repo stringlengths 1 99 | file stringlengths 13 215 | code stringlengths 12 59.2M | file_length int64 12 59.2M | avg_line_length float64 3.82 1.48M | max_line_length int64 12 2.51M | extension_type stringclasses 1
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Mask-aware-IoU | Mask-aware-IoU-master/tests/test_pisa_heads.py | import mmcv
import torch
from mmdet.models.dense_heads import PISARetinaHead, PISASSDHead
from mmdet.models.roi_heads import PISARoIHead
def test_pisa_retinanet_head_loss():
"""
Tests pisa retinanet head loss when truth is empty and non-empty
"""
s = 256
img_metas = [{
'img_shape': (s, s,... | 8,777 | 33.972112 | 79 | py |
Mask-aware-IoU | Mask-aware-IoU-master/tests/test_losses.py | import pytest
import torch
def test_ce_loss():
from mmdet.models import build_loss
# use_mask and use_sigmoid cannot be true at the same time
with pytest.raises(AssertionError):
loss_cfg = dict(
type='CrossEntropyLoss',
use_mask=True,
use_sigmoid=True,
... | 967 | 29.25 | 78 | py |
Mask-aware-IoU | Mask-aware-IoU-master/tests/test_masks.py | import numpy as np
import pytest
import torch
from mmdet.core import BitmapMasks, PolygonMasks
def dummy_raw_bitmap_masks(size):
"""
Args:
size (tuple): expected shape of dummy masks, (H, W) or (N, H, W)
Return:
ndarray: dummy mask
"""
return np.random.randint(0, 2, size, dtype=n... | 23,708 | 37.995066 | 79 | py |
Mask-aware-IoU | Mask-aware-IoU-master/tests/test_backbone.py | import pytest
import torch
from torch.nn.modules import AvgPool2d, GroupNorm
from torch.nn.modules.batchnorm import _BatchNorm
from mmdet.models.backbones import RegNet, Res2Net, ResNet, ResNetV1d, ResNeXt
from mmdet.models.backbones.hourglass import HourglassNet
from mmdet.models.backbones.res2net import Bottle2neck
... | 28,455 | 33.618005 | 79 | py |
Mask-aware-IoU | Mask-aware-IoU-master/tests/test_assigner.py | """
Tests the Assigner objects.
CommandLine:
pytest tests/test_assigner.py
xdoctest tests/test_assigner.py zero
"""
import torch
from mmdet.core.bbox.assigners import (ApproxMaxIoUAssigner,
CenterRegionAssigner, MaxIoUAssigner,
P... | 12,028 | 28.848635 | 79 | py |
Mask-aware-IoU | Mask-aware-IoU-master/tests/test_fp16.py | import numpy as np
import pytest
import torch
import torch.nn as nn
from mmdet.core import auto_fp16, force_fp32
from mmdet.core.fp16.utils import cast_tensor_type
def test_cast_tensor_type():
inputs = torch.FloatTensor([5.])
src_type = torch.float32
dst_type = torch.int32
outputs = cast_tensor_type(... | 9,713 | 31.165563 | 75 | py |
Mask-aware-IoU | Mask-aware-IoU-master/tests/test_ops/test_merge_cells.py | """
CommandLine:
pytest tests/test_merge_cells.py
"""
import torch
import torch.nn.functional as F
from mmdet.ops.merge_cells import (BaseMergeCell, ConcatCell,
GlobalPoolingCell, SumCell)
def test_sum_cell():
inputs_x = torch.randn([2, 256, 32, 32])
inputs_y = torch.ra... | 2,504 | 36.954545 | 75 | py |
Mask-aware-IoU | Mask-aware-IoU-master/tests/test_ops/test_soft_nms.py | """
CommandLine:
pytest tests/test_soft_nms.py
"""
import numpy as np
import torch
from mmdet.ops.nms.nms_wrapper import soft_nms
def test_soft_nms_device_and_dtypes_cpu():
"""
CommandLine:
xdoctest -m tests/test_soft_nms.py test_soft_nms_device_and_dtypes_cpu
"""
iou_thr = 0.7
base_d... | 1,257 | 28.952381 | 78 | py |
Mask-aware-IoU | Mask-aware-IoU-master/tests/test_ops/test_nms.py | """
CommandLine:
pytest tests/test_nms.py
"""
import numpy as np
import pytest
import torch
from mmdet.ops.nms.nms_wrapper import nms, nms_match
def test_nms_device_and_dtypes_cpu():
"""
CommandLine:
xdoctest -m tests/test_nms.py test_nms_device_and_dtypes_cpu
"""
iou_thr = 0.6
base_d... | 4,220 | 36.026316 | 79 | py |
Mask-aware-IoU | Mask-aware-IoU-master/tests/test_ops/test_wrappers.py | from collections import OrderedDict
from itertools import product
from unittest.mock import patch
import torch
import torch.nn as nn
from mmdet.ops import Conv2d, ConvTranspose2d, Linear, MaxPool2d
torch.__version__ = '1.1' # force test
def test_conv2d():
"""
CommandLine:
xdoctest -m tests/test_wr... | 6,705 | 32.698492 | 79 | py |
Mask-aware-IoU | Mask-aware-IoU-master/tests/test_ops/test_corner_pool.py | """
CommandLine:
pytest tests/test_corner_pool.py
"""
import pytest
import torch
from mmdet.ops import CornerPool
def test_corner_pool_device_and_dtypes_cpu():
"""
CommandLine:
xdoctest -m tests/test_corner_pool.py \
test_corner_pool_device_and_dtypes_cpu
"""
with pytest.raise... | 2,301 | 38.016949 | 69 | py |
Mask-aware-IoU | Mask-aware-IoU-master/demo/webcam_demo.py | import argparse
import cv2
import torch
from mmdet.apis import inference_detector, init_detector
def parse_args():
parser = argparse.ArgumentParser(description='MMDetection webcam demo')
parser.add_argument('config', help='test config file path')
parser.add_argument('checkpoint', help='checkpoint file')... | 1,260 | 25.829787 | 78 | py |
Mask-aware-IoU | Mask-aware-IoU-master/configs/ghm/retinanet_ghm_x101_32x4d_fpn_1x_coco.py | _base_ = './retinanet_ghm_r50_fpn_1x_coco.py'
model = dict(
pretrained='open-mmlab://resnext101_32x4d',
backbone=dict(
type='ResNeXt',
depth=101,
groups=32,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='... | 372 | 25.642857 | 53 | py |
Mask-aware-IoU | Mask-aware-IoU-master/configs/ghm/retinanet_ghm_r101_fpn_1x_coco.py | _base_ = './retinanet_ghm_r50_fpn_1x_coco.py'
model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101))
| 123 | 40.333333 | 76 | py |
Mask-aware-IoU | Mask-aware-IoU-master/configs/ghm/retinanet_ghm_x101_64x4d_fpn_1x_coco.py | _base_ = './retinanet_ghm_r50_fpn_1x_coco.py'
model = dict(
pretrained='open-mmlab://resnext101_64x4d',
backbone=dict(
type='ResNeXt',
depth=101,
groups=64,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='... | 372 | 25.642857 | 53 | py |
Mask-aware-IoU | Mask-aware-IoU-master/configs/dcn/faster_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco.py | _base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py'
model = dict(
pretrained='open-mmlab://resnext101_32x4d',
backbone=dict(
type='ResNeXt',
depth=101,
groups=32,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=... | 510 | 30.9375 | 76 | py |
Mask-aware-IoU | Mask-aware-IoU-master/configs/htc/htc_x101_64x4d_fpn_16x1_20e_coco.py | _base_ = './htc_r50_fpn_1x_coco.py'
model = dict(
pretrained='open-mmlab://resnext101_64x4d',
backbone=dict(
type='ResNeXt',
depth=101,
groups=64,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requi... | 504 | 25.578947 | 53 | py |
Mask-aware-IoU | Mask-aware-IoU-master/configs/htc/htc_without_semantic_r50_fpn_1x_coco.py | _base_ = [
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
# model settings
model = dict(
type='HybridTaskCascade',
pretrained='torchvision://resnet50',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
... | 7,989 | 32.153527 | 79 | py |
Mask-aware-IoU | Mask-aware-IoU-master/configs/htc/htc_x101_32x4d_fpn_16x1_20e_coco.py | _base_ = './htc_r50_fpn_1x_coco.py'
model = dict(
pretrained='open-mmlab://resnext101_32x4d',
backbone=dict(
type='ResNeXt',
depth=101,
groups=32,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requi... | 504 | 25.578947 | 53 | py |
Mask-aware-IoU | Mask-aware-IoU-master/configs/htc/htc_x101_64x4d_fpn_dconv_c3-c5_mstrain_400_1400_16x1_20e_coco.py | _base_ = './htc_r50_fpn_1x_coco.py'
model = dict(
pretrained='open-mmlab://resnext101_64x4d',
backbone=dict(
type='ResNeXt',
depth=101,
groups=64,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requi... | 1,406 | 31.72093 | 79 | py |
Mask-aware-IoU | Mask-aware-IoU-master/configs/htc/htc_r101_fpn_20e_coco.py | _base_ = './htc_r50_fpn_1x_coco.py'
model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101))
# learning policy
lr_config = dict(step=[16, 19])
total_epochs = 20
| 181 | 29.333333 | 76 | py |
Mask-aware-IoU | Mask-aware-IoU-master/configs/reppoints/reppoints_moment_r101_fpn_dconv_c3-c5_gn-neck+head_2x_coco.py | _base_ = './reppoints_moment_r50_fpn_gn-neck+head_2x_coco.py'
model = dict(
pretrained='torchvision://resnet101',
backbone=dict(
depth=101,
dcn=dict(type='DCN', deformable_groups=1, fallback_on_stride=False),
stage_with_dcn=(False, True, True, True)))
| 284 | 34.625 | 76 | py |
Mask-aware-IoU | Mask-aware-IoU-master/configs/reppoints/reppoints_moment_r101_fpn_gn-neck+head_2x_coco.py | _base_ = './reppoints_moment_r50_fpn_gn-neck+head_2x_coco.py'
model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101))
| 139 | 45.666667 | 76 | py |
Mask-aware-IoU | Mask-aware-IoU-master/configs/reppoints/reppoints_moment_r50_fpn_1x_coco.py | _base_ = [
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
type='RepPointsDetector',
pretrained='torchvision://resnet50',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0,... | 1,931 | 27.411765 | 79 | py |
Mask-aware-IoU | Mask-aware-IoU-master/configs/reppoints/reppoints_moment_x101_fpn_dconv_c3-c5_gn-neck+head_2x_coco.py | _base_ = './reppoints_moment_r50_fpn_gn-neck+head_2x_coco.py'
model = dict(
pretrained='open-mmlab://resnext101_32x4d',
backbone=dict(
type='ResNeXt',
depth=101,
groups=32,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm... | 515 | 31.25 | 76 | py |
Mask-aware-IoU | Mask-aware-IoU-master/configs/nas_fpn/retinanet_r50_fpn_crop640_50e_coco.py | _base_ = [
'../_base_/models/retinanet_r50_fpn.py',
'../_base_/datasets/coco_detection.py', '../_base_/default_runtime.py'
]
cudnn_benchmark = True
norm_cfg = dict(type='BN', requires_grad=True)
model = dict(
pretrained='torchvision://resnet50',
backbone=dict(
type='ResNet',
depth=50,
... | 2,407 | 28.728395 | 77 | py |
Mask-aware-IoU | Mask-aware-IoU-master/configs/nas_fpn/retinanet_r50_nasfpn_crop640_50e_coco.py | _base_ = [
'../_base_/models/retinanet_r50_fpn.py',
'../_base_/datasets/coco_detection.py', '../_base_/default_runtime.py'
]
cudnn_benchmark = True
# model settings
norm_cfg = dict(type='BN', requires_grad=True)
model = dict(
type='RetinaNet',
pretrained='torchvision://resnet50',
backbone=dict(
... | 2,397 | 28.975 | 77 | py |
Mask-aware-IoU | Mask-aware-IoU-master/configs/yolact/yolact_r50_1x8_coco.py | _base_ = '../_base_/default_runtime.py'
# model settings
img_size = 550
model = dict(
type='YOLACT',
pretrained='torchvision://resnet50',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=-1, # do not freeze stem
n... | 4,946 | 29.91875 | 77 | py |
Mask-aware-IoU | Mask-aware-IoU-master/configs/yolact/yolact_r101_1x8_coco.py | _base_ = './yolact_r50_1x8_coco.py'
model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) | 113 | 37 | 76 | py |
Mask-aware-IoU | Mask-aware-IoU-master/configs/point_rend/point_rend_r50_caffe_fpn_mstrain_1x_coco.py | _base_ = '../mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain_1x_coco.py'
# model settings
model = dict(
type='PointRend',
roi_head=dict(
type='PointRendRoIHead',
mask_roi_extractor=dict(
type='GenericRoIExtractor',
aggregation='concat',
roi_layer=dict(_delete_=True,... | 1,371 | 31.666667 | 78 | py |
Mask-aware-IoU | Mask-aware-IoU-master/configs/point_rend/point_rend_r50_caffe_fpn_mstrain_3x_coco.py | _base_ = './point_rend_r50_caffe_fpn_mstrain_1x_coco.py'
# learning policy
lr_config = dict(step=[28, 34])
total_epochs = 36
| 125 | 24.2 | 56 | py |
Mask-aware-IoU | Mask-aware-IoU-master/configs/fcos/fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_4x4_1x_coco.py | _base_ = 'fcos_r50_caffe_fpn_gn-head_4x4_1x_coco.py'
model = dict(
pretrained='open-mmlab://resnet50_caffe_bgr',
bbox_head=dict(
norm_on_bbox=True,
centerness_on_reg=True,
dcn_on_last_conv=False,
center_sampling=True,
conv_bias=True,
loss_bbox=dict(type='GIoULoss... | 1,681 | 31.346154 | 72 | py |
Mask-aware-IoU | Mask-aware-IoU-master/configs/fcos/fcos_r101_caffe_fpn_gn-head_4x4_1x_coco.py | _base_ = './fcos_r50_caffe_fpn_gn-head_4x4_1x_coco.py'
model = dict(
pretrained='open-mmlab://detectron/resnet101_caffe',
backbone=dict(depth=101))
| 156 | 30.4 | 56 | py |
Mask-aware-IoU | Mask-aware-IoU-master/configs/fcos/fcos_r101_caffe_fpn_gn-head_mstrain_640-800_4x4_2x_coco.py | _base_ = './fcos_r50_caffe_fpn_gn-head_4x4_1x_coco.py'
model = dict(
pretrained='open-mmlab://detectron/resnet101_caffe',
backbone=dict(depth=101))
img_norm_cfg = dict(
mean=[102.9801, 115.9465, 122.7717], std=[1.0, 1.0, 1.0], to_rgb=False)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(typ... | 1,446 | 31.155556 | 75 | py |
Mask-aware-IoU | Mask-aware-IoU-master/configs/fcos/fcos_x101_64x4d_fpn_gn-head_mstrain_640-800_4x2_2x_coco.py | _base_ = './fcos_r50_caffe_fpn_gn-head_4x4_1x_coco.py'
model = dict(
pretrained='open-mmlab://resnext101_64x4d',
backbone=dict(
type='ResNeXt',
depth=101,
groups=64,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=di... | 1,883 | 30.4 | 77 | py |
Mask-aware-IoU | Mask-aware-IoU-master/configs/fcos/fcos_center_r50_caffe_fpn_gn-head_4x4_1x_coco.py | _base_ = './fcos_r50_caffe_fpn_gn-head_4x4_1x_coco.py'
model = dict(bbox_head=dict(center_sampling=True, center_sample_radius=1.5))
| 132 | 43.333333 | 76 | py |
Mask-aware-IoU | Mask-aware-IoU-master/configs/fcos/fcos_r101_caffe_fpn_gn-head_4x4_2x_coco.py | _base_ = ['./fcos_r50_caffe_fpn_gn-head_4x4_2x_coco.py']
model = dict(
pretrained='open-mmlab://detectron/resnet101_caffe',
backbone=dict(depth=101))
| 158 | 30.8 | 56 | py |
Mask-aware-IoU | Mask-aware-IoU-master/configs/fcos/fcos_r50_caffe_fpn_gn-head_mstrain_640-800_4x4_2x_coco.py | _base_ = './fcos_r50_caffe_fpn_gn-head_4x4_1x_coco.py'
img_norm_cfg = dict(
mean=[102.9801, 115.9465, 122.7717], std=[1.0, 1.0, 1.0], to_rgb=False)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True),
dict(
type='Resize',
img_scale=[(1333, 640)... | 1,299 | 31.5 | 75 | py |
Mask-aware-IoU | Mask-aware-IoU-master/configs/fcos/fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_dcn_4x4_1x_coco.py | _base_ = 'fcos_r50_caffe_fpn_gn-head_4x4_1x_coco.py'
model = dict(
pretrained='open-mmlab://resnet50_caffe_bgr',
backbone=dict(
dcn=dict(type='DCNv2', deformable_groups=1, fallback_on_stride=False),
stage_with_dcn=(False, True, True, True)),
bbox_head=dict(
norm_on_bbox=True,
... | 1,829 | 32.272727 | 78 | py |
Mask-aware-IoU | Mask-aware-IoU-master/configs/fcos/fcos_r50_caffe_fpn_gn-head_4x4_1x_coco.py | _base_ = [
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
# model settings
model = dict(
type='FCOS',
pretrained='open-mmlab://detectron/resnet50_caffe',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
... | 3,140 | 28.632075 | 75 | py |
Mask-aware-IoU | Mask-aware-IoU-master/configs/fcos/fcos_r50_caffe_fpn_gn-head_4x4_2x_coco.py | _base_ = './fcos_r50_caffe_fpn_gn-head_4x4_1x_coco.py'
# learning policy
lr_config = dict(step=[16, 22])
total_epochs = 24
| 124 | 19.833333 | 54 | py |
Mask-aware-IoU | Mask-aware-IoU-master/configs/fcos/fcos_r50_caffe_fpn_4x4_1x_coco.py | _base_ = [
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
# model settings
model = dict(
type='FCOS',
pretrained='open-mmlab://detectron/resnet50_caffe',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
... | 3,163 | 28.570093 | 75 | py |
Mask-aware-IoU | Mask-aware-IoU-master/configs/legacy_1.x/faster_rcnn_r50_fpn_1x_coco_v1.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(
type='FasterRCNN',
pretrained='torchvision://resnet50',
rpn_head=dict(
type='RPNHead',
anchor_genera... | 1,268 | 35.257143 | 78 | py |
Mask-aware-IoU | Mask-aware-IoU-master/configs/legacy_1.x/cascade_mask_rcnn_r50_fpn_1x_coco_v1.py | _base_ = [
'../_base_/models/cascade_mask_rcnn_r50_fpn.py',
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
type='CascadeRCNN',
pretrained='torchvision://resnet50',
backbone=dict(
type='ResNet',
depth=50,... | 2,643 | 34.72973 | 79 | py |
Mask-aware-IoU | Mask-aware-IoU-master/configs/legacy_1.x/retinanet_r50_caffe_fpn_1x_coco_v1.py | _base_ = './retinanet_r50_fpn_1x_coco_v1.py'
model = dict(
pretrained='open-mmlab://detectron/resnet50_caffe',
backbone=dict(
norm_cfg=dict(requires_grad=False), norm_eval=True, style='caffe'))
# use caffe img_norm
img_norm_cfg = dict(
mean=[102.9801, 115.9465, 122.7717], std=[1.0, 1.0, 1.0], to_rgb... | 1,334 | 34.131579 | 75 | py |
Mask-aware-IoU | Mask-aware-IoU-master/configs/ms_rcnn/ms_rcnn_r50_caffe_fpn_1x_coco.py | _base_ = '../mask_rcnn/mask_rcnn_r50_caffe_fpn_1x_coco.py'
model = dict(
type='MaskScoringRCNN',
roi_head=dict(
type='MaskScoringRoIHead',
mask_iou_head=dict(
type='MaskIoUHead',
num_convs=4,
num_fcs=2,
roi_feat_size=14,
in_channels=256... | 508 | 28.941176 | 58 | py |
Mask-aware-IoU | Mask-aware-IoU-master/configs/ms_rcnn/ms_rcnn_x101_64x4d_fpn_1x_coco.py | _base_ = './ms_rcnn_r50_fpn_1x_coco.py'
model = dict(
pretrained='open-mmlab://resnext101_64x4d',
backbone=dict(
type='ResNeXt',
depth=101,
groups=64,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', r... | 366 | 25.214286 | 53 | py |
Mask-aware-IoU | Mask-aware-IoU-master/configs/ms_rcnn/ms_rcnn_r50_caffe_fpn_2x_coco.py | _base_ = './ms_rcnn_r50_caffe_fpn_1x_coco.py'
# learning policy
lr_config = dict(step=[16, 22])
total_epochs = 24
| 114 | 22 | 45 | py |
Mask-aware-IoU | Mask-aware-IoU-master/configs/ms_rcnn/ms_rcnn_x101_32x4d_fpn_1x_coco.py | _base_ = './ms_rcnn_r50_fpn_1x_coco.py'
model = dict(
pretrained='open-mmlab://resnext101_32x4d',
backbone=dict(
type='ResNeXt',
depth=101,
groups=32,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', r... | 366 | 25.214286 | 53 | py |
Mask-aware-IoU | Mask-aware-IoU-master/configs/ms_rcnn/ms_rcnn_r101_caffe_fpn_2x_coco.py | _base_ = './ms_rcnn_r101_caffe_fpn_1x_coco.py'
# learning policy
lr_config = dict(step=[16, 22])
total_epochs = 24
| 115 | 22.2 | 46 | py |
Mask-aware-IoU | Mask-aware-IoU-master/configs/ms_rcnn/ms_rcnn_r101_caffe_fpn_1x_coco.py | _base_ = './ms_rcnn_r50_caffe_fpn_1x_coco.py'
model = dict(
pretrained='open-mmlab://detectron2/resnet101_caffe',
backbone=dict(depth=101))
| 148 | 28.8 | 57 | py |
Mask-aware-IoU | Mask-aware-IoU-master/configs/fast_rcnn/fast_rcnn_r101_fpn_2x_coco.py | _base_ = './fast_rcnn_r50_fpn_2x_coco.py'
model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101))
| 119 | 39 | 76 | py |
Mask-aware-IoU | Mask-aware-IoU-master/configs/fast_rcnn/fast_rcnn_r101_fpn_1x_coco.py | _base_ = './fast_rcnn_r50_fpn_1x_coco.py'
model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101))
| 119 | 39 | 76 | py |
Mask-aware-IoU | Mask-aware-IoU-master/configs/fast_rcnn/fast_rcnn_r101_caffe_fpn_1x_coco.py | _base_ = './fast_rcnn_r50_caffe_fpn_1x_coco.py'
model = dict(
pretrained='open-mmlab://detectron2/resnet101_caffe',
backbone=dict(depth=101))
| 150 | 29.2 | 57 | py |
Mask-aware-IoU | Mask-aware-IoU-master/configs/fast_rcnn/fast_rcnn_r50_caffe_fpn_1x_coco.py | _base_ = './fast_rcnn_r50_fpn_1x_coco.py'
model = dict(
pretrained='open-mmlab://detectron2/resnet50_caffe',
backbone=dict(
norm_cfg=dict(type='BN', requires_grad=False), style='caffe'))
# use caffe img_norm
img_norm_cfg = dict(
mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False)
... | 1,639 | 34.652174 | 78 | py |
Mask-aware-IoU | Mask-aware-IoU-master/configs/hrnet/fcos_hrnetv2p_w32_gn-head_4x4_1x_coco.py | _base_ = '../fcos/fcos_r50_caffe_fpn_gn-head_4x4_1x_coco.py'
model = dict(
pretrained='open-mmlab://msra/hrnetv2_w32',
backbone=dict(
_delete_=True,
type='HRNet',
extra=dict(
stage1=dict(
num_modules=1,
num_branches=1,
block='BO... | 1,176 | 29.179487 | 60 | py |
Mask-aware-IoU | Mask-aware-IoU-master/configs/foveabox/fovea_r50_fpn_4x4_100e_minicoco500.py | _base_ = [
'../_base_/datasets/minicoco500_detection_augm.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
# model settings
model = dict(
type='FOVEA',
pretrained='torchvision://resnet50',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
... | 1,575 | 26.649123 | 78 | py |
Mask-aware-IoU | Mask-aware-IoU-master/configs/foveabox/fovea_r50_fpn_4x4_100e_minicoco500_lrsched2.py | _base_ = [
'../_base_/datasets/minicoco500_detection_augm.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
# model settings
model = dict(
type='FOVEA',
pretrained='torchvision://resnet50',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
... | 1,575 | 26.649123 | 78 | py |
Mask-aware-IoU | Mask-aware-IoU-master/configs/foveabox/fovea_r50_fpn_4x4_100e_coco500_60_80.py | _base_ = [
'../_base_/datasets/coco500_detection_augm.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
# model settings
model = dict(
type='FOVEA',
pretrained='torchvision://resnet50',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
ou... | 1,571 | 26.578947 | 78 | py |
Mask-aware-IoU | Mask-aware-IoU-master/configs/foveabox/fovea_r50_fpn_4x4_1x_coco.py | _base_ = [
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
# model settings
model = dict(
type='FOVEA',
pretrained='torchvision://resnet50',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indice... | 1,542 | 28.113208 | 78 | py |
Mask-aware-IoU | Mask-aware-IoU-master/configs/foveabox/fovea_align_r50_fpn_gn-head_4x4_100e_coco500.py | _base_ = [
'../_base_/datasets/coco500_detection_augm.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
# model settings
model = dict(
type='FOVEA',
pretrained='torchvision://resnet50',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
ou... | 1,639 | 27.275862 | 78 | py |
Mask-aware-IoU | Mask-aware-IoU-master/configs/foveabox/fovea_r101_fpn_4x4_2x_coco.py | _base_ = './fovea_r50_fpn_4x4_2x_coco.py'
model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101))
| 119 | 39 | 76 | py |
Mask-aware-IoU | Mask-aware-IoU-master/configs/foveabox/fovea_r50_fpn_4x4_100e_coco500.py | _base_ = [
'../_base_/datasets/coco500_detection_augm.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
# model settings
model = dict(
type='FOVEA',
pretrained='torchvision://resnet50',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
ou... | 1,571 | 26.578947 | 78 | py |
Mask-aware-IoU | Mask-aware-IoU-master/configs/foveabox/fovea_align_r50_fpn_gn-head_4x4_100e_coco800.py | _base_ = [
'../_base_/datasets/coco800_detection_augm.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
# model settings
model = dict(
type='FOVEA',
pretrained='torchvision://resnet50',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
ou... | 1,639 | 27.275862 | 78 | py |
Mask-aware-IoU | Mask-aware-IoU-master/configs/foveabox/fovea_align_r101_fpn_gn-head_mstrain_640-800_4x4_2x_coco.py | _base_ = './fovea_r50_fpn_4x4_1x_coco.py'
model = dict(
pretrained='torchvision://resnet101',
backbone=dict(depth=101),
bbox_head=dict(
with_deform=True,
norm_cfg=dict(type='GN', num_groups=32, requires_grad=True)))
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12,... | 937 | 32.5 | 77 | py |
Mask-aware-IoU | Mask-aware-IoU-master/configs/foveabox/fovea_r50_fpn_4x4_1x_minicoco500.py | _base_ = [
'../_base_/datasets/minicoco500_detection_augm.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
# model settings
model = dict(
type='FOVEA',
pretrained='torchvision://resnet50',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
... | 1,505 | 27.415094 | 78 | py |
Mask-aware-IoU | Mask-aware-IoU-master/configs/foveabox/fovea_r101_fpn_4x4_1x_coco.py | _base_ = './fovea_r50_fpn_4x4_1x_coco.py'
model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101))
| 119 | 39 | 76 | py |
Mask-aware-IoU | Mask-aware-IoU-master/configs/foveabox/fovea_align_r101_fpn_gn-head_4x4_2x_coco.py | _base_ = './fovea_r50_fpn_4x4_1x_coco.py'
model = dict(
pretrained='torchvision://resnet101',
backbone=dict(depth=101),
bbox_head=dict(
with_deform=True,
norm_cfg=dict(type='GN', num_groups=32, requires_grad=True)))
# learning policy
lr_config = dict(step=[16, 22])
total_epochs = 24
| 312 | 27.454545 | 69 | py |
Mask-aware-IoU | Mask-aware-IoU-master/configs/regnet/mask_rcnn_regnetx-3GF_fpn_mstrain_3x_coco.py | _base_ = [
'../_base_/models/mask_rcnn_r50_fpn.py',
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
pretrained='open-mmlab://regnetx_3.2gf',
backbone=dict(
_delete_=True,
type='RegNet',
arch='regnetx_... | 2,261 | 32.761194 | 77 | py |
Mask-aware-IoU | Mask-aware-IoU-master/configs/regnet/mask_rcnn_regnetx-8GF_fpn_1x_coco.py | _base_ = './mask_rcnn_regnetx-3GF_fpn_1x_coco.py'
model = dict(
pretrained='open-mmlab://regnetx_8.0gf',
backbone=dict(
type='RegNet',
arch='regnetx_8.0gf',
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
... | 468 | 26.588235 | 53 | py |
Mask-aware-IoU | Mask-aware-IoU-master/configs/regnet/retinanet_r50_regnetx-3GF_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(
pretrained='open-mmlab://regnetx_3.2gf',
backbone=dict(
_delete_=True,
type='RegNet',
arch='regnetx... | 1,969 | 32.389831 | 73 | py |
Mask-aware-IoU | Mask-aware-IoU-master/configs/regnet/faster_rcnn_regnetx-3GF_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'
]
model = dict(
pretrained='open-mmlab://regnetx_3.2gf',
backbone=dict(
_delete_=True,
type='RegNet',
arch='regne... | 1,885 | 32.087719 | 73 | py |
Mask-aware-IoU | Mask-aware-IoU-master/configs/regnet/mask_rcnn_regnetx-3GF_fpn_1x_coco.py | _base_ = [
'../_base_/models/mask_rcnn_r50_fpn.py',
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
pretrained='open-mmlab://regnetx_3.2gf',
backbone=dict(
_delete_=True,
type='RegNet',
arch='regnetx_... | 1,981 | 33.172414 | 77 | py |
Mask-aware-IoU | Mask-aware-IoU-master/configs/regnet/mask_rcnn_regnetx-12GF_fpn_1x_coco.py | _base_ = './mask_rcnn_regnetx-3GF_fpn_1x_coco.py'
model = dict(
pretrained='open-mmlab://regnetx_12gf',
backbone=dict(
type='RegNet',
arch='regnetx_12gf',
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
... | 467 | 26.529412 | 53 | py |
Mask-aware-IoU | Mask-aware-IoU-master/configs/regnet/faster_rcnn_regnetx-3GF_fpn_1x_coco.py | _base_ = [
'../_base_/models/faster_rcnn_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
pretrained='open-mmlab://regnetx_3.2gf',
backbone=dict(
_delete_=True,
type='RegNet',
arch='regne... | 1,885 | 32.087719 | 73 | py |
Mask-aware-IoU | Mask-aware-IoU-master/configs/regnet/mask_rcnn_regnetx-4GF_fpn_1x_coco.py | _base_ = './mask_rcnn_regnetx-3GF_fpn_1x_coco.py'
model = dict(
pretrained='open-mmlab://regnetx_4.0gf',
backbone=dict(
type='RegNet',
arch='regnetx_4.0gf',
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
... | 468 | 26.588235 | 53 | py |
Mask-aware-IoU | Mask-aware-IoU-master/configs/regnet/retinanet_r50_regnetx-800MF_fpn_1x_coco.py | _base_ = './retinanet_r50_regnetx-3GF_fpn_1x_coco.py'
model = dict(
pretrained='open-mmlab://regnetx_800mf',
backbone=dict(
type='RegNet',
arch='regnetx_800mf',
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True... | 471 | 26.764706 | 53 | py |
Mask-aware-IoU | Mask-aware-IoU-master/configs/regnet/retinanet_r50_regnetx-1GF_fpn_1x_coco.py | _base_ = './retinanet_r50_regnetx-3GF_fpn_1x_coco.py'
model = dict(
pretrained='open-mmlab://regnetx_1.6gf',
backbone=dict(
type='RegNet',
arch='regnetx_1.6gf',
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True... | 471 | 26.764706 | 53 | py |
Mask-aware-IoU | Mask-aware-IoU-master/configs/regnet/mask_rcnn_regnetx-6GF_fpn_1x_coco.py | _base_ = './mask_rcnn_regnetx-3GF_fpn_1x_coco.py'
model = dict(
pretrained='open-mmlab://regnetx_6.4gf',
backbone=dict(
type='RegNet',
arch='regnetx_6.4gf',
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
... | 469 | 26.647059 | 53 | py |
Mask-aware-IoU | Mask-aware-IoU-master/configs/regnet/mask_rcnn_regnetx-3GF_fpn_mdconv_c3-c5_1x_coco.py | _base_ = [
'../_base_/models/mask_rcnn_r50_fpn.py',
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
pretrained='open-mmlab://regnetx_3.2gf',
backbone=dict(
_delete_=True,
type='RegNet',
arch='regnetx_... | 2,110 | 34.183333 | 78 | py |
Mask-aware-IoU | Mask-aware-IoU-master/configs/regnet/faster_rcnn_regnetx-3GF_fpn_mstrain_3x_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(
pretrained='open-mmlab://regnetx_3.2gf',
backbone=dict(
_delete_=True,
type='RegNet',
arch='regne... | 2,079 | 31.5 | 73 | py |
Mask-aware-IoU | Mask-aware-IoU-master/configs/mayolact/yolact_r50_4x8_coco_scale550_ATSSwmaIoU.py | _base_ = '../_base_/default_runtime.py'
# model settings
img_size = 550
model = dict(
type='YOLACT',
pretrained='torchvision://resnet50',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=-1, # do not freeze stem
n... | 4,691 | 29.467532 | 77 | py |
Mask-aware-IoU | Mask-aware-IoU-master/configs/mayolact/yolact_r50_4x8_coco_scale400.py | _base_ = '../_base_/default_runtime.py'
# model settings
img_size = 400
model = dict(
type='YOLACT',
pretrained='torchvision://resnet50',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=-1, # do not freeze stem
n... | 4,999 | 30.055901 | 77 | py |
Mask-aware-IoU | Mask-aware-IoU-master/configs/mayolact/yolact_r50_4x8_coco_scale700_ATSSwIoU.py | _base_ = '../_base_/default_runtime.py'
# model settings
img_size = 700
model = dict(
type='YOLACT',
pretrained='torchvision://resnet50',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=-1, # do not freeze stem
n... | 4,686 | 29.435065 | 77 | py |
Mask-aware-IoU | Mask-aware-IoU-master/configs/mayolact/yolact_r50_4x8_coco_scale550_ATSSwIoU.py | _base_ = '../_base_/default_runtime.py'
# model settings
img_size = 550
model = dict(
type='YOLACT',
pretrained='torchvision://resnet50',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=-1, # do not freeze stem
n... | 4,688 | 29.448052 | 77 | py |
Mask-aware-IoU | Mask-aware-IoU-master/configs/mayolact/yolact_r50_4x8_coco_scale700_ATSSwmaIoU.py | _base_ = '../_base_/default_runtime.py'
# model settings
img_size = 700
model = dict(
type='YOLACT',
pretrained='torchvision://resnet50',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=-1, # do not freeze stem
n... | 4,691 | 29.467532 | 77 | py |
Mask-aware-IoU | Mask-aware-IoU-master/configs/mayolact/yolact_r50_4x8_coco_scale400_ATSSwmaIoU.py | _base_ = '../_base_/default_runtime.py'
# model settings
img_size = 400
model = dict(
type='YOLACT',
pretrained='torchvision://resnet50',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=-1, # do not freeze stem
n... | 4,691 | 29.467532 | 77 | py |
Mask-aware-IoU | Mask-aware-IoU-master/configs/mayolact/yolact_r50_4x8_coco_scale700.py | _base_ = '../_base_/default_runtime.py'
# model settings
img_size = 700
model = dict(
type='YOLACT',
pretrained='torchvision://resnet50',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=-1, # do not freeze stem
n... | 4,999 | 30.055901 | 77 | py |
Mask-aware-IoU | Mask-aware-IoU-master/configs/mayolact/yolact_r50_4x8_coco_scale550.py | _base_ = '../_base_/default_runtime.py'
# model settings
img_size = 550
model = dict(
type='YOLACT',
pretrained='torchvision://resnet50',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=-1, # do not freeze stem
n... | 4,987 | 29.981366 | 77 | py |
Mask-aware-IoU | Mask-aware-IoU-master/configs/mayolact/yolact_r50_4x8_coco_scale400_ATSSwIoU.py | _base_ = '../_base_/default_runtime.py'
# model settings
img_size = 400
model = dict(
type='YOLACT',
pretrained='torchvision://resnet50',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=-1, # do not freeze stem
n... | 4,686 | 29.435065 | 77 | py |
Mask-aware-IoU | Mask-aware-IoU-master/configs/fsaf/fsaf_x101_64x4d_fpn_1x_coco.py | _base_ = './fsaf_r50_fpn_1x_coco.py'
model = dict(
pretrained='open-mmlab://resnext101_64x4d',
backbone=dict(
type='ResNeXt',
depth=101,
groups=64,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requ... | 363 | 25 | 53 | py |
Mask-aware-IoU | Mask-aware-IoU-master/configs/fsaf/fsaf_r101_fpn_1x_coco.py | _base_ = './fsaf_r50_fpn_1x_coco.py'
model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101))
| 114 | 37.333333 | 76 | py |
Mask-aware-IoU | Mask-aware-IoU-master/configs/grid_rcnn/grid_rcnn_r101_fpn_gn-head_2x_coco.py | _base_ = './grid_rcnn_r50_fpn_gn-head_2x_coco.py'
model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101))
| 128 | 31.25 | 76 | py |
Mask-aware-IoU | Mask-aware-IoU-master/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(
pretrained='open-mmlab://resnext101_64x4d',
backbone=dict(
type='ResNeXt',
depth=101,
groups=64,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='py... | 329 | 24.384615 | 56 | py |
Mask-aware-IoU | Mask-aware-IoU-master/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(
pretrained='open-mmlab://resnext101_32x4d',
backbone=dict(
type='ResNeXt',
depth=101,
groups=32,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='pytorch')... | 610 | 24.458333 | 72 | py |
Mask-aware-IoU | Mask-aware-IoU-master/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',
pretrained='torchvision://resnet50',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1... | 4,055 | 29.268657 | 78 | py |
Mask-aware-IoU | Mask-aware-IoU-master/configs/_base_/models/retinanet_r50_fpn.py | # model settings
model = dict(
type='RetinaNet',
pretrained='torchvision://resnet50',
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,
... | 1,651 | 26.081967 | 56 | py |
Mask-aware-IoU | Mask-aware-IoU-master/configs/_base_/models/faster_rcnn_r50_fpn.py | model = dict(
type='FasterRCNN',
pretrained='torchvision://resnet50',
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'... | 3,380 | 29.736364 | 77 | py |
Mask-aware-IoU | Mask-aware-IoU-master/configs/_base_/models/cascade_rcnn_r50_fpn.py | # model settings
model = dict(
type='CascadeRCNN',
pretrained='torchvision://resnet50',
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,
... | 5,972 | 31.818681 | 79 | py |
Mask-aware-IoU | Mask-aware-IoU-master/configs/_base_/models/rpn_r50_caffe_c4.py | # model settings
model = dict(
type='RPN',
pretrained='open-mmlab://detectron2/resnet50_caffe',
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... | 1,655 | 27.067797 | 72 | py |
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