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Commit eb1107e4 authored by raojy's avatar raojy
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fix_mmdetection

parent 7aa442d5
Pipeline #3461 canceled with stages
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mmde/mmdet/.mim/configs/cascade_rcnn/cascade-rcnn_r101_fpn_1x_coco.py 0 → 100644
View file @ eb1107e4
_base_ = './cascade-rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
mmde/mmdet/.mim/configs/cascade_rcnn/cascade-rcnn_r101_fpn_20e_coco.py 0 → 100644
View file @ eb1107e4
_base_ = './cascade-rcnn_r50_fpn_20e_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
mmde/mmdet/.mim/configs/cascade_rcnn/cascade-rcnn_r101_fpn_8xb8-amp-lsj-200e_coco.py 0 → 100644
View file @ eb1107e4
_base_ = './cascade-rcnn_r50_fpn_8xb8-amp-lsj-200e_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
mmde/mmdet/.mim/configs/cascade_rcnn/cascade-rcnn_r18_fpn_8xb8-amp-lsj-200e_coco.py 0 → 100644
View file @ eb1107e4
_base_ = './cascade-rcnn_r50_fpn_8xb8-amp-lsj-200e_coco.py'
model = dict(
backbone=dict(
depth=18,
init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet18')),
neck=dict(in_channels=[64, 128, 256, 512]))
mmde/mmdet/.mim/configs/cascade_rcnn/cascade-rcnn_r50-caffe_fpn_1x_coco.py 0 → 100644
View file @ eb1107e4
_base_ = './cascade-rcnn_r50_fpn_1x_coco.py'
model = dict(
# use caffe img_norm
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[103.530, 116.280, 123.675],
std=[1.0, 1.0, 1.0],
bgr_to_rgb=False,
pad_size_divisor=32),
backbone=dict(
norm_cfg=dict(requires_grad=False),
style='caffe',
init_cfg=dict(
type='Pretrained',
checkpoint='open-mmlab://detectron2/resnet50_caffe')))
mmde/mmdet/.mim/configs/cascade_rcnn/cascade-rcnn_r50_fpn_1x_coco.py 0 → 100644
View file @ eb1107e4
_base_ = [
'../_base_/models/cascade-rcnn_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
mmde/mmdet/.mim/configs/cascade_rcnn/cascade-rcnn_r50_fpn_20e_coco.py 0 → 100644
View file @ eb1107e4
_base_ = [
'../_base_/models/cascade-rcnn_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_20e.py', '../_base_/default_runtime.py'
]
mmde/mmdet/.mim/configs/cascade_rcnn/cascade-rcnn_r50_fpn_8xb8-amp-lsj-200e_coco.py 0 → 100644
View file @ eb1107e4
_base_ = [
'../_base_/models/cascade-rcnn_r50_fpn.py',
'../common/lsj-200e_coco-detection.py'
]
image_size = (1024, 1024)
batch_augments = [dict(type='BatchFixedSizePad', size=image_size)]
# disable allowed_border to avoid potential errors.
model = dict(
data_preprocessor=dict(batch_augments=batch_augments),
train_cfg=dict(rpn=dict(allowed_border=-1)))
train_dataloader = dict(batch_size=8, num_workers=4)
# Enable automatic-mixed-precision training with AmpOptimWrapper.
optim_wrapper = dict(
type='AmpOptimWrapper',
optimizer=dict(
type='SGD', lr=0.02 * 4, momentum=0.9, weight_decay=0.00004))
# NOTE: `auto_scale_lr` is for automatically scaling LR,
# USER SHOULD NOT CHANGE ITS VALUES.
# base_batch_size = (8 GPUs) x (8 samples per GPU)
auto_scale_lr = dict(base_batch_size=64)
mmde/mmdet/.mim/configs/cascade_rcnn/cascade-rcnn_x101-32x4d_fpn_1x_coco.py 0 → 100644
View file @ eb1107e4
_base_ = './cascade-rcnn_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')))
mmde/mmdet/.mim/configs/cascade_rcnn/cascade-rcnn_x101-32x4d_fpn_20e_coco.py 0 → 100644
View file @ eb1107e4
_base_ = './cascade-rcnn_r50_fpn_20e_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')))
mmde/mmdet/.mim/configs/cascade_rcnn/cascade-rcnn_x101-64x4d_fpn_1x_coco.py 0 → 100644
View file @ eb1107e4
_base_ = './cascade-rcnn_r50_fpn_1x_coco.py'
model = dict(
type='CascadeRCNN',
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')))
mmde/mmdet/.mim/configs/cascade_rcnn/cascade-rcnn_x101_64x4d_fpn_20e_coco.py 0 → 100644
View file @ eb1107e4
_base_ = './cascade-rcnn_r50_fpn_20e_coco.py'
model = dict(
type='CascadeRCNN',
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')))
mmde/mmdet/.mim/configs/cascade_rcnn/metafile.yml 0 → 100644
View file @ eb1107e4
Collections:
- Name: Cascade R-CNN
Metadata:
Training Data: COCO
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Resources: 8x V100 GPUs
Architecture:
- Cascade R-CNN
- FPN
- RPN
- ResNet
- RoIAlign
Paper:
URL: http://dx.doi.org/10.1109/tpami.2019.2956516
Title: 'Cascade R-CNN: Delving into High Quality Object Detection'
README: configs/cascade_rcnn/README.md
Code:
URL: https://github.com/open-mmlab/mmdetection/blob/v2.0.0/mmdet/models/detectors/cascade_rcnn.py#L6
Version: v2.0.0
- Name: Cascade Mask R-CNN
Metadata:
Training Data: COCO
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Resources: 8x V100 GPUs
Architecture:
- Cascade R-CNN
- FPN
- RPN
- ResNet
- RoIAlign
Paper:
URL: http://dx.doi.org/10.1109/tpami.2019.2956516
Title: 'Cascade R-CNN: Delving into High Quality Object Detection'
README: configs/cascade_rcnn/README.md
Code:
URL: https://github.com/open-mmlab/mmdetection/blob/v2.0.0/mmdet/models/detectors/cascade_rcnn.py#L6
Version: v2.0.0
Models:
- Name: cascade-rcnn_r50-caffe_fpn_1x_coco
In Collection: Cascade R-CNN
Config: configs/cascade_rcnn/cascade-rcnn_r50-caffe_fpn_1x_coco.py
Metadata:
Training Memory (GB): 4.2
Epochs: 12
Results:
- Task: Object Detection
Dataset: COCO
Metrics:
box AP: 40.4
Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_r50_caffe_fpn_1x_coco/cascade_rcnn_r50_caffe_fpn_1x_coco_bbox_mAP-0.404_20200504_174853-b857be87.pth
- Name: cascade-rcnn_r50_fpn_1x_coco
In Collection: Cascade R-CNN
Config: configs/cascade_rcnn/cascade-rcnn_r50_fpn_1x_coco.py
Metadata:
Training Memory (GB): 4.4
inference time (ms/im):
- value: 62.11
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (800, 1333)
Epochs: 12
Results:
- Task: Object Detection
Dataset: COCO
Metrics:
box AP: 40.3
Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_r50_fpn_1x_coco/cascade_rcnn_r50_fpn_1x_coco_20200316-3dc56deb.pth
- Name: cascade-rcnn_r50_fpn_20e_coco
In Collection: Cascade R-CNN
Config: configs/cascade_rcnn/cascade-rcnn_r50_fpn_20e_coco.py
Metadata:
Training Memory (GB): 4.4
inference time (ms/im):
- value: 62.11
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (800, 1333)
Epochs: 20
Results:
- Task: Object Detection
Dataset: COCO
Metrics:
box AP: 41.0
Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_r50_fpn_20e_coco/cascade_rcnn_r50_fpn_20e_coco_bbox_mAP-0.41_20200504_175131-e9872a90.pth
- Name: cascade-rcnn_r101-caffe_fpn_1x_coco
In Collection: Cascade R-CNN
Config: configs/cascade_rcnn/cascade-rcnn_r101-caffe_fpn_1x_coco.py
Metadata:
Training Memory (GB): 6.2
Epochs: 12
Results:
- Task: Object Detection
Dataset: COCO
Metrics:
box AP: 42.3
Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_r101_caffe_fpn_1x_coco/cascade_rcnn_r101_caffe_fpn_1x_coco_bbox_mAP-0.423_20200504_175649-cab8dbd5.pth
- Name: cascade-rcnn_r101_fpn_1x_coco
In Collection: Cascade R-CNN
Config: configs/cascade_rcnn/cascade-rcnn_r101_fpn_1x_coco.py
Metadata:
Training Memory (GB): 6.4
inference time (ms/im):
- value: 74.07
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (800, 1333)
Epochs: 12
Results:
- Task: Object Detection
Dataset: COCO
Metrics:
box AP: 42.0
Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_r101_fpn_1x_coco/cascade_rcnn_r101_fpn_1x_coco_20200317-0b6a2fbf.pth
- Name: cascade-rcnn_r101_fpn_20e_coco
In Collection: Cascade R-CNN
Config: configs/cascade_rcnn/cascade-rcnn_r101_fpn_20e_coco.py
Metadata:
Training Memory (GB): 6.4
inference time (ms/im):
- value: 74.07
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (800, 1333)
Epochs: 20
Results:
- Task: Object Detection
Dataset: COCO
Metrics:
box AP: 42.5
Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_r101_fpn_20e_coco/cascade_rcnn_r101_fpn_20e_coco_bbox_mAP-0.425_20200504_231812-5057dcc5.pth
- Name: cascade-rcnn_x101-32x4d_fpn_1x_coco
In Collection: Cascade R-CNN
Config: configs/cascade_rcnn/cascade-rcnn_x101-32x4d_fpn_1x_coco.py
Metadata:
Training Memory (GB): 7.6
inference time (ms/im):
- value: 91.74
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (800, 1333)
Epochs: 12
Results:
- Task: Object Detection
Dataset: COCO
Metrics:
box AP: 43.7
Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_x101_32x4d_fpn_1x_coco/cascade_rcnn_x101_32x4d_fpn_1x_coco_20200316-95c2deb6.pth
- Name: cascade-rcnn_x101-32x4d_fpn_20e_coco
In Collection: Cascade R-CNN
Config: configs/cascade_rcnn/cascade-rcnn_x101-32x4d_fpn_20e_coco.py
Metadata:
Training Memory (GB): 7.6
Epochs: 20
Results:
- Task: Object Detection
Dataset: COCO
Metrics:
box AP: 43.7
Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_x101_32x4d_fpn_20e_coco/cascade_rcnn_x101_32x4d_fpn_20e_coco_20200906_134608-9ae0a720.pth
- Name: cascade-rcnn_x101-64x4d_fpn_1x_coco
In Collection: Cascade R-CNN
Config: configs/cascade_rcnn/cascade-rcnn_x101-64x4d_fpn_1x_coco.py
Metadata:
Training Memory (GB): 10.7
Epochs: 12
Results:
- Task: Object Detection
Dataset: COCO
Metrics:
box AP: 44.7
Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_x101_64x4d_fpn_1x_coco/cascade_rcnn_x101_64x4d_fpn_1x_coco_20200515_075702-43ce6a30.pth
- Name: cascade-rcnn_x101_64x4d_fpn_20e_coco
In Collection: Cascade R-CNN
Config: configs/cascade_rcnn/cascade-rcnn_x101_64x4d_fpn_20e_coco.py
Metadata:
Training Memory (GB): 10.7
Epochs: 20
Results:
- Task: Object Detection
Dataset: COCO
Metrics:
box AP: 44.5
Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_x101_64x4d_fpn_20e_coco/cascade_rcnn_x101_64x4d_fpn_20e_coco_20200509_224357-051557b1.pth
- Name: cascade-mask-rcnn_r50-caffe_fpn_1x_coco
In Collection: Cascade R-CNN
Config: configs/cascade_rcnn/cascade-mask-rcnn_r50-caffe_fpn_1x_coco.py
Metadata:
Training Memory (GB): 5.9
Epochs: 12
Results:
- Task: Object Detection
Dataset: COCO
Metrics:
box AP: 41.2
- Task: Instance Segmentation
Dataset: COCO
Metrics:
mask AP: 36.0
Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r50_caffe_fpn_1x_coco/cascade_mask_rcnn_r50_caffe_fpn_1x_coco_bbox_mAP-0.412__segm_mAP-0.36_20200504_174659-5004b251.pth
- Name: cascade-mask-rcnn_r50_fpn_1x_coco
In Collection: Cascade R-CNN
Config: configs/cascade_rcnn/cascade-mask-rcnn_r50_fpn_1x_coco.py
Metadata:
Training Memory (GB): 6.0
inference time (ms/im):
- value: 89.29
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (800, 1333)
Epochs: 12
Results:
- Task: Object Detection
Dataset: COCO
Metrics:
box AP: 41.2
- Task: Instance Segmentation
Dataset: COCO
Metrics:
mask AP: 35.9
Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r50_fpn_1x_coco/cascade_mask_rcnn_r50_fpn_1x_coco_20200203-9d4dcb24.pth
- Name: cascade-mask-rcnn_r50_fpn_20e_coco
In Collection: Cascade R-CNN
Config: configs/cascade_rcnn/cascade-mask-rcnn_r50_fpn_20e_coco.py
Metadata:
Training Memory (GB): 6.0
inference time (ms/im):
- value: 89.29
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (800, 1333)
Epochs: 20
Results:
- Task: Object Detection
Dataset: COCO
Metrics:
box AP: 41.9
- Task: Instance Segmentation
Dataset: COCO
Metrics:
mask AP: 36.5
Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r50_fpn_20e_coco/cascade_mask_rcnn_r50_fpn_20e_coco_bbox_mAP-0.419__segm_mAP-0.365_20200504_174711-4af8e66e.pth
- Name: cascade-mask-rcnn_r101-caffe_fpn_1x_coco
In Collection: Cascade R-CNN
Config: configs/cascade_rcnn/cascade-mask-rcnn_r101-caffe_fpn_1x_coco.py
Metadata:
Training Memory (GB): 7.8
Epochs: 12
Results:
- Task: Object Detection
Dataset: COCO
Metrics:
box AP: 43.2
- Task: Instance Segmentation
Dataset: COCO
Metrics:
mask AP: 37.6
Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r101_caffe_fpn_1x_coco/cascade_mask_rcnn_r101_caffe_fpn_1x_coco_bbox_mAP-0.432__segm_mAP-0.376_20200504_174813-5c1e9599.pth
- Name: cascade-mask-rcnn_r101_fpn_1x_coco
In Collection: Cascade R-CNN
Config: configs/cascade_rcnn/cascade-mask-rcnn_r101_fpn_1x_coco.py
Metadata:
Training Memory (GB): 7.9
inference time (ms/im):
- value: 102.04
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (800, 1333)
Epochs: 12
Results:
- Task: Object Detection
Dataset: COCO
Metrics:
box AP: 42.9
- Task: Instance Segmentation
Dataset: COCO
Metrics:
mask AP: 37.3
Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r101_fpn_1x_coco/cascade_mask_rcnn_r101_fpn_1x_coco_20200203-befdf6ee.pth
- Name: cascade-mask-rcnn_r101_fpn_20e_coco
In Collection: Cascade R-CNN
Config: configs/cascade_rcnn/cascade-mask-rcnn_r101_fpn_20e_coco.py
Metadata:
Training Memory (GB): 7.9
inference time (ms/im):
- value: 102.04
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (800, 1333)
Epochs: 20
Results:
- Task: Object Detection
Dataset: COCO
Metrics:
box AP: 43.4
- Task: Instance Segmentation
Dataset: COCO
Metrics:
mask AP: 37.8
Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r101_fpn_20e_coco/cascade_mask_rcnn_r101_fpn_20e_coco_bbox_mAP-0.434__segm_mAP-0.378_20200504_174836-005947da.pth
- Name: cascade-mask-rcnn_x101-32x4d_fpn_1x_coco
In Collection: Cascade R-CNN
Config: configs/cascade_rcnn/cascade-mask-rcnn_x101-32x4d_fpn_1x_coco.py
Metadata:
Training Memory (GB): 9.2
inference time (ms/im):
- value: 116.28
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (800, 1333)
Epochs: 12
Results:
- Task: Object Detection
Dataset: COCO
Metrics:
box AP: 44.3
- Task: Instance Segmentation
Dataset: COCO
Metrics:
mask AP: 38.3
Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_1x_coco_20200201-0f411b1f.pth
- Name: cascade-mask-rcnn_x101-32x4d_fpn_20e_coco
In Collection: Cascade R-CNN
Config: configs/cascade_rcnn/cascade-mask-rcnn_x101-32x4d_fpn_20e_coco.py
Metadata:
Training Memory (GB): 9.2
inference time (ms/im):
- value: 116.28
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (800, 1333)
Epochs: 20
Results:
- Task: Object Detection
Dataset: COCO
Metrics:
box AP: 45.0
- Task: Instance Segmentation
Dataset: COCO
Metrics:
mask AP: 39.0
Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_20e_coco/cascade_mask_rcnn_x101_32x4d_fpn_20e_coco_20200528_083917-ed1f4751.pth
- Name: cascade-mask-rcnn_x101-64x4d_fpn_1x_coco
In Collection: Cascade R-CNN
Config: configs/cascade_rcnn/cascade-mask-rcnn_x101-64x4d_fpn_1x_coco.py
Metadata:
Training Memory (GB): 12.2
inference time (ms/im):
- value: 149.25
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (800, 1333)
Epochs: 12
Results:
- Task: Object Detection
Dataset: COCO
Metrics:
box AP: 45.3
- Task: Instance Segmentation
Dataset: COCO
Metrics:
mask AP: 39.2
Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_x101_64x4d_fpn_1x_coco/cascade_mask_rcnn_x101_64x4d_fpn_1x_coco_20200203-9a2db89d.pth
- Name: cascade-mask-rcnn_x101-64x4d_fpn_20e_coco
In Collection: Cascade R-CNN
Config: configs/cascade_rcnn/cascade-mask-rcnn_x101-64x4d_fpn_20e_coco.py
Metadata:
Training Memory (GB): 12.2
Epochs: 20
Results:
- Task: Object Detection
Dataset: COCO
Metrics:
box AP: 45.6
- Task: Instance Segmentation
Dataset: COCO
Metrics:
mask AP: 39.5
Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_x101_64x4d_fpn_20e_coco/cascade_mask_rcnn_x101_64x4d_fpn_20e_coco_20200512_161033-bdb5126a.pth
- Name: cascade-mask-rcnn_r50-caffe_fpn_ms-3x_coco
In Collection: Cascade R-CNN
Config: configs/cascade_rcnn/cascade-mask-rcnn_r50-caffe_fpn_ms-3x_coco.py
Metadata:
Training Memory (GB): 5.7
Epochs: 36
Results:
- Task: Object Detection
Dataset: COCO
Metrics:
box AP: 44.0
- Task: Instance Segmentation
Dataset: COCO
Metrics:
mask AP: 38.1
Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r50_caffe_fpn_mstrain_3x_coco/cascade_mask_rcnn_r50_caffe_fpn_mstrain_3x_coco_20210707_002651-6e29b3a6.pth
- Name: cascade-mask-rcnn_r50_fpn_mstrain_3x_coco
In Collection: Cascade R-CNN
Config: configs/cascade_rcnn/cascade-mask-rcnn_r50_fpn_ms-3x_coco.py
Metadata:
Training Memory (GB): 5.9
Epochs: 36
Results:
- Task: Object Detection
Dataset: COCO
Metrics:
box AP: 44.3
- Task: Instance Segmentation
Dataset: COCO
Metrics:
mask AP: 38.5
Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r50_fpn_mstrain_3x_coco/cascade_mask_rcnn_r50_fpn_mstrain_3x_coco_20210628_164719-5bdc3824.pth
- Name: cascade-mask-rcnn_r101-caffe_fpn_ms-3x_coco
In Collection: Cascade R-CNN
Config: configs/cascade_rcnn/cascade-mask-rcnn_r101-caffe_fpn_ms-3x_coco.py
Metadata:
Training Memory (GB): 7.7
Epochs: 36
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/cascade_rcnn/cascade_mask_rcnn_r101_caffe_fpn_mstrain_3x_coco/cascade_mask_rcnn_r101_caffe_fpn_mstrain_3x_coco_20210707_002620-a5bd2389.pth
- Name: cascade-mask-rcnn_r101_fpn_ms-3x_coco
In Collection: Cascade R-CNN
Config: configs/cascade_rcnn/cascade-mask-rcnn_r101_fpn_ms-3x_coco.py
Metadata:
Training Memory (GB): 7.8
Epochs: 36
Results:
- Task: Object Detection
Dataset: COCO
Metrics:
box AP: 45.5
- Task: Instance Segmentation
Dataset: COCO
Metrics:
mask AP: 39.6
Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r101_fpn_mstrain_3x_coco/cascade_mask_rcnn_r101_fpn_mstrain_3x_coco_20210628_165236-51a2d363.pth
- Name: cascade-mask-rcnn_x101-32x4d_fpn_ms-3x_coco
In Collection: Cascade R-CNN
Config: configs/cascade_rcnn/cascade-mask-rcnn_x101-32x4d_fpn_ms-3x_coco.py
Metadata:
Training Memory (GB): 9.0
Epochs: 36
Results:
- Task: Object Detection
Dataset: COCO
Metrics:
box AP: 46.3
- Task: Instance Segmentation
Dataset: COCO
Metrics:
mask AP: 40.1
Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_mstrain_3x_coco/cascade_mask_rcnn_x101_32x4d_fpn_mstrain_3x_coco_20210706_225234-40773067.pth
- Name: cascade-mask-rcnn_x101-32x8d_fpn_ms-3x_coco
In Collection: Cascade R-CNN
Config: configs/cascade_rcnn/cascade-mask-rcnn_x101-32x8d_fpn_ms-3x_coco.py
Metadata:
Training Memory (GB): 12.1
Epochs: 36
Results:
- Task: Object Detection
Dataset: COCO
Metrics:
box AP: 46.1
- Task: Instance Segmentation
Dataset: COCO
Metrics:
mask AP: 39.9
Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_x101_32x8d_fpn_mstrain_3x_coco/cascade_mask_rcnn_x101_32x8d_fpn_mstrain_3x_coco_20210719_180640-9ff7e76f.pth
- Name: cascade-mask-rcnn_x101-64x4d_fpn_ms-3x_coco
In Collection: Cascade R-CNN
Config: configs/cascade_rcnn/cascade-mask-rcnn_x101-64x4d_fpn_ms-3x_coco.py
Metadata:
Training Memory (GB): 12.0
Epochs: 36
Results:
- Task: Object Detection
Dataset: COCO
Metrics:
box AP: 46.6
- Task: Instance Segmentation
Dataset: COCO
Metrics:
mask AP: 40.3
Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_x101_64x4d_fpn_mstrain_3x_coco/cascade_mask_rcnn_x101_64x4d_fpn_mstrain_3x_coco_20210719_210311-d3e64ba0.pth
mmde/mmdet/.mim/configs/cascade_rpn/cascade-rpn_fast-rcnn_r50-caffe_fpn_1x_coco.py 0 → 100644
View file @ eb1107e4
_base_ = '../fast_rcnn/fast-rcnn_r50-caffe_fpn_1x_coco.py'
model = dict(
roi_head=dict(
bbox_head=dict(
bbox_coder=dict(target_stds=[0.04, 0.04, 0.08, 0.08]),
loss_cls=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.5),
loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0))),
# model training and testing settings
train_cfg=dict(
rcnn=dict(
assigner=dict(
pos_iou_thr=0.65, neg_iou_thr=0.65, min_pos_iou=0.65),
sampler=dict(num=256))),
test_cfg=dict(rcnn=dict(score_thr=1e-3)))
# MMEngine support the following two ways, users can choose
# according to convenience
# train_dataloader = dict(dataset=dict(proposal_file='proposals/crpn_r50_caffe_fpn_1x_train2017.pkl')) # noqa
_base_.train_dataloader.dataset.proposal_file = 'proposals/crpn_r50_caffe_fpn_1x_train2017.pkl' # noqa
# val_dataloader = dict(dataset=dict(proposal_file='proposals/crpn_r50_caffe_fpn_1x_val2017.pkl')) # noqa
# test_dataloader = val_dataloader
_base_.val_dataloader.dataset.proposal_file = 'proposals/crpn_r50_caffe_fpn_1x_val2017.pkl' # noqa
test_dataloader = _base_.val_dataloader
optim_wrapper = dict(clip_grad=dict(max_norm=35, norm_type=2))
mmde/mmdet/.mim/configs/cascade_rpn/cascade-rpn_faster-rcnn_r50-caffe_fpn_1x_coco.py 0 → 100644
View file @ eb1107e4
_base_ = '../faster_rcnn/faster-rcnn_r50-caffe_fpn_1x_coco.py'
rpn_weight = 0.7
model = dict(
rpn_head=dict(
_delete_=True,
type='CascadeRPNHead',
num_stages=2,
stages=[
dict(
type='StageCascadeRPNHead',
in_channels=256,
feat_channels=256,
anchor_generator=dict(
type='AnchorGenerator',
scales=[8],
ratios=[1.0],
strides=[4, 8, 16, 32, 64]),
adapt_cfg=dict(type='dilation', dilation=3),
bridged_feature=True,
with_cls=False,
reg_decoded_bbox=True,
bbox_coder=dict(
type='DeltaXYWHBBoxCoder',
target_means=(.0, .0, .0, .0),
target_stds=(0.1, 0.1, 0.5, 0.5)),
loss_bbox=dict(
type='IoULoss', linear=True,
loss_weight=10.0 * rpn_weight)),
dict(
type='StageCascadeRPNHead',
in_channels=256,
feat_channels=256,
adapt_cfg=dict(type='offset'),
bridged_feature=False,
with_cls=True,
reg_decoded_bbox=True,
bbox_coder=dict(
type='DeltaXYWHBBoxCoder',
target_means=(.0, .0, .0, .0),
target_stds=(0.05, 0.05, 0.1, 0.1)),
loss_cls=dict(
type='CrossEntropyLoss',
use_sigmoid=True,
loss_weight=1.0 * rpn_weight),
loss_bbox=dict(
type='IoULoss', linear=True,
loss_weight=10.0 * rpn_weight))
]),
roi_head=dict(
bbox_head=dict(
bbox_coder=dict(target_stds=[0.04, 0.04, 0.08, 0.08]),
loss_cls=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.5),
loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0))),
# model training and testing settings
train_cfg=dict(
rpn=[
dict(
assigner=dict(
type='RegionAssigner', center_ratio=0.2, ignore_ratio=0.5),
allowed_border=-1,
pos_weight=-1,
debug=False),
dict(
assigner=dict(
type='MaxIoUAssigner',
pos_iou_thr=0.7,
neg_iou_thr=0.7,
min_pos_iou=0.3,
ignore_iof_thr=-1),
sampler=dict(
type='RandomSampler',
num=256,
pos_fraction=0.5,
neg_pos_ub=-1,
add_gt_as_proposals=False),
allowed_border=-1,
pos_weight=-1,
debug=False)
],
rpn_proposal=dict(max_per_img=300, nms=dict(iou_threshold=0.8)),
rcnn=dict(
assigner=dict(
pos_iou_thr=0.65, neg_iou_thr=0.65, min_pos_iou=0.65),
sampler=dict(type='RandomSampler', num=256))),
test_cfg=dict(
rpn=dict(max_per_img=300, nms=dict(iou_threshold=0.8)),
rcnn=dict(score_thr=1e-3)))
optim_wrapper = dict(clip_grad=dict(max_norm=35, norm_type=2))
mmde/mmdet/.mim/configs/cascade_rpn/cascade-rpn_r50-caffe_fpn_1x_coco.py 0 → 100644
View file @ eb1107e4
_base_ = '../rpn/rpn_r50-caffe_fpn_1x_coco.py'
model = dict(
rpn_head=dict(
_delete_=True,
type='CascadeRPNHead',
num_stages=2,
stages=[
dict(
type='StageCascadeRPNHead',
in_channels=256,
feat_channels=256,
anchor_generator=dict(
type='AnchorGenerator',
scales=[8],
ratios=[1.0],
strides=[4, 8, 16, 32, 64]),
adapt_cfg=dict(type='dilation', dilation=3),
bridged_feature=True,
sampling=False,
with_cls=False,
reg_decoded_bbox=True,
bbox_coder=dict(
type='DeltaXYWHBBoxCoder',
target_means=(.0, .0, .0, .0),
target_stds=(0.1, 0.1, 0.5, 0.5)),
loss_bbox=dict(type='IoULoss', linear=True, loss_weight=10.0)),
dict(
type='StageCascadeRPNHead',
in_channels=256,
feat_channels=256,
adapt_cfg=dict(type='offset'),
bridged_feature=False,
sampling=True,
with_cls=True,
reg_decoded_bbox=True,
bbox_coder=dict(
type='DeltaXYWHBBoxCoder',
target_means=(.0, .0, .0, .0),
target_stds=(0.05, 0.05, 0.1, 0.1)),
loss_cls=dict(
type='CrossEntropyLoss', use_sigmoid=True,
loss_weight=1.0),
loss_bbox=dict(type='IoULoss', linear=True, loss_weight=10.0))
]),
train_cfg=dict(rpn=[
dict(
assigner=dict(
type='RegionAssigner', center_ratio=0.2, ignore_ratio=0.5),
allowed_border=-1,
pos_weight=-1,
debug=False),
dict(
assigner=dict(
type='MaxIoUAssigner',
pos_iou_thr=0.7,
neg_iou_thr=0.7,
min_pos_iou=0.3,
ignore_iof_thr=-1,
iou_calculator=dict(type='BboxOverlaps2D')),
sampler=dict(
type='RandomSampler',
num=256,
pos_fraction=0.5,
neg_pos_ub=-1,
add_gt_as_proposals=False),
allowed_border=-1,
pos_weight=-1,
debug=False)
]),
test_cfg=dict(
rpn=dict(
nms_pre=2000,
max_per_img=2000,
nms=dict(type='nms', iou_threshold=0.8),
min_bbox_size=0)))
optim_wrapper = dict(clip_grad=dict(max_norm=35, norm_type=2))
mmde/mmdet/.mim/configs/cascade_rpn/metafile.yml 0 → 100644
View file @ eb1107e4
Collections:
- Name: Cascade RPN
Metadata:
Training Data: COCO
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Resources: 8x V100 GPUs
Architecture:
- Cascade RPN
- FPN
- ResNet
Paper:
URL: https://arxiv.org/abs/1909.06720
Title: 'Cascade RPN: Delving into High-Quality Region Proposal Network with Adaptive Convolution'
README: configs/cascade_rpn/README.md
Code:
URL: https://github.com/open-mmlab/mmdetection/blob/v2.8.0/mmdet/models/dense_heads/cascade_rpn_head.py#L538
Version: v2.8.0
Models:
- Name: cascade-rpn_fast-rcnn_r50-caffe_fpn_1x_coco
In Collection: Cascade RPN
Config: configs/cascade_rpn/cascade-rpn_fast-rcnn_r50-caffe_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/cascade_rpn/crpn_fast_rcnn_r50_caffe_fpn_1x_coco/crpn_fast_rcnn_r50_caffe_fpn_1x_coco-cb486e66.pth
- Name: cascade-rpn_faster-rcnn_r50-caffe_fpn_1x_coco
In Collection: Cascade RPN
Config: configs/cascade_rpn/cascade-rpn_faster-rcnn_r50-caffe_fpn_1x_coco.py
Metadata:
Epochs: 12
Results:
- Task: Object Detection
Dataset: COCO
Metrics:
box AP: 40.4
Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rpn/crpn_faster_rcnn_r50_caffe_fpn_1x_coco/crpn_faster_rcnn_r50_caffe_fpn_1x_coco-c8283cca.pth
mmde/mmdet/.mim/configs/centernet/centernet-update_r101_fpn_8xb8-amp-lsj-200e_coco.py 0 → 100644
View file @ eb1107e4
_base_ = './centernet-update_r50_fpn_8xb8-amp-lsj-200e_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
mmde/mmdet/.mim/configs/centernet/centernet-update_r18_fpn_8xb8-amp-lsj-200e_coco.py 0 → 100644
View file @ eb1107e4
_base_ = './centernet-update_r50_fpn_8xb8-amp-lsj-200e_coco.py'
model = dict(
backbone=dict(
depth=18,
init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet18')),
neck=dict(in_channels=[64, 128, 256, 512]))
mmde/mmdet/.mim/configs/centernet/centernet-update_r50-caffe_fpn_ms-1x_coco.py 0 → 100644
View file @ eb1107e4
_base_ = [
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
type='CenterNet',
# use caffe img_norm
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[103.530, 116.280, 123.675],
std=[1.0, 1.0, 1.0],
bgr_to_rgb=False,
pad_size_divisor=32),
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=False),
norm_eval=True,
style='caffe',
init_cfg=dict(
type='Pretrained',
checkpoint='open-mmlab://detectron2/resnet50_caffe')),
neck=dict(
type='FPN',
in_channels=[256, 512, 1024, 2048],
out_channels=256,
start_level=1,
add_extra_convs='on_output',
num_outs=5,
# There is a chance to get 40.3 after switching init_cfg,
# otherwise it is about 39.9~40.1
init_cfg=dict(type='Caffe2Xavier', layer='Conv2d'),
relu_before_extra_convs=True),
bbox_head=dict(
type='CenterNetUpdateHead',
num_classes=80,
in_channels=256,
stacked_convs=4,
feat_channels=256,
strides=[8, 16, 32, 64, 128],
hm_min_radius=4,
hm_min_overlap=0.8,
more_pos_thresh=0.2,
more_pos_topk=9,
soft_weight_on_reg=False,
loss_cls=dict(
type='GaussianFocalLoss',
pos_weight=0.25,
neg_weight=0.75,
loss_weight=1.0),
loss_bbox=dict(type='GIoULoss', loss_weight=2.0),
),
train_cfg=None,
test_cfg=dict(
nms_pre=1000,
min_bbox_size=0,
score_thr=0.05,
nms=dict(type='nms', iou_threshold=0.6),
max_per_img=100))
# single-scale training is about 39.3
train_pipeline = [
dict(type='LoadImageFromFile', backend_args={{_base_.backend_args}}),
dict(type='LoadAnnotations', with_bbox=True),
dict(
type='RandomChoiceResize',
scales=[(1333, 640), (1333, 672), (1333, 704), (1333, 736),
(1333, 768), (1333, 800)],
keep_ratio=True),
dict(type='RandomFlip', prob=0.5),
dict(type='PackDetInputs')
]
train_dataloader = dict(dataset=dict(pipeline=train_pipeline))
# learning rate
param_scheduler = [
dict(
type='LinearLR',
start_factor=0.00025,
by_epoch=False,
begin=0,
end=4000),
dict(
type='MultiStepLR',
begin=0,
end=12,
by_epoch=True,
milestones=[8, 11],
gamma=0.1)
]
optim_wrapper = dict(
optimizer=dict(lr=0.01),
# Experiments show that there is no need to turn on clip_grad.
paramwise_cfg=dict(norm_decay_mult=0.))
# 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)
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