repo stringlengths 2 99 | file stringlengths 13 225 | code stringlengths 0 18.3M | file_length int64 0 18.3M | avg_line_length float64 0 1.36M | max_line_length int64 0 4.26M | extension_type stringclasses 1
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ERD | ERD-main/configs/_base_/schedules/schedule_1x.py | # training schedule for 1x
train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=12, val_interval=1)
val_cfg = dict(type='ValLoop')
test_cfg = dict(type='TestLoop')
# learning rate
param_scheduler = [
dict(
type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
dict(
type='Mu... | 814 | 27.103448 | 79 | py |
ERD | ERD-main/configs/_base_/schedules/schedule_2x.py | # training schedule for 2x
train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=24, val_interval=1)
val_cfg = dict(type='ValLoop')
test_cfg = dict(type='TestLoop')
# learning rate
param_scheduler = [
dict(
type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
dict(
type='Mu... | 815 | 27.137931 | 79 | py |
ERD | ERD-main/configs/_base_/datasets/semi_coco_detection.py | # dataset settings
dataset_type = 'CocoDataset'
data_root = 'data/coco/'
# Example to use different file client
# Method 1: simply set the data root and let the file I/O module
# automatically infer from prefix (not support LMDB and Memcache yet)
# data_root = 's3://openmmlab/datasets/detection/coco/'
# Method 2: Us... | 5,916 | 32.055866 | 78 | py |
ERD | ERD-main/configs/_base_/datasets/deepfashion.py | # dataset settings
dataset_type = 'DeepFashionDataset'
data_root = 'data/DeepFashion/In-shop/'
# Example to use different file client
# Method 1: simply set the data root and let the file I/O module
# automatically infer from prefix (not support LMDB and Memcache yet)
# data_root = 's3://openmmlab/datasets/detection/... | 3,198 | 32.322917 | 78 | py |
ERD | ERD-main/configs/_base_/datasets/objects365v1_detection.py | # dataset settings
dataset_type = 'Objects365V1Dataset'
data_root = 'data/Objects365/Obj365_v1/'
# Example to use different file client
# Method 1: simply set the data root and let the file I/O module
# automatically infer from prefix (not support LMDB and Memcache yet)
# data_root = 's3://openmmlab/datasets/detectio... | 2,481 | 32.093333 | 78 | py |
ERD | ERD-main/configs/_base_/datasets/coco_instance.py | # dataset settings
dataset_type = 'CocoDataset'
data_root = 'data/coco/'
# Example to use different file client
# Method 1: simply set the data root and let the file I/O module
# automatically infer from prefix (not support LMDB and Memcache yet)
# data_root = 's3://openmmlab/datasets/detection/coco/'
# Method 2: Us... | 3,238 | 32.739583 | 78 | py |
ERD | ERD-main/configs/_base_/datasets/coco_instance_semantic.py | # dataset settings
dataset_type = 'CocoDataset'
data_root = 'data/coco/'
# Example to use different file client
# Method 1: simply set the data root and let the file I/O module
# automatically infer from prefix (not support LMDB and Memcache yet)
# data_root = 's3://openmmlab/datasets/detection/coco/'
# Method 2: Us... | 2,574 | 31.594937 | 79 | py |
ERD | ERD-main/configs/_base_/datasets/openimages_detection.py | # dataset settings
dataset_type = 'OpenImagesDataset'
data_root = 'data/OpenImages/'
# Example to use different file client
# Method 1: simply set the data root and let the file I/O module
# automatically infer from prefix (not support LMDB and Memcache yet)
# data_root = 's3://openmmlab/datasets/detection/coco/'
# ... | 2,979 | 35.341463 | 78 | py |
ERD | ERD-main/configs/_base_/datasets/objects365v2_detection.py | # dataset settings
dataset_type = 'Objects365V2Dataset'
data_root = 'data/Objects365/Obj365_v2/'
# Example to use different file client
# Method 1: simply set the data root and let the file I/O module
# automatically infer from prefix (not support LMDB and Memcache yet)
# data_root = 's3://openmmlab/datasets/detectio... | 2,464 | 32.310811 | 78 | py |
ERD | ERD-main/configs/_base_/datasets/cityscapes_detection.py | # dataset settings
dataset_type = 'CityscapesDataset'
data_root = 'data/cityscapes/'
# Example to use different file client
# Method 1: simply set the data root and let the file I/O module
# automatically infer from prefix (not support LMDB and Memcache yet)
# data_root = 's3://openmmlab/datasets/segmentation/citysca... | 2,729 | 31.117647 | 78 | py |
ERD | ERD-main/configs/_base_/datasets/voc0712.py | # dataset settings
dataset_type = 'VOCDataset'
data_root = 'data/VOCdevkit/'
# Example to use different file client
# Method 1: simply set the data root and let the file I/O module
# automatically Infer from prefix (not support LMDB and Memcache yet)
# data_root = 's3://openmmlab/datasets/detection/segmentation/VOCde... | 3,434 | 35.935484 | 79 | py |
ERD | ERD-main/configs/_base_/datasets/lvis_v1_instance.py | # dataset settings
_base_ = 'lvis_v0.5_instance.py'
dataset_type = 'LVISV1Dataset'
data_root = 'data/lvis_v1/'
train_dataloader = dict(
dataset=dict(
dataset=dict(
type=dataset_type,
data_root=data_root,
ann_file='annotations/lvis_v1_train.json',
data_prefix=... | 656 | 27.565217 | 73 | py |
ERD | ERD-main/configs/_base_/datasets/cityscapes_instance.py | # dataset settings
dataset_type = 'CityscapesDataset'
data_root = 'data/cityscapes/'
# Example to use different file client
# Method 1: simply set the data root and let the file I/O module
# automatically infer from prefix (not support LMDB and Memcache yet)
# data_root = 's3://openmmlab/datasets/segmentation/citysca... | 3,725 | 31.684211 | 76 | py |
ERD | ERD-main/configs/_base_/datasets/coco_detection.py | # dataset settings
dataset_type = 'CocoDataset'
data_root = '../data/coco/'
# Example to use different file client
# Method 1: simply set the data root and let the file I/O module
# automatically infer from prefix (not support LMDB and Memcache yet)
# data_root = 's3://openmmlab/datasets/detection/coco/'
# Method 2:... | 3,190 | 32.239583 | 78 | py |
ERD | ERD-main/configs/_base_/datasets/lvis_v0.5_instance.py | # dataset settings
dataset_type = 'LVISV05Dataset'
data_root = 'data/lvis_v0.5/'
# Example to use different file client
# Method 1: simply set the data root and let the file I/O module
# automatically infer from prefix (not support LMDB and Memcache yet)
# data_root = 's3://openmmlab/datasets/detection/lvis_v0.5/'
#... | 2,646 | 32.0875 | 78 | py |
ERD | ERD-main/configs/_base_/datasets/coco_panoptic.py | # dataset settings
dataset_type = 'CocoPanopticDataset'
# data_root = 'data/coco/'
# Example to use different file client
# Method 1: simply set the data root and let the file I/O module
# automatically infer from prefix (not support LMDB and Memcache yet)
data_root = 's3://openmmlab/datasets/detection/coco/'
# Meth... | 3,277 | 33.505263 | 79 | py |
ERD | ERD-main/configs/_base_/datasets/wider_face.py | # dataset settings
dataset_type = 'WIDERFaceDataset'
data_root = 'data/WIDERFace/'
# Example to use different file client
# Method 1: simply set the data root and let the file I/O module
# automatically infer from prefix (not support LMDB and Memcache yet)
# data_root = 's3://openmmlab/datasets/detection/cityscapes/'
... | 2,338 | 30.608108 | 78 | py |
ERD | ERD-main/configs/libra_rcnn/libra-faster-rcnn_r50_fpn_1x_coco.py | _base_ = '../faster_rcnn/faster-rcnn_r50_fpn_1x_coco.py'
# model settings
model = dict(
neck=[
dict(
type='FPN',
in_channels=[256, 512, 1024, 2048],
out_channels=256,
num_outs=5),
dict(
type='BFP',
in_channels=256,
n... | 1,268 | 29.214286 | 68 | py |
ERD | ERD-main/configs/libra_rcnn/libra-faster-rcnn_r101_fpn_1x_coco.py | _base_ = './libra-faster-rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 205 | 28.428571 | 61 | py |
ERD | ERD-main/configs/libra_rcnn/libra-faster-rcnn_x101-64x4d_fpn_1x_coco.py | _base_ = './libra-faster-rcnn_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='pyt... | 427 | 27.533333 | 76 | py |
ERD | ERD-main/configs/libra_rcnn/libra-retinanet_r50_fpn_1x_coco.py | _base_ = '../retinanet/retinanet_r50_fpn_1x_coco.py'
# model settings
model = dict(
neck=[
dict(
type='FPN',
in_channels=[256, 512, 1024, 2048],
out_channels=256,
start_level=1,
add_extra_convs='on_input',
num_outs=5),
dict(
... | 674 | 24 | 52 | py |
ERD | ERD-main/configs/libra_rcnn/libra-fast-rcnn_r50_fpn_1x_coco.py | _base_ = '../fast_rcnn/fast-rcnn_r50_fpn_1x_coco.py'
# model settings
model = dict(
neck=[
dict(
type='FPN',
in_channels=[256, 512, 1024, 2048],
out_channels=256,
num_outs=5),
dict(
type='BFP',
in_channels=256,
num_l... | 1,775 | 32.509434 | 104 | py |
ERD | ERD-main/configs/pvt/retinanet_pvtv2-b2_fpn_1x_coco.py | _base_ = 'retinanet_pvtv2-b0_fpn_1x_coco.py'
model = dict(
backbone=dict(
embed_dims=64,
num_layers=[3, 4, 6, 3],
init_cfg=dict(checkpoint='https://github.com/whai362/PVT/'
'releases/download/v2/pvt_v2_b2.pth')),
neck=dict(in_channels=[64, 128, 320, 512]))
| 311 | 33.666667 | 66 | py |
ERD | ERD-main/configs/pvt/retinanet_pvtv2-b1_fpn_1x_coco.py | _base_ = 'retinanet_pvtv2-b0_fpn_1x_coco.py'
model = dict(
backbone=dict(
embed_dims=64,
init_cfg=dict(checkpoint='https://github.com/whai362/PVT/'
'releases/download/v2/pvt_v2_b1.pth')),
neck=dict(in_channels=[64, 128, 320, 512]))
| 278 | 33.875 | 66 | py |
ERD | ERD-main/configs/pvt/retinanet_pvt-m_fpn_1x_coco.py | _base_ = 'retinanet_pvt-t_fpn_1x_coco.py'
model = dict(
backbone=dict(
num_layers=[3, 4, 18, 3],
init_cfg=dict(checkpoint='https://github.com/whai362/PVT/'
'releases/download/v2/pvt_medium.pth')))
| 239 | 33.285714 | 66 | py |
ERD | ERD-main/configs/pvt/retinanet_pvtv2-b4_fpn_1x_coco.py | _base_ = 'retinanet_pvtv2-b0_fpn_1x_coco.py'
model = dict(
backbone=dict(
embed_dims=64,
num_layers=[3, 8, 27, 3],
init_cfg=dict(checkpoint='https://github.com/whai362/PVT/'
'releases/download/v2/pvt_v2_b4.pth')),
neck=dict(in_channels=[64, 128, 320, 512]))
# optimi... | 701 | 32.428571 | 75 | py |
ERD | ERD-main/configs/pvt/retinanet_pvt-t_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 = dict(
type='RetinaNet',
backbone=dict(
_delete_=True,
type='PyramidVisionTransformer',
num_layers=[2, 2, ... | 627 | 32.052632 | 72 | py |
ERD | ERD-main/configs/pvt/retinanet_pvt-s_fpn_1x_coco.py | _base_ = 'retinanet_pvt-t_fpn_1x_coco.py'
model = dict(
backbone=dict(
num_layers=[3, 4, 6, 3],
init_cfg=dict(checkpoint='https://github.com/whai362/PVT/'
'releases/download/v2/pvt_small.pth')))
| 237 | 33 | 66 | py |
ERD | ERD-main/configs/pvt/retinanet_pvtv2-b3_fpn_1x_coco.py | _base_ = 'retinanet_pvtv2-b0_fpn_1x_coco.py'
model = dict(
backbone=dict(
embed_dims=64,
num_layers=[3, 4, 18, 3],
init_cfg=dict(checkpoint='https://github.com/whai362/PVT/'
'releases/download/v2/pvt_v2_b3.pth')),
neck=dict(in_channels=[64, 128, 320, 512]))
| 312 | 33.777778 | 66 | py |
ERD | ERD-main/configs/pvt/retinanet_pvt-l_fpn_1x_coco.py | _base_ = 'retinanet_pvt-t_fpn_1x_coco.py'
model = dict(
backbone=dict(
num_layers=[3, 8, 27, 3],
init_cfg=dict(checkpoint='https://github.com/whai362/PVT/'
'releases/download/v2/pvt_large.pth')))
# Enable automatic-mixed-precision training with AmpOptimWrapper.
optim_wrapper = ... | 349 | 37.888889 | 66 | py |
ERD | ERD-main/configs/pvt/retinanet_pvtv2-b0_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 = dict(
type='RetinaNet',
backbone=dict(
_delete_=True,
type='PyramidVisionTransformerV2',
embed_dims=32,
... | 652 | 31.65 | 72 | py |
ERD | ERD-main/configs/pvt/retinanet_pvtv2-b5_fpn_1x_coco.py | _base_ = 'retinanet_pvtv2-b0_fpn_1x_coco.py'
model = dict(
backbone=dict(
embed_dims=64,
num_layers=[3, 6, 40, 3],
mlp_ratios=(4, 4, 4, 4),
init_cfg=dict(checkpoint='https://github.com/whai362/PVT/'
'releases/download/v2/pvt_v2_b5.pth')),
neck=dict(in_channe... | 734 | 32.409091 | 75 | py |
ERD | ERD-main/configs/efficientnet/retinanet_effb3_fpn_8xb4-crop896-1x_coco.py | _base_ = [
'../_base_/models/retinanet_r50_fpn.py',
'../_base_/schedules/schedule_1x.py',
'../_base_/datasets/coco_detection.py', '../_base_/default_runtime.py'
]
image_size = (896, 896)
batch_augments = [dict(type='BatchFixedSizePad', size=image_size)]
norm_cfg = dict(type='BN', requires_grad=True)
checkp... | 3,148 | 32.147368 | 147 | py |
ERD | ERD-main/configs/autoassign/autoassign_r50-caffe_fpn_1x_coco.py | # We follow the original implementation which
# adopts the Caffe pre-trained backbone.
_base_ = [
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
# model settings
model = dict(
type='AutoAssign',
data_preprocessor=dict(
type='DetData... | 1,923 | 26.485714 | 72 | py |
ERD | ERD-main/configs/gfl_increment/gfl_r50_fpn_1x_coco_first_40_incre_last_40_cats.py | _base_ = [
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
dataset_type = 'CocoDataset'
data_root = '../data/coco/'
backend_args = None
train_pipeline = [
dict(type='LoadImageFromFile', backend_args=backend_args),
dict(type='LoadAnnotation... | 3,820 | 31.65812 | 129 | py |
ERD | ERD-main/configs/gfl_increment/gfl_r50_fpn_1x_coco_first_40_cats.py | _base_ = [
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
dataset_type = 'CocoDataset'
data_root = '../data/coco/'
backend_args = None
train_pipeline = [
dict(type='LoadImageFromFile', backend_args=backend_args),
dict(type='LoadAnnotation... | 3,407 | 29.702703 | 80 | py |
ERD | ERD-main/configs/retinanet/retinanet_r50_fpn_8xb8-amp-lsj-200e_coco.py | _base_ = [
'../_base_/models/retinanet_r50_fpn.py',
'../common/lsj-200e_coco-detection.py'
]
image_size = (1024, 1024)
batch_augments = [dict(type='BatchFixedSizePad', size=image_size)]
model = dict(data_preprocessor=dict(batch_augments=batch_augments))
train_dataloader = dict(batch_size=8, num_workers=4)
# ... | 711 | 31.363636 | 69 | py |
ERD | ERD-main/configs/retinanet/retinanet_r50-caffe_fpn_ms-3x_coco.py | _base_ = './retinanet_r50-caffe_fpn_ms-1x_coco.py'
# training schedule for 2x
train_cfg = dict(max_epochs=36)
# learning rate policy
param_scheduler = [
dict(
type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
dict(
type='MultiStepLR',
begin=0,
end=36,
... | 389 | 20.666667 | 79 | py |
ERD | ERD-main/configs/retinanet/retinanet_tta.py | tta_model = dict(
type='DetTTAModel',
tta_cfg=dict(nms=dict(type='nms', iou_threshold=0.5), max_per_img=100))
img_scales = [(1333, 800), (666, 400), (2000, 1200)]
tta_pipeline = [
dict(type='LoadImageFromFile', backend_args=None),
dict(
type='TestTimeAug',
transforms=[[
dict... | 881 | 35.75 | 77 | py |
ERD | ERD-main/configs/retinanet/retinanet_r50_fpn_2x_coco.py | _base_ = [
'../_base_/models/retinanet_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
# training schedule for 2x
train_cfg = dict(max_epochs=24)
# learning rate policy
param_scheduler = [
dict(
type='LinearLR', start_... | 624 | 23.038462 | 79 | py |
ERD | ERD-main/configs/retinanet/retinanet_r50_fpn_90k_coco.py | _base_ = 'retinanet_r50_fpn_1x_coco.py'
# training schedule for 90k
train_cfg = dict(
_delete_=True,
type='IterBasedTrainLoop',
max_iters=90000,
val_interval=10000)
# learning rate policy
param_scheduler = [
dict(
type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
d... | 639 | 24.6 | 79 | py |
ERD | ERD-main/configs/retinanet/retinanet_r50-caffe_fpn_ms-2x_coco.py | _base_ = './retinanet_r50-caffe_fpn_ms-1x_coco.py'
# training schedule for 2x
train_cfg = dict(max_epochs=24)
# learning rate policy
param_scheduler = [
dict(
type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
dict(
type='MultiStepLR',
begin=0,
end=24,
... | 388 | 21.882353 | 79 | py |
ERD | ERD-main/configs/retinanet/retinanet_r101_fpn_ms-640-800-3x_coco.py | _base_ = ['../_base_/models/retinanet_r50_fpn.py', '../common/ms_3x_coco.py']
# optimizer
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
optim_wrapper = dict(
optimizer=dict(type='SGD', lr=0.01, momentum=0.9,... | 343 | 33.4 | 77 | py |
ERD | ERD-main/configs/retinanet/retinanet_r50-caffe_fpn_ms-1x_coco.py | _base_ = './retinanet_r50-caffe_fpn_1x_coco.py'
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True),
dict(
type='RandomChoiceResize',
scale=[(1333, 640), (1333, 672), (1333, 704), (1333, 736), (1333, 768),
(1333, 800)],
keep... | 470 | 28.4375 | 79 | py |
ERD | ERD-main/configs/retinanet/retinanet_r50_fpn_ms-640-800-3x_coco.py | _base_ = ['../_base_/models/retinanet_r50_fpn.py', '../common/ms_3x_coco.py']
# optimizer
optim_wrapper = dict(
optimizer=dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001))
| 188 | 36.8 | 77 | py |
ERD | ERD-main/configs/retinanet/retinanet_r101_fpn_8xb8-amp-lsj-200e_coco.py | _base_ = './retinanet_r50_fpn_8xb8-amp-lsj-200e_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 213 | 25.75 | 61 | py |
ERD | ERD-main/configs/retinanet/retinanet_x101-64x4d_fpn_ms-640-800-3x_coco.py | _base_ = ['../_base_/models/retinanet_r50_fpn.py', '../common/ms_3x_coco.py']
# optimizer
model = dict(
backbone=dict(
type='ResNeXt',
depth=101,
groups=64,
base_width=4,
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d')))
optim_wrapper... | 365 | 29.5 | 77 | py |
ERD | ERD-main/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',
... | 419 | 27 | 76 | py |
ERD | ERD-main/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'
]
# model
model = dict(
backbone=dict(
depth=18,
init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet18')),
n... | 682 | 31.52381 | 79 | py |
ERD | ERD-main/configs/retinanet/retinanet_r50_fpn_amp-1x_coco.py | _base_ = './retinanet_r50_fpn_1x_coco.py'
# MMEngine support the following two ways, users can choose
# according to convenience
# optim_wrapper = dict(type='AmpOptimWrapper')
_base_.optim_wrapper.type = 'AmpOptimWrapper'
| 223 | 31 | 59 | py |
ERD | ERD-main/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 |
ERD | ERD-main/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',
... | 419 | 27 | 76 | py |
ERD | ERD-main/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 |
ERD | ERD-main/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',
... | 419 | 27 | 76 | py |
ERD | ERD-main/configs/retinanet/retinanet_r101-caffe_fpn_ms-3x_coco.py | _base_ = './retinanet_r50-caffe_fpn_ms-3x_coco.py'
# learning policy
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(
type='Pretrained',
checkpoint='open-mmlab://detectron2/resnet101_caffe')))
| 243 | 26.111111 | 67 | py |
ERD | ERD-main/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',
... | 419 | 27 | 76 | py |
ERD | ERD-main/configs/retinanet/retinanet_r18_fpn_1xb8-1x_coco.py | _base_ = [
'../_base_/models/retinanet_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
# data
train_dataloader = dict(batch_size=8)
# model
model = dict(
backbone=dict(
depth=18,
init_cfg=dict(type='Pretrained'... | 797 | 30.92 | 79 | py |
ERD | ERD-main/configs/retinanet/retinanet_r18_fpn_8xb8-amp-lsj-200e_coco.py | _base_ = './retinanet_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]))
| 237 | 28.75 | 79 | py |
ERD | ERD-main/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 |
ERD | ERD-main/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',
'./retinanet_tta.py'
]
# optimizer
optim_wrapper = dict(
optimizer=dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001))
| 312 | 27.454545 | 75 | py |
ERD | ERD-main/configs/retinanet/retinanet_r50-caffe_fpn_1x_coco.py | _base_ = './retinanet_r50_fpn_1x_coco.py'
model = dict(
data_preprocessor=dict(
type='DetDataPreprocessor',
# use caffe img_norm
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(req... | 507 | 28.882353 | 66 | py |
ERD | ERD-main/configs/misc/d2_mask-rcnn_r50-caffe_fpn_ms-90k_coco.py | _base_ = '../common/ms-poly-90k_coco-instance.py'
# model settings
model = dict(
type='Detectron2Wrapper',
bgr_to_rgb=False,
detector=dict(
# The settings in `d2_detector` will merged into default settings
# in detectron2. More details please refer to
# https://github.com/facebookre... | 3,229 | 37.452381 | 121 | py |
ERD | ERD-main/configs/misc/d2_faster-rcnn_r50-caffe_fpn_ms-90k_coco.py | _base_ = '../common/ms-90k_coco.py'
# model settings
model = dict(
type='Detectron2Wrapper',
bgr_to_rgb=False,
detector=dict(
# The settings in `d2_detector` will merged into default settings
# in detectron2. More details please refer to
# https://github.com/facebookresearch/detectr... | 2,940 | 37.697368 | 115 | py |
ERD | ERD-main/configs/misc/d2_retinanet_r50-caffe_fpn_ms-90k_coco.py | _base_ = '../common/ms-90k_coco.py'
# model settings
model = dict(
type='Detectron2Wrapper',
bgr_to_rgb=False,
detector=dict(
# The settings in `d2_detector` will merged into default settings
# in detectron2. More details please refer to
# https://github.com/facebookresearch/detectr... | 1,987 | 39.571429 | 113 | py |
ERD | ERD-main/configs/free_anchor/freeanchor_x101-32x4d_fpn_1x_coco.py | _base_ = './freeanchor_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,
style='pytorch',
init_cfg=dict(
type='Pretrained', ... | 366 | 25.214286 | 76 | py |
ERD | ERD-main/configs/free_anchor/freeanchor_r101_fpn_1x_coco.py | _base_ = './freeanchor_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 198 | 27.428571 | 61 | py |
ERD | ERD-main/configs/free_anchor/freeanchor_r50_fpn_1x_coco.py | _base_ = '../retinanet/retinanet_r50_fpn_1x_coco.py'
model = dict(
bbox_head=dict(
_delete_=True,
type='FreeAnchorRetinaHead',
num_classes=80,
in_channels=256,
stacked_convs=4,
feat_channels=256,
anchor_generator=dict(
type='AnchorGenerator',
... | 753 | 31.782609 | 74 | py |
ERD | ERD-main/configs/scratch/faster-rcnn_r50-scratch_fpn_gn-all_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_resi... | 1,044 | 25.125 | 79 | py |
ERD | ERD-main/configs/scratch/mask-rcnn_r50-scratch_fpn_gn-all_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_residua... | 1,084 | 25.463415 | 79 | py |
ERD | ERD-main/configs/faster_rcnn/faster-rcnn_r50_fpn_ms-3x_coco.py | _base_ = ['../common/ms_3x_coco.py', '../_base_/models/faster-rcnn_r50_fpn.py']
| 80 | 39.5 | 79 | py |
ERD | ERD-main/configs/faster_rcnn/faster-rcnn_x101-32x4d_fpn_1x_coco.py | _base_ = './faster-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',... | 421 | 27.133333 | 76 | py |
ERD | ERD-main/configs/faster_rcnn/faster-rcnn_x101-64x4d_fpn_ms-3x_coco.py | _base_ = ['../common/ms_3x_coco.py', '../_base_/models/faster-rcnn_r50_fpn.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_... | 457 | 29.533333 | 79 | py |
ERD | ERD-main/configs/faster_rcnn/faster-rcnn_r18_fpn_8xb8-amp-lsj-200e_coco.py | _base_ = './faster-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]))
| 239 | 29 | 79 | py |
ERD | ERD-main/configs/faster_rcnn/faster-rcnn_r50_fpn_bounded-iou_1x_coco.py | _base_ = './faster-rcnn_r50_fpn_1x_coco.py'
model = dict(
roi_head=dict(
bbox_head=dict(
reg_decoded_bbox=True,
loss_bbox=dict(type='BoundedIoULoss', loss_weight=10.0))))
| 207 | 28.714286 | 70 | py |
ERD | ERD-main/configs/faster_rcnn/faster-rcnn_r50_fpn_soft-nms_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(
test_cfg=dict(
rcnn=dict(
score_thr=0.05,
nms=dict(type='soft_nms', iou_threshold=0.5),
... | 347 | 25.769231 | 72 | py |
ERD | ERD-main/configs/faster_rcnn/faster-rcnn_r101_fpn_ms-3x_coco.py | _base_ = 'faster-rcnn_r50_fpn_ms-3x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 201 | 24.25 | 61 | py |
ERD | ERD-main/configs/faster_rcnn/faster-rcnn_x101-32x4d_fpn_ms-3x_coco.py | _base_ = ['../common/ms_3x_coco.py', '../_base_/models/faster-rcnn_r50_fpn.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_... | 457 | 29.533333 | 79 | py |
ERD | ERD-main/configs/faster_rcnn/faster-rcnn_r50-caffe-dc5_ms-1x_coco.py | _base_ = 'faster-rcnn_r50-caffe-dc5_1x_coco.py'
train_pipeline = [
dict(type='LoadImageFromFile', backend_args=_base_.backend_args),
dict(type='LoadAnnotations', with_bbox=True),
dict(
type='RandomChoiceResize',
scale=[(1333, 640), (1333, 672), (1333, 704), (1333, 736), (1333, 768),
... | 498 | 32.266667 | 79 | py |
ERD | ERD-main/configs/faster_rcnn/faster-rcnn_r50_fpn_ciou_1x_coco.py | _base_ = './faster-rcnn_r50_fpn_1x_coco.py'
model = dict(
roi_head=dict(
bbox_head=dict(
reg_decoded_bbox=True,
loss_bbox=dict(type='CIoULoss', loss_weight=12.0))))
| 201 | 27.857143 | 64 | py |
ERD | ERD-main/configs/faster_rcnn/faster-rcnn_r50-caffe_fpn_ms-1x_coco-person.py | _base_ = './faster-rcnn_r50-caffe_fpn_ms-1x_coco.py'
model = dict(roi_head=dict(bbox_head=dict(num_classes=1)))
metainfo = {
'classes': ('person', ),
'palette': [
(220, 20, 60),
]
}
train_dataloader = dict(dataset=dict(metainfo=metainfo))
val_dataloader = dict(dataset=dict(metainfo=metainfo))
test_... | 582 | 37.866667 | 209 | py |
ERD | ERD-main/configs/faster_rcnn/faster-rcnn_r50_fpn_iou_1x_coco.py | _base_ = './faster-rcnn_r50_fpn_1x_coco.py'
model = dict(
roi_head=dict(
bbox_head=dict(
reg_decoded_bbox=True,
loss_bbox=dict(type='IoULoss', loss_weight=10.0))))
| 200 | 27.714286 | 63 | py |
ERD | ERD-main/configs/faster_rcnn/faster-rcnn_x101-32x8d_fpn_ms-3x_coco.py | _base_ = ['../common/ms_3x_coco.py', '../_base_/models/faster-rcnn_r50_fpn.py']
model = dict(
# ResNeXt-101-32x8d model trained with Caffe2 at FB,
# so the mean and std need to be changed.
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[103.530, 116.280, 123.675],
std=[57.3... | 784 | 31.708333 | 79 | py |
ERD | ERD-main/configs/faster_rcnn/faster-rcnn_r50_fpn_ohem_1x_coco.py | _base_ = './faster-rcnn_r50_fpn_1x_coco.py'
model = dict(train_cfg=dict(rcnn=dict(sampler=dict(type='OHEMSampler'))))
| 118 | 38.666667 | 73 | py |
ERD | ERD-main/configs/faster_rcnn/faster-rcnn_r101_fpn_8xb8-amp-lsj-200e_coco.py | _base_ = './faster-rcnn_r50_fpn_8xb8-amp-lsj-200e_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 215 | 26 | 61 | py |
ERD | ERD-main/configs/faster_rcnn/faster-rcnn_r50-caffe_fpn_1x_coco.py | _base_ = './faster-rcnn_r50_fpn_1x_coco.py'
model = dict(
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),
... | 480 | 29.0625 | 66 | py |
ERD | ERD-main/configs/faster_rcnn/faster-rcnn_r101-caffe_fpn_1x_coco.py | _base_ = './faster-rcnn_r50-caffe_fpn_1x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(
type='Pretrained',
checkpoint='open-mmlab://detectron2/resnet101_caffe')))
| 224 | 27.125 | 67 | py |
ERD | ERD-main/configs/faster_rcnn/faster-rcnn_r50-caffe_fpn_ms-90k_coco.py | _base_ = 'faster-rcnn_r50-caffe_fpn_ms-1x_coco.py'
max_iter = 90000
param_scheduler = [
dict(
type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
dict(
type='MultiStepLR',
begin=0,
end=max_iter,
by_epoch=False,
milestones=[60000, 80000],
... | 561 | 22.416667 | 79 | py |
ERD | ERD-main/configs/faster_rcnn/faster-rcnn_r50-caffe-dc5_1x_coco.py | _base_ = [
'../_base_/models/faster-rcnn_r50-caffe-dc5.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
| 183 | 29.666667 | 72 | py |
ERD | ERD-main/configs/faster_rcnn/faster-rcnn_r50_fpn_8xb8-amp-lsj-200e_coco.py | _base_ = [
'../_base_/models/faster-rcnn_r50_fpn.py',
'../common/lsj-200e_coco-detection.py'
]
image_size = (1024, 1024)
batch_augments = [dict(type='BatchFixedSizePad', size=image_size)]
model = dict(data_preprocessor=dict(batch_augments=batch_augments))
train_dataloader = dict(batch_size=8, num_workers=4)
#... | 712 | 32.952381 | 69 | py |
ERD | ERD-main/configs/faster_rcnn/faster-rcnn_r101_fpn_2x_coco.py | _base_ = './faster-rcnn_r50_fpn_2x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 199 | 27.571429 | 61 | py |
ERD | ERD-main/configs/faster_rcnn/faster-rcnn_x101-64x4d_fpn_1x_coco.py | _base_ = './faster-rcnn_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',... | 421 | 27.133333 | 76 | py |
ERD | ERD-main/configs/faster_rcnn/faster-rcnn_r101-caffe_fpn_ms-3x_coco.py | _base_ = 'faster-rcnn_r50_fpn_ms-3x_coco.py'
model = dict(
backbone=dict(
depth=101,
norm_cfg=dict(requires_grad=False),
norm_eval=True,
style='caffe',
init_cfg=dict(
type='Pretrained',
checkpoint='open-mmlab://detectron2/resnet101_caffe')))
| 311 | 25 | 67 | py |
ERD | ERD-main/configs/faster_rcnn/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'
]
| 177 | 28.666667 | 72 | py |
ERD | ERD-main/configs/faster_rcnn/faster-rcnn_r50_fpn_giou_1x_coco.py | _base_ = './faster-rcnn_r50_fpn_1x_coco.py'
model = dict(
roi_head=dict(
bbox_head=dict(
reg_decoded_bbox=True,
loss_bbox=dict(type='GIoULoss', loss_weight=10.0))))
| 201 | 27.857143 | 64 | py |
ERD | ERD-main/configs/faster_rcnn/faster-rcnn_r50_fpn_fcos-rpn_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(
# copied from configs/fcos/fcos_r50-caffe_fpn_gn-head_1x_coco.py
neck=dict(
start_level=1,
add_extra_con... | 1,520 | 30.040816 | 73 | py |
ERD | ERD-main/configs/faster_rcnn/faster-rcnn_r50-caffe-c4_ms-1x_coco.py | _base_ = './faster-rcnn_r50-caffe_c4-1x_coco.py'
train_pipeline = [
dict(type='LoadImageFromFile', backend_args=_base_.backend_args),
dict(type='LoadAnnotations', with_bbox=True),
dict(
type='RandomChoiceResize',
scale=[(1333, 640), (1333, 672), (1333, 704), (1333, 736), (1333, 768),
... | 499 | 32.333333 | 79 | py |
ERD | ERD-main/configs/faster_rcnn/faster-rcnn_r50-caffe_fpn_ms-2x_coco.py | _base_ = './faster-rcnn_r50-caffe_fpn_ms-1x_coco.py'
# MMEngine support the following two ways, users can choose
# according to convenience
# param_scheduler = [
# dict(
# type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500), # noqa
# dict(
# type='MultiStepLR',
# begi... | 505 | 25.631579 | 88 | py |
ERD | ERD-main/configs/faster_rcnn/faster-rcnn_r50-caffe_c4-1x_coco.py | _base_ = [
'../_base_/models/faster-rcnn_r50-caffe-c4.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
| 182 | 29.5 | 72 | py |
ERD | ERD-main/configs/faster_rcnn/faster-rcnn_r50_fpn_2x_coco.py | _base_ = [
'../_base_/models/faster-rcnn_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py'
]
| 177 | 28.666667 | 72 | py |
ERD | ERD-main/configs/faster_rcnn/faster-rcnn_r50-caffe_fpn_ms-1x_coco.py | _base_ = './faster-rcnn_r50_fpn_1x_coco.py'
model = dict(
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),
... | 1,082 | 32.84375 | 79 | py |
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