r101_caffe_fpn_2x_coco/ms_rcnn_r101_caffe_fpn_2x_coco_20200506_011134.log.json) |
| R-X101-32x4d | pytorch | 2x | 7.9 | 11.0 | 41.8 | 38.7 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/ms_rcnn/ms_rcnn_x101_32x4d_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/ms_rcnn/ms_rcnn_x101_32x4d_fpn_1x_coco/ms_rcnn_x101_32x4d_fpn_1x_coco_20200206-81fd1740.pth) &#124; [log](https://download.openmmlab.com/mmdetection/v2.0/ms_rcnn/ms_rcnn_x101_32x4d_fpn_1x_coco/ms_rcnn_x101_32x4d_fpn_1x_coco_20200206_100113.log.json) |
| R-X101-64x4d | pytorch | 1x | 11.0 | 8.0 | 43.0 | 39.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/ms_rcnn/ms_rcnn_x101_64x4d_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/ms_rcnn/ms_rcnn_x101_64x4d_fpn_1x_coco/ms_rcnn_x101_64x4d_fpn_1x_coco_20200206-86ba88d2.pth) &#124; [log](https://download.openmmlab.com/mmdetection/v2.0/ms_rcnn/ms_rcnn_x101_64x4d_fpn_1x_coco/ms_rcnn_x101_64x4d_fpn_1x_coco_20200206_091744.log.json) |
| R-X101-64x4d | pytorch | 2x | 11.0 | 8.0 | 42.6 | 39.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/ms_rcnn/ms_rcnn_x101_64x4d_fpn_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/ms_rcnn/ms_rcnn_x101_64x4d_fpn_2x_coco/ms_rcnn_x101_64x4d_fpn_2x_coco_20200308-02a445e2.pth) &#124; [log](https://download.openmmlab.com/mmdetection/v2.0/ms_rcnn/ms_rcnn_x101_64x4d_fpn_2x_coco/ms_rcnn_x101_64x4d_fpn_2x_coco_20200308_012247.log.json) |
openmmlab_test/mmdetection-speed_xinpian/configs/ms_rcnn/metafile.yml 0 → 100644
View file @ 85529f35
Collections:
- Name: Mask Scoring R-CNN
Metadata:
Training Data: COCO
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Resources: 8x NVIDIA V100 GPUs
Architecture:
- RPN
- FPN
- ResNet
- RoIAlign
Paper: https://arxiv.org/abs/1903.00241
README: configs/ms_rcnn/README.md
Models:
- Name: ms_rcnn_r50_caffe_fpn_1x_coco
In Collection: Mask Scoring R-CNN
Config: configs/ms_rcnn/ms_rcnn_r50_caffe_fpn_1x_coco.py
Metadata:
Training Memory (GB): 4.5
Epochs: 12
Results:
- Task: Object Detection
Dataset: COCO
Metrics:
box AP: 38.2
- Task: Instance Segmentation
Dataset: COCO
Metrics:
mask AP: 36.0
Weights: https://download.openmmlab.com/mmdetection/v2.0/ms_rcnn/ms_rcnn_r50_caffe_fpn_1x_coco/ms_rcnn_r50_caffe_fpn_1x_coco_20200702_180848-61c9355e.pth
- Name: ms_rcnn_r50_caffe_fpn_2x_coco
In Collection: Mask Scoring R-CNN
Config: configs/ms_rcnn/ms_rcnn_r50_caffe_fpn_2x_coco.py
Metadata:
Epochs: 24
Results:
- Task: Object Detection
Dataset: COCO
Metrics:
box AP: 38.8
- Task: Instance Segmentation
Dataset: COCO
Metrics:
mask AP: 36.3
Weights: https://download.openmmlab.com/mmdetection/v2.0/ms_rcnn/ms_rcnn_r50_caffe_fpn_2x_coco/ms_rcnn_r50_caffe_fpn_2x_coco_bbox_mAP-0.388__segm_mAP-0.363_20200506_004738-ee87b137.pth
- Name: ms_rcnn_r101_caffe_fpn_1x_coco
In Collection: Mask Scoring R-CNN
Config: configs/ms_rcnn/ms_rcnn_r101_caffe_fpn_1x_coco.py
Metadata:
Training Memory (GB): 6.5
Epochs: 12
Results:
- Task: Object Detection
Dataset: COCO
Metrics:
box AP: 40.4
- Task: Instance Segmentation
Dataset: COCO
Metrics:
mask AP: 37.6
Weights: https://download.openmmlab.com/mmdetection/v2.0/ms_rcnn/ms_rcnn_r101_caffe_fpn_1x_coco/ms_rcnn_r101_caffe_fpn_1x_coco_bbox_mAP-0.404__segm_mAP-0.376_20200506_004755-b9b12a37.pth
- Name: ms_rcnn_r101_caffe_fpn_2x_coco
In Collection: Mask Scoring R-CNN
Config: configs/ms_rcnn/ms_rcnn_r101_caffe_fpn_2x_coco.py
Metadata:
Epochs: 24
Results:
- Task: Object Detection
Dataset: COCO
Metrics:
box AP: 41.1
- Task: Instance Segmentation
Dataset: COCO
Metrics:
mask AP: 38.1
Weights: https://download.openmmlab.com/mmdetection/v2.0/ms_rcnn/ms_rcnn_r101_caffe_fpn_2x_coco/ms_rcnn_r101_caffe_fpn_2x_coco_bbox_mAP-0.411__segm_mAP-0.381_20200506_011134-5f3cc74f.pth
- Name: ms_rcnn_x101_32x4d_fpn_1x_coco
In Collection: Mask Scoring R-CNN
Config: configs/ms_rcnn/ms_rcnn_x101_32x4d_fpn_1x_coco.py
Metadata:
Training Memory (GB): 7.9
inference time (s/im): 0.09091
Epochs: 12
Results:
- Task: Object Detection
Dataset: COCO
Metrics:
box AP: 41.8
- Task: Instance Segmentation
Dataset: COCO
Metrics:
mask AP: 38.7
Weights: https://download.openmmlab.com/mmdetection/v2.0/ms_rcnn/ms_rcnn_x101_32x4d_fpn_1x_coco/ms_rcnn_x101_32x4d_fpn_1x_coco_20200206-81fd1740.pth
- Name: ms_rcnn_x101_64x4d_fpn_1x_coco
In Collection: Mask Scoring R-CNN
Config: configs/ms_rcnn/ms_rcnn_x101_64x4d_fpn_1x_coco.py
Metadata:
Training Memory (GB): 11.0
inference time (s/im): 0.125
Epochs: 12
Results:
- Task: Object Detection
Dataset: COCO
Metrics:
box AP: 43.0
- Task: Instance Segmentation
Dataset: COCO
Metrics:
mask AP: 39.5
Weights: https://download.openmmlab.com/mmdetection/v2.0/ms_rcnn/ms_rcnn_x101_64x4d_fpn_1x_coco/ms_rcnn_x101_64x4d_fpn_1x_coco_20200206-86ba88d2.pth
- Name: ms_rcnn_x101_64x4d_fpn_2x_coco
In Collection: Mask Scoring R-CNN
Config: configs/ms_rcnn/ms_rcnn_x101_64x4d_fpn_2x_coco.py
Metadata:
Training Memory (GB): 11.0
inference time (s/im): 0.125
Epochs: 24
Results:
- Task: Object Detection
Dataset: COCO
Metrics:
box AP: 42.6
- Task: Instance Segmentation
Dataset: COCO
Metrics:
mask AP: 39.5
Weights: https://download.openmmlab.com/mmdetection/v2.0/ms_rcnn/ms_rcnn_x101_64x4d_fpn_2x_coco/ms_rcnn_x101_64x4d_fpn_2x_coco_20200308-02a445e2.pth
openmmlab_test/mmdetection-speed_xinpian/configs/ms_rcnn/ms_rcnn_r101_caffe_fpn_1x_coco.py 0 → 100644
View file @ 85529f35
_base_ = './ms_rcnn_r50_caffe_fpn_1x_coco.py'
model = dict(
pretrained='open-mmlab://detectron2/resnet101_caffe',
backbone=dict(depth=101))
openmmlab_test/mmdetection-speed_xinpian/configs/ms_rcnn/ms_rcnn_r101_caffe_fpn_2x_coco.py 0 → 100644
View file @ 85529f35
_base_ = './ms_rcnn_r101_caffe_fpn_1x_coco.py'
# learning policy
lr_config = dict(step=[16, 22])
runner = dict(type='EpochBasedRunner', max_epochs=24)
openmmlab_test/mmdetection-speed_xinpian/configs/ms_rcnn/ms_rcnn_r50_caffe_fpn_1x_coco.py 0 → 100644
View file @ 85529f35
_base_ = '../mask_rcnn/mask_rcnn_r50_caffe_fpn_1x_coco.py'
model = dict(
type='MaskScoringRCNN',
roi_head=dict(
type='MaskScoringRoIHead',
mask_iou_head=dict(
type='MaskIoUHead',
num_convs=4,
num_fcs=2,
roi_feat_size=14,
in_channels=256,
conv_out_channels=256,
fc_out_channels=1024,
num_classes=80)),
# model training and testing settings
train_cfg=dict(rcnn=dict(mask_thr_binary=0.5)))
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