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
value |
|---|---|---|---|---|---|---|
DSLA-DSLA | DSLA-DSLA/configs/resnest/mask_rcnn_s101_fpn_syncbn-backbone+head_mstrain_1x_coco.py | _base_ = './mask_rcnn_s50_fpn_syncbn-backbone+head_mstrain_1x_coco.py'
model = dict(
backbone=dict(
stem_channels=128,
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='open-mmlab://resnest101')))
| 253 | 30.75 | 70 | py |
DSLA-DSLA | DSLA-DSLA/configs/resnest/cascade_mask_rcnn_s101_fpn_syncbn-backbone+head_mstrain_1x_coco.py | _base_ = './cascade_mask_rcnn_s50_fpn_syncbn-backbone+head_mstrain_1x_coco.py'
model = dict(
backbone=dict(
stem_channels=128,
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='open-mmlab://resnest101')))
| 261 | 31.75 | 78 | py |
DSLA-DSLA | DSLA-DSLA/configs/resnest/cascade_rcnn_s101_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py | _base_ = './cascade_rcnn_s50_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py'
model = dict(
backbone=dict(
stem_channels=128,
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='open-mmlab://resnest101')))
| 262 | 31.875 | 79 | py |
DSLA-DSLA | DSLA-DSLA/configs/resnest/cascade_rcnn_s50_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py | _base_ = '../cascade_rcnn/cascade_rcnn_r50_fpn_1x_coco.py'
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
backbone=dict(
type='ResNeSt',
stem_channels=64,
depth=50,
radix=2,
reduction_factor=4,
avg_down_stride=True,
num_stages=4,
out_... | 4,127 | 34.282051 | 79 | py |
DSLA-DSLA | DSLA-DSLA/configs/groie/faster_rcnn_r50_fpn_groie_1x_coco.py | _base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py'
# model settings
model = dict(
roi_head=dict(
bbox_roi_extractor=dict(
type='GenericRoIExtractor',
aggregation='sum',
roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=2),
out_channels=256,
... | 834 | 31.115385 | 77 | py |
DSLA-DSLA | DSLA-DSLA/configs/groie/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco.py | _base_ = '../gcnet/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py'
# model settings
model = dict(
roi_head=dict(
bbox_roi_extractor=dict(
type='GenericRoIExtractor',
aggregation='sum',
roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=2),
... | 1,551 | 32.73913 | 78 | py |
DSLA-DSLA | DSLA-DSLA/configs/groie/grid_rcnn_r50_fpn_gn-head_groie_1x_coco.py | _base_ = '../grid_rcnn/grid_rcnn_r50_fpn_gn-head_1x_coco.py'
# model settings
model = dict(
roi_head=dict(
bbox_roi_extractor=dict(
type='GenericRoIExtractor',
aggregation='sum',
roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=2),
out_channels=25... | 1,534 | 32.369565 | 78 | py |
DSLA-DSLA | DSLA-DSLA/configs/groie/mask_rcnn_r50_fpn_groie_1x_coco.py | _base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py'
# model settings
model = dict(
roi_head=dict(
bbox_roi_extractor=dict(
type='GenericRoIExtractor',
aggregation='sum',
roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=2),
out_channels=256,
... | 1,526 | 32.195652 | 78 | py |
DSLA-DSLA | DSLA-DSLA/configs/groie/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco.py | _base_ = '../gcnet/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py'
# model settings
model = dict(
roi_head=dict(
bbox_roi_extractor=dict(
type='GenericRoIExtractor',
aggregation='sum',
roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=2),
... | 1,552 | 32.76087 | 78 | py |
DSLA-DSLA | DSLA-DSLA/configs/albu_example/mask_rcnn_r50_fpn_albu_1x_coco.py | _base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py'
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
albu_train_transforms = [
dict(
type='ShiftScaleRotate',
shift_limit=0.0625,
scale_limit=0.0,
rotate_limit=0,
interpolation=... | 2,276 | 29.77027 | 77 | py |
DSLA-DSLA | DSLA-DSLA/configs/fsaf/fsaf_x101_64x4d_fpn_1x_coco.py | _base_ = './fsaf_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',
... | 414 | 26.666667 | 76 | py |
DSLA-DSLA | DSLA-DSLA/configs/fsaf/fsaf_r50_fpn_1x_coco.py | _base_ = '../retinanet/retinanet_r50_fpn_1x_coco.py'
# model settings
model = dict(
type='FSAF',
bbox_head=dict(
type='FSAFHead',
num_classes=80,
in_channels=256,
stacked_convs=4,
feat_channels=256,
reg_decoded_bbox=True,
# Only anchor-free branch is imple... | 1,554 | 30.734694 | 77 | py |
DSLA-DSLA | DSLA-DSLA/configs/fsaf/fsaf_r101_fpn_1x_coco.py | _base_ = './fsaf_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 192 | 26.571429 | 61 | py |
DSLA-DSLA | DSLA-DSLA/configs/grid_rcnn/grid_rcnn_r50_fpn_gn-head_1x_coco.py | _base_ = ['grid_rcnn_r50_fpn_gn-head_2x_coco.py']
# learning policy
lr_config = dict(
policy='step',
warmup='linear',
warmup_iters=500,
warmup_ratio=0.001,
step=[8, 11])
checkpoint_config = dict(interval=1)
# runtime settings
runner = dict(type='EpochBasedRunner', max_epochs=12)
| 300 | 24.083333 | 53 | py |
DSLA-DSLA | DSLA-DSLA/configs/grid_rcnn/grid_rcnn_r101_fpn_gn-head_2x_coco.py | _base_ = './grid_rcnn_r50_fpn_gn-head_2x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 206 | 24.875 | 61 | py |
DSLA-DSLA | DSLA-DSLA/configs/grid_rcnn/grid_rcnn_x101_64x4d_fpn_gn-head_2x_coco.py | _base_ = './grid_rcnn_x101_32x4d_fpn_gn-head_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,
style='pytorch',
init_cfg=dict(
type=... | 380 | 26.214286 | 76 | py |
DSLA-DSLA | DSLA-DSLA/configs/grid_rcnn/grid_rcnn_x101_32x4d_fpn_gn-head_2x_coco.py | _base_ = './grid_rcnn_r50_fpn_gn-head_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,
style='pytorch',
init_cfg=dict(
type='Pretra... | 697 | 26.92 | 76 | py |
DSLA-DSLA | DSLA-DSLA/configs/grid_rcnn/grid_rcnn_r50_fpn_gn-head_2x_coco.py | _base_ = [
'../_base_/datasets/coco_detection.py', '../_base_/default_runtime.py'
]
# model settings
model = dict(
type='GridRCNN',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requir... | 4,315 | 31.69697 | 79 | py |
DSLA-DSLA | DSLA-DSLA/configs/centripetalnet/centripetalnet_hourglass104_mstest_16x6_210e_coco.py | _base_ = [
'../_base_/default_runtime.py', '../_base_/datasets/coco_detection.py'
]
# model settings
model = dict(
type='CornerNet',
backbone=dict(
type='HourglassNet',
downsample_times=5,
num_stacks=2,
stage_channels=[256, 256, 384, 384, 384, 512],
stage_blocks=[2, ... | 3,465 | 31.698113 | 78 | py |
DSLA-DSLA | DSLA-DSLA/configs/_base_/default_runtime.py | checkpoint_config = dict(interval=1)
# yapf:disable
log_config = dict(
interval=50,
hooks=[
dict(type='TextLoggerHook'),
# dict(type='TensorboardLoggerHook')
])
# yapf:enable
custom_hooks = [dict(type='NumClassCheckHook')]
dist_params = dict(backend='nccl')
log_level = 'INFO'
load_from = No... | 368 | 20.705882 | 47 | py |
DSLA-DSLA | DSLA-DSLA/configs/_base_/models/retinanet_r50_fpn.py | # model settings
model = dict(
type='RetinaNet',
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=True),
norm_eval=True,
style='pytorch',
init_cfg=dict(t... | 1,767 | 27.983607 | 79 | py |
DSLA-DSLA | DSLA-DSLA/configs/_base_/models/faster_rcnn_r50_fpn.py | # model settings
model = dict(
type='FasterRCNN',
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=True),
norm_eval=True,
style='pytorch',
init_cfg=dict(... | 3,632 | 32.330275 | 79 | py |
DSLA-DSLA | DSLA-DSLA/configs/_base_/models/cascade_rcnn_r50_fpn.py | # model settings
model = dict(
type='CascadeRCNN',
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=True),
norm_eval=True,
style='pytorch',
init_cfg=dict... | 6,325 | 34.144444 | 79 | py |
DSLA-DSLA | DSLA-DSLA/configs/_base_/models/rpn_r50_caffe_c4.py | # model settings
model = dict(
type='RPN',
backbone=dict(
type='ResNet',
depth=50,
num_stages=3,
strides=(1, 2, 2),
dilations=(1, 1, 1),
out_indices=(2, ),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=False),
norm_eval=True,
... | 1,788 | 29.322034 | 72 | py |
DSLA-DSLA | DSLA-DSLA/configs/_base_/models/cascade_mask_rcnn_r50_fpn.py | # model settings
model = dict(
type='CascadeRCNN',
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=True),
norm_eval=True,
style='pytorch',
init_cfg=dict... | 6,950 | 34.284264 | 79 | py |
DSLA-DSLA | DSLA-DSLA/configs/_base_/models/fast_rcnn_r50_fpn.py | # model settings
model = dict(
type='FastRCNN',
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=True),
norm_eval=True,
style='pytorch',
init_cfg=dict(ty... | 2,060 | 31.714286 | 79 | py |
DSLA-DSLA | DSLA-DSLA/configs/_base_/models/mask_rcnn_r50_fpn.py | # model settings
model = dict(
type='MaskRCNN',
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=True),
norm_eval=True,
style='pytorch',
init_cfg=dict(ty... | 4,054 | 32.512397 | 79 | py |
DSLA-DSLA | DSLA-DSLA/configs/_base_/models/faster_rcnn_r50_caffe_dc5.py | # model settings
norm_cfg = dict(type='BN', requires_grad=False)
model = dict(
type='FasterRCNN',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
strides=(1, 2, 2, 1),
dilations=(1, 1, 1, 2),
out_indices=(3, ),
frozen_stages=1,
norm_cfg=norm_... | 3,479 | 31.830189 | 77 | py |
DSLA-DSLA | DSLA-DSLA/configs/_base_/models/rpn_r50_fpn.py | # model settings
model = dict(
type='RPN',
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=True),
norm_eval=True,
style='pytorch',
init_cfg=dict(type='P... | 1,807 | 29.644068 | 79 | py |
DSLA-DSLA | DSLA-DSLA/configs/_base_/models/ssd300.py | # model settings
input_size = 300
model = dict(
type='SingleStageDetector',
backbone=dict(
type='SSDVGG',
depth=16,
with_last_pool=False,
ceil_mode=True,
out_indices=(3, 4),
out_feature_indices=(22, 34),
init_cfg=dict(
type='Pretrained', checkp... | 1,734 | 29.438596 | 71 | py |
DSLA-DSLA | DSLA-DSLA/configs/_base_/models/faster_rcnn_r50_caffe_c4.py | # model settings
norm_cfg = dict(type='BN', requires_grad=False)
model = dict(
type='FasterRCNN',
backbone=dict(
type='ResNet',
depth=50,
num_stages=3,
strides=(1, 2, 2),
dilations=(1, 1, 1),
out_indices=(2, ),
frozen_stages=1,
norm_cfg=norm_cfg,
... | 3,694 | 31.130435 | 78 | py |
DSLA-DSLA | DSLA-DSLA/configs/_base_/models/mask_rcnn_r50_caffe_c4.py | # model settings
norm_cfg = dict(type='BN', requires_grad=False)
model = dict(
type='MaskRCNN',
backbone=dict(
type='ResNet',
depth=50,
num_stages=3,
strides=(1, 2, 2),
dilations=(1, 1, 1),
out_indices=(2, ),
frozen_stages=1,
norm_cfg=norm_cfg,
... | 4,061 | 31.238095 | 78 | py |
DSLA-DSLA | DSLA-DSLA/configs/_base_/schedules/schedule_20e.py | # optimizer
optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config = dict(
policy='step',
warmup='linear',
warmup_iters=500,
warmup_ratio=0.001,
step=[16, 19])
runner = dict(type='EpochBasedRunner', max_epochs=20)
| 320 | 25.75 | 72 | py |
DSLA-DSLA | DSLA-DSLA/configs/_base_/schedules/schedule_1x.py | # optimizer
optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config = dict(
policy='step',
warmup='linear',
warmup_iters=500,
warmup_ratio=0.001,
step=[8, 11])
runner = dict(type='EpochBasedRunner', max_epochs=12)
| 319 | 25.666667 | 72 | py |
DSLA-DSLA | DSLA-DSLA/configs/_base_/schedules/schedule_2x.py | # optimizer
optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config = dict(
policy='step',
warmup='linear',
warmup_iters=500,
warmup_ratio=0.001,
step=[16, 22])
runner = dict(type='EpochBasedRunner', max_epochs=24)
| 320 | 25.75 | 72 | py |
DSLA-DSLA | DSLA-DSLA/configs/_base_/datasets/deepfashion.py | # dataset settings
dataset_type = 'DeepFashionDataset'
data_root = 'data/DeepFashion/In-shop/'
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),
d... | 1,888 | 33.981481 | 79 | py |
DSLA-DSLA | DSLA-DSLA/configs/_base_/datasets/coco_instance.py | # dataset settings
dataset_type = 'CocoDataset'
data_root = 'data/coco/'
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
dict(type='Resize', img... | 1,737 | 33.76 | 77 | py |
DSLA-DSLA | DSLA-DSLA/configs/_base_/datasets/coco_instance_semantic.py | # dataset settings
dataset_type = 'CocoDataset'
data_root = 'data/coco/'
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, with_seg=True),
... | 1,922 | 33.963636 | 79 | py |
DSLA-DSLA | DSLA-DSLA/configs/_base_/datasets/cityscapes_detection.py | # dataset settings
dataset_type = 'CityscapesDataset'
data_root = 'data/cityscapes/'
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True),
dict(
type='Resize'... | 1,937 | 33 | 79 | py |
DSLA-DSLA | DSLA-DSLA/configs/_base_/datasets/voc0712.py | # dataset settings
dataset_type = 'VOCDataset'
data_root = 'data/VOCdevkit/'
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True),
dict(type='Resize', img_scale=(1000... | 1,916 | 33.232143 | 77 | py |
DSLA-DSLA | DSLA-DSLA/configs/_base_/datasets/lvis_v1_instance.py | # dataset settings
_base_ = 'coco_instance.py'
dataset_type = 'LVISV1Dataset'
data_root = 'data/lvis_v1/'
data = dict(
samples_per_gpu=2,
workers_per_gpu=2,
train=dict(
_delete_=True,
type='ClassBalancedDataset',
oversample_thr=1e-3,
dataset=dict(
type=dataset_typ... | 736 | 28.48 | 66 | py |
DSLA-DSLA | DSLA-DSLA/configs/_base_/datasets/cityscapes_instance.py | # dataset settings
dataset_type = 'CityscapesDataset'
data_root = 'data/cityscapes/'
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
dict(
... | 1,963 | 33.45614 | 79 | py |
DSLA-DSLA | DSLA-DSLA/configs/_base_/datasets/coco_detection.py | # dataset settings
dataset_type = 'CocoDataset'
data_root = 'data/coco/'
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True),
dict(type='Resize', img_scale=(1333, 80... | 1,711 | 33.24 | 77 | py |
DSLA-DSLA | DSLA-DSLA/configs/_base_/datasets/lvis_v0.5_instance.py | # dataset settings
_base_ = 'coco_instance.py'
dataset_type = 'LVISV05Dataset'
data_root = 'data/lvis_v0.5/'
data = dict(
samples_per_gpu=2,
workers_per_gpu=2,
train=dict(
_delete_=True,
type='ClassBalancedDataset',
oversample_thr=1e-3,
dataset=dict(
type=dataset_... | 786 | 30.48 | 68 | py |
DSLA-DSLA | DSLA-DSLA/configs/_base_/datasets/coco_panoptic.py | # dataset settings
dataset_type = 'CocoPanopticDataset'
data_root = 'data/coco/'
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='LoadPanopticAnnotations',
with_bbox=True,
wit... | 2,079 | 33.666667 | 79 | py |
DSLA-DSLA | DSLA-DSLA/configs/_base_/datasets/wider_face.py | # dataset settings
dataset_type = 'WIDERFaceDataset'
data_root = 'data/WIDERFace/'
img_norm_cfg = dict(mean=[123.675, 116.28, 103.53], std=[1, 1, 1], to_rgb=True)
train_pipeline = [
dict(type='LoadImageFromFile', to_float32=True),
dict(type='LoadAnnotations', with_bbox=True),
dict(
type='PhotoMetric... | 2,011 | 30.4375 | 79 | py |
DSLA-DSLA | DSLA-DSLA/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 |
DSLA-DSLA | DSLA-DSLA/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 |
DSLA-DSLA | DSLA-DSLA/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 |
DSLA-DSLA | DSLA-DSLA/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... | 1,590 | 30.196078 | 68 | py |
DSLA-DSLA | DSLA-DSLA/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 |
DSLA-DSLA | DSLA-DSLA/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 |
DSLA-DSLA | DSLA-DSLA/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 |
DSLA-DSLA | DSLA-DSLA/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 |
DSLA-DSLA | DSLA-DSLA/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... | 482 | 33.5 | 70 | py |
DSLA-DSLA | DSLA-DSLA/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, ... | 593 | 33.941176 | 77 | py |
DSLA-DSLA | DSLA-DSLA/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 |
DSLA-DSLA | DSLA-DSLA/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 |
DSLA-DSLA | DSLA-DSLA/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')))
fp16 = dict(loss_scale=dict(init_scale=512))
| 283 | 34.5 | 66 | py |
DSLA-DSLA | DSLA-DSLA/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,
... | 618 | 33.388889 | 77 | py |
DSLA-DSLA | DSLA-DSLA/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... | 515 | 33.4 | 70 | py |
DSLA-DSLA | DSLA-DSLA/configs/autoassign/autoassign_r50_fpn_8x2_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 = dict(
type='AutoAssign',
backbone=dict(
type='ResNet',
depth=50,
... | 2,672 | 30.081395 | 75 | py |
DSLA-DSLA | DSLA-DSLA/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 |
DSLA-DSLA | DSLA-DSLA/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 |
DSLA-DSLA | DSLA-DSLA/configs/retinanet/retinanet_r50_fpn_2x_coco.py | _base_ = './retinanet_r50_fpn_1x_coco.py'
# learning policy
lr_config = dict(step=[16, 22])
runner = dict(type='EpochBasedRunner', max_epochs=24)
| 146 | 28.4 | 53 | py |
DSLA-DSLA | DSLA-DSLA/configs/retinanet/retinanet_r50_fpn_90k_coco.py | _base_ = 'retinanet_r50_fpn_1x_coco.py'
# learning policy
lr_config = dict(
policy='step',
warmup='linear',
warmup_iters=500,
warmup_ratio=0.001,
step=[60000, 80000])
# Runner type
runner = dict(_delete_=True, type='IterBasedRunner', max_iters=90000)
checkpoint_config = dict(interval=10000)
evalu... | 364 | 21.8125 | 69 | py |
DSLA-DSLA | DSLA-DSLA/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 |
DSLA-DSLA | DSLA-DSLA/configs/retinanet/retinanet_r50_caffe_fpn_mstrain_1x_coco.py | _base_ = './retinanet_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
norm_cfg=dict(requires_grad=False),
norm_eval=True,
style='caffe',
init_cfg=dict(
type='Pretrained',
checkpoint='open-mmlab://detectron2/resnet50_caffe')))
# use caffe img_norm
img_norm_cfg... | 1,552 | 32.042553 | 72 | py |
DSLA-DSLA | DSLA-DSLA/configs/retinanet/retinanet_r101_fpn_mstrain_640-800_3x_coco.py | _base_ = [
'../_base_/models/retinanet_r50_fpn.py', '../common/mstrain_3x_coco.py'
]
# optimizer
model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101))
optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
| 251 | 35 | 76 | py |
DSLA-DSLA | DSLA-DSLA/configs/retinanet/retinanet_x101_64x4d_fpn_mstrain_640-800_3x_coco.py | _base_ = [
'../_base_/models/retinanet_r50_fpn.py', '../common/mstrain_3x_coco.py'
]
# optimizer
model = dict(
pretrained='open-mmlab://resnext101_64x4d',
backbone=dict(type='ResNeXt', depth=101, groups=64, base_width=4))
optimizer = dict(type='SGD', lr=0.01)
| 272 | 29.333333 | 75 | py |
DSLA-DSLA | DSLA-DSLA/configs/retinanet/retinanet_r50_caffe_fpn_mstrain_2x_coco.py | _base_ = './retinanet_r50_caffe_fpn_mstrain_1x_coco.py'
# learning policy
lr_config = dict(step=[16, 23])
runner = dict(type='EpochBasedRunner', max_epochs=24)
| 160 | 31.2 | 55 | py |
DSLA-DSLA | DSLA-DSLA/configs/retinanet/retinanet_r50_fpn_mstrain_640-800_3x_coco.py | _base_ = [
'../_base_/models/retinanet_r50_fpn.py', '../common/mstrain_3x_coco.py'
]
# optimizer
optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
| 174 | 28.166667 | 75 | py |
DSLA-DSLA | DSLA-DSLA/configs/retinanet/retinanet_r50_caffe_fpn_1x_coco.py | _base_ = './retinanet_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
norm_cfg=dict(requires_grad=False),
norm_eval=True,
style='caffe',
init_cfg=dict(
type='Pretrained',
checkpoint='open-mmlab://detectron2/resnet50_caffe')))
# use caffe img_norm
img_norm_cfg... | 1,408 | 32.547619 | 72 | py |
DSLA-DSLA | DSLA-DSLA/configs/retinanet/retinanet_r101_caffe_fpn_mstrain_3x_coco.py | _base_ = './retinanet_r50_caffe_fpn_mstrain_1x_coco.py'
# learning policy
model = dict(
pretrained='open-mmlab://detectron2/resnet101_caffe',
backbone=dict(depth=101))
lr_config = dict(step=[28, 34])
runner = dict(type='EpochBasedRunner', max_epochs=36)
| 262 | 31.875 | 57 | py |
DSLA-DSLA | DSLA-DSLA/configs/retinanet/retinanet_r50_fpn_fp16_1x_coco.py | _base_ = './retinanet_r50_fpn_1x_coco.py'
# fp16 settings
fp16 = dict(loss_scale=512.)
| 87 | 21 | 41 | py |
DSLA-DSLA | DSLA-DSLA/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 |
DSLA-DSLA | DSLA-DSLA/configs/retinanet/retinanet_r50_caffe_fpn_mstrain_3x_coco.py | _base_ = './retinanet_r50_caffe_fpn_mstrain_1x_coco.py'
# learning policy
lr_config = dict(step=[28, 34])
runner = dict(type='EpochBasedRunner', max_epochs=36)
| 160 | 31.2 | 55 | py |
DSLA-DSLA | DSLA-DSLA/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 |
DSLA-DSLA | DSLA-DSLA/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 |
DSLA-DSLA | DSLA-DSLA/configs/retinanet/retinanet_r50_fpn_1x_coco.py | _base_ = [
'../_base_/models/retinanet_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
# optimizer
optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
| 260 | 31.625 | 72 | py |
DSLA-DSLA | DSLA-DSLA/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 |
DSLA-DSLA | DSLA-DSLA/configs/free_anchor/retinanet_free_anchor_x101_32x4d_fpn_1x_coco.py | _base_ = './retinanet_free_anchor_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='Pr... | 377 | 26 | 76 | py |
DSLA-DSLA | DSLA-DSLA/configs/free_anchor/retinanet_free_anchor_r101_fpn_1x_coco.py | _base_ = './retinanet_free_anchor_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| 209 | 29 | 61 | py |
DSLA-DSLA | DSLA-DSLA/configs/free_anchor/retinanet_free_anchor_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',
... | 775 | 32.73913 | 74 | py |
DSLA-DSLA | DSLA-DSLA/configs/scratch/faster_rcnn_r50_fpn_gn-all_scratch_6x_coco.py | _base_ = [
'../_base_/models/faster_rcnn_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
model = dict(
backbone=dict(
frozen_stages=-1,
zero_init_resi... | 816 | 31.68 | 72 | py |
DSLA-DSLA | DSLA-DSLA/configs/scratch/mask_rcnn_r50_fpn_gn-all_scratch_6x_coco.py | _base_ = [
'../_base_/models/mask_rcnn_r50_fpn.py',
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
model = dict(
backbone=dict(
frozen_stages=-1,
zero_init_residua... | 856 | 31.961538 | 72 | py |
DSLA-DSLA | DSLA-DSLA/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 |
DSLA-DSLA | DSLA-DSLA/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 |
DSLA-DSLA | DSLA-DSLA/configs/faster_rcnn/faster_rcnn_r101_caffe_fpn_mstrain_3x_coco.py | _base_ = 'faster_rcnn_r50_fpn_mstrain_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')))
# u... | 1,526 | 29.54 | 72 | py |
DSLA-DSLA | DSLA-DSLA/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'
]
# 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 = [
... | 1,304 | 33.342105 | 72 | py |
DSLA-DSLA | DSLA-DSLA/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_1x_coco-person-bicycle-car.py | _base_ = './faster_rcnn_r50_caffe_fpn_mstrain_1x_coco.py'
model = dict(roi_head=dict(bbox_head=dict(num_classes=3)))
classes = ('person', 'bicycle', 'car')
data = dict(
train=dict(classes=classes),
val=dict(classes=classes),
test=dict(classes=classes))
load_from = 'https://download.openmmlab.com/mmdetectio... | 476 | 46.7 | 209 | py |
DSLA-DSLA | DSLA-DSLA/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 |
DSLA-DSLA | DSLA-DSLA/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_1x_coco.py | _base_ = './faster_rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
norm_cfg=dict(requires_grad=False),
norm_eval=True,
style='caffe',
init_cfg=dict(
type='Pretrained',
checkpoint='open-mmlab://detectron2/resnet50_caffe')))
# use caffe img_norm
img_norm_c... | 1,554 | 32.085106 | 72 | py |
DSLA-DSLA | DSLA-DSLA/configs/faster_rcnn/faster_rcnn_r50_caffe_dc5_mstrain_3x_coco.py | _base_ = './faster_rcnn_r50_caffe_dc5_mstrain_1x_coco.py'
# learning policy
lr_config = dict(step=[28, 34])
runner = dict(type='EpochBasedRunner', max_epochs=36)
| 162 | 31.6 | 57 | py |
DSLA-DSLA | DSLA-DSLA/configs/faster_rcnn/faster_rcnn_x101_64x4d_fpn_mstrain_3x_coco.py | _base_ = [
'../common/mstrain_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'... | 468 | 26.588235 | 77 | py |
DSLA-DSLA | DSLA-DSLA/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 |
DSLA-DSLA | DSLA-DSLA/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_90k_coco.py | _base_ = 'faster_rcnn_r50_caffe_fpn_1x_coco.py'
# learning policy
lr_config = dict(
policy='step',
warmup='linear',
warmup_iters=500,
warmup_ratio=0.001,
step=[60000, 80000])
# Runner type
runner = dict(_delete_=True, type='IterBasedRunner', max_iters=90000)
checkpoint_config = dict(interval=1000... | 372 | 22.3125 | 69 | py |
DSLA-DSLA | DSLA-DSLA/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 |
DSLA-DSLA | DSLA-DSLA/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_1x_coco-person.py | _base_ = './faster_rcnn_r50_caffe_fpn_mstrain_1x_coco.py'
model = dict(roi_head=dict(bbox_head=dict(num_classes=1)))
classes = ('person', )
data = dict(
train=dict(classes=classes),
val=dict(classes=classes),
test=dict(classes=classes))
load_from = 'https://download.openmmlab.com/mmdetection/v2.0/faster_rc... | 460 | 45.1 | 209 | py |
DSLA-DSLA | DSLA-DSLA/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 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.