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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s2anet | s2anet-master/DOTA_devkit/polyiou/setup.py | from distutils.core import setup, Extension
polyiou_module = Extension(
'_polyiou',
sources=['csrc/polyiou_wrap.cxx', 'csrc/polyiou.cpp'])
setup(name='polyiou',
version='0.1',
author="SWIG Docs",
description="""Simple swig example from docs""",
ext_modules=[polyiou_module],
py_mod... | 346 | 25.692308 | 58 | py |
s2anet | s2anet-master/DOTA_devkit/polyiou/__init__.py | from . import polyiou
__all__ = ['polyiou'] | 44 | 14 | 21 | py |
s2anet | s2anet-master/DOTA_devkit/polyiou/polyiou.py | # This file was automatically generated by SWIG (http://www.swig.org).
# Version 2.0.10
#
# Do not make changes to this file unless you know what you are doing--modify
# the SWIG interface file instead.
from sys import version_info
if version_info >= (2,6,0):
def swig_import_helper():
from os.path import... | 7,002 | 44.771242 | 107 | py |
s2anet | s2anet-master/demo/webcam_demo.py | import argparse
import cv2
import torch
from mmdet.apis import inference_detector, init_detector, show_result
def parse_args():
parser = argparse.ArgumentParser(description='MMDetection webcam demo')
parser.add_argument('config', help='test config file path')
parser.add_argument('checkpoint', help='chec... | 1,243 | 26.644444 | 79 | py |
s2anet | s2anet-master/demo/demo_inference.py | import argparse
import os
import os.path as osp
import pdb
import random
import cv2
import mmcv
from mmcv import Config
from mmdet.apis import init_detector, inference_detector
from mmdet.core import rotated_box_to_poly_single
from mmdet.datasets import build_dataset
def show_result_rbox(img,
d... | 3,798 | 32.324561 | 118 | py |
s2anet | s2anet-master/configs/faster_rcnn_r50_fpn_1x.py | # model settings
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,
style='pytorch'),
neck=dict(
type='FPN',
in_channels=[25... | 5,328 | 29.451429 | 78 | py |
s2anet | s2anet-master/configs/cascade_rcnn_r50_caffe_c4_1x.py | # model settings
norm_cfg = dict(type='BN', requires_grad=False)
model = dict(
type='CascadeRCNN',
num_stages=3,
pretrained='open-mmlab://resnet50_caffe',
backbone=dict(
type='ResNet',
depth=50,
num_stages=3,
strides=(1, 2, 2),
dilations=(1, 1, 1),
out_ind... | 7,572 | 30.036885 | 78 | py |
s2anet | s2anet-master/configs/retinanet_r101_fpn_1x.py | # model settings
model = dict(
type='RetinaNet',
pretrained='torchvision://resnet101',
backbone=dict(
type='ResNet',
depth=101,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='pytorch'),
neck=dict(
type='FPN',
in_channels=[2... | 3,802 | 28.253846 | 77 | py |
s2anet | s2anet-master/configs/fast_mask_rcnn_r50_fpn_1x.py | # model settings
model = dict(
type='FastRCNN',
pretrained='torchvision://resnet50',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='pytorch'),
neck=dict(
type='FPN',
in_channels=[256,... | 4,901 | 30.831169 | 77 | py |
s2anet | s2anet-master/configs/faster_rcnn_x101_32x4d_fpn_1x.py | # model settings
model = dict(
type='FasterRCNN',
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'),
neck... | 5,385 | 29.429379 | 78 | py |
s2anet | s2anet-master/configs/cascade_rcnn_x101_32x4d_fpn_1x.py | # model settings
model = dict(
type='CascadeRCNN',
num_stages=3,
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='... | 7,434 | 30.371308 | 78 | py |
s2anet | s2anet-master/configs/faster_rcnn_x101_64x4d_fpn_1x.py | # model settings
model = dict(
type='FasterRCNN',
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='pytorch'),
neck... | 5,385 | 29.429379 | 78 | py |
s2anet | s2anet-master/configs/mask_rcnn_r101_fpn_1x.py | # model settings
model = dict(
type='MaskRCNN',
pretrained='torchvision://resnet101',
backbone=dict(
type='ResNet',
depth=101,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='pytorch'),
neck=dict(
type='FPN',
in_channels=[25... | 5,747 | 29.412698 | 78 | py |
s2anet | s2anet-master/configs/mask_rcnn_r50_caffe_c4_1x.py | # model settings
norm_cfg = dict(type='BN', requires_grad=False)
model = dict(
type='MaskRCNN',
# pretrained='open-mmlab://resnet50_caffe',
backbone=dict(
type='ResNet',
depth=50,
num_stages=3,
strides=(1, 2, 2),
dilations=(1, 1, 1),
out_indices=(2, ),
... | 5,767 | 28.88601 | 78 | py |
s2anet | s2anet-master/configs/faster_rcnn_r50_caffe_c4_1x.py | # model settings
norm_cfg = dict(type='BN', requires_grad=False)
model = dict(
type='FasterRCNN',
pretrained='open-mmlab://resnet50_caffe',
backbone=dict(
type='ResNet',
depth=50,
num_stages=3,
strides=(1, 2, 2),
dilations=(1, 1, 1),
out_indices=(2, ),
... | 5,400 | 29.005556 | 78 | py |
s2anet | s2anet-master/configs/retinanet_x101_32x4d_fpn_1x.py | # model settings
model = dict(
type='RetinaNet',
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'),
neck=... | 3,856 | 28.219697 | 77 | py |
s2anet | s2anet-master/configs/fast_mask_rcnn_r101_fpn_1x.py | # model settings
model = dict(
type='FastRCNN',
pretrained='torchvision://resnet101',
backbone=dict(
type='ResNet',
depth=101,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='pytorch'),
neck=dict(
type='FPN',
in_channels=[25... | 4,904 | 30.850649 | 77 | py |
s2anet | s2anet-master/configs/rpn_x101_32x4d_fpn_1x.py | # model settings
model = dict(
type='RPN',
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'),
neck=dict(
... | 3,924 | 28.734848 | 78 | py |
s2anet | s2anet-master/configs/faster_rcnn_ohem_r50_fpn_1x.py | # model settings
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,
style='pytorch'),
neck=dict(
type='FPN',
in_channels=[25... | 5,326 | 29.44 | 78 | py |
s2anet | s2anet-master/configs/mask_rcnn_r50_fpn_1x.py | # model settings
model = dict(
type='MaskRCNN',
pretrained='torchvision://resnet50',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='pytorch'),
neck=dict(
type='FPN',
in_channels=[256,... | 5,774 | 29.394737 | 78 | py |
s2anet | s2anet-master/configs/ssd512_coco.py | # model settings
input_size = 512
model = dict(
type='SingleStageDetector',
pretrained='open-mmlab://vgg16_caffe',
backbone=dict(
type='SSDVGG',
input_size=input_size,
depth=16,
with_last_pool=False,
ceil_mode=True,
out_indices=(3, 4),
out_feature_indi... | 3,959 | 28.333333 | 79 | py |
s2anet | s2anet-master/configs/faster_rcnn_r101_fpn_1x.py | # model settings
model = dict(
type='FasterRCNN',
pretrained='torchvision://resnet101',
backbone=dict(
type='ResNet',
depth=101,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='pytorch'),
neck=dict(
type='FPN',
in_channels=[... | 5,331 | 29.468571 | 78 | py |
s2anet | s2anet-master/configs/mask_rcnn_x101_64x4d_fpn_1x.py | # model settings
model = dict(
type='MaskRCNN',
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='pytorch'),
neck=d... | 5,801 | 29.376963 | 78 | py |
s2anet | s2anet-master/configs/cascade_rcnn_r50_fpn_1x.py | # model settings
model = dict(
type='CascadeRCNN',
num_stages=3,
pretrained='torchvision://resnet50',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='pytorch'),
neck=dict(
type='FPN',
... | 7,377 | 30.395745 | 78 | py |
s2anet | s2anet-master/configs/cascade_mask_rcnn_x101_32x4d_fpn_1x.py | # model settings
model = dict(
type='CascadeRCNN',
num_stages=3,
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='... | 8,033 | 30.382813 | 78 | py |
s2anet | s2anet-master/configs/rpn_x101_64x4d_fpn_1x.py | # model settings
model = dict(
type='RPN',
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='pytorch'),
neck=dict(
... | 3,924 | 28.734848 | 78 | py |
s2anet | s2anet-master/configs/fast_rcnn_r101_fpn_1x.py | # model settings
model = dict(
type='FastRCNN',
pretrained='torchvision://resnet101',
backbone=dict(
type='ResNet',
depth=101,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='pytorch'),
neck=dict(
type='FPN',
in_channels=[25... | 4,344 | 31.185185 | 78 | py |
s2anet | s2anet-master/configs/rpn_r50_fpn_1x.py | # model settings
model = dict(
type='RPN',
pretrained='torchvision://resnet50',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='pytorch'),
neck=dict(
type='FPN',
in_channels=[256, 512,... | 3,867 | 28.753846 | 78 | py |
s2anet | s2anet-master/configs/retinanet_r50_fpn_1x.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,
style='pytorch'),
neck=dict(
type='FPN',
in_channels=[256... | 3,799 | 28.230769 | 77 | py |
s2anet | s2anet-master/configs/retinanet_x101_64x4d_fpn_1x.py | # model settings
model = dict(
type='RetinaNet',
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='pytorch'),
neck=... | 3,856 | 28.219697 | 77 | py |
s2anet | s2anet-master/configs/fast_rcnn_r50_fpn_1x.py | # model settings
model = dict(
type='FastRCNN',
pretrained='torchvision://resnet50',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='pytorch'),
neck=dict(
type='FPN',
in_channels=[256,... | 4,341 | 31.162963 | 78 | py |
s2anet | s2anet-master/configs/cascade_mask_rcnn_r50_fpn_1x.py | # model settings
model = dict(
type='CascadeRCNN',
num_stages=3,
pretrained='torchvision://resnet50',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='pytorch'),
neck=dict(
type='FPN',
... | 7,976 | 30.405512 | 78 | py |
s2anet | s2anet-master/configs/rpn_r50_caffe_c4_1x.py | # model settings
model = dict(
type='RPN',
pretrained='open-mmlab://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='BN', requ... | 3,893 | 28.953846 | 78 | py |
s2anet | s2anet-master/configs/cascade_rcnn_x101_64x4d_fpn_1x.py | # model settings
model = dict(
type='CascadeRCNN',
num_stages=3,
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='... | 7,434 | 30.371308 | 78 | py |
s2anet | s2anet-master/configs/fast_mask_rcnn_r50_caffe_c4_1x.py | # model settings
norm_cfg = dict(type='BN', requires_grad=False)
model = dict(
type='FastRCNN',
pretrained='open-mmlab://resnet50_caffe',
backbone=dict(
type='ResNet',
depth=50,
num_stages=3,
strides=(1, 2, 2),
dilations=(1, 1, 1),
out_indices=(2, ),
f... | 4,653 | 29.418301 | 75 | py |
s2anet | s2anet-master/configs/cascade_mask_rcnn_x101_64x4d_fpn_1x.py | # model settings
model = dict(
type='CascadeRCNN',
num_stages=3,
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='... | 8,033 | 30.382813 | 78 | py |
s2anet | s2anet-master/configs/ssd300_coco.py | # model settings
input_size = 300
model = dict(
type='SingleStageDetector',
pretrained='open-mmlab://vgg16_caffe',
backbone=dict(
type='SSDVGG',
input_size=input_size,
depth=16,
with_last_pool=False,
ceil_mode=True,
out_indices=(3, 4),
out_feature_indi... | 3,942 | 28.207407 | 79 | py |
s2anet | s2anet-master/configs/mask_rcnn_x101_32x4d_fpn_1x.py | # model settings
model = dict(
type='MaskRCNN',
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'),
neck=d... | 5,801 | 29.376963 | 78 | py |
s2anet | s2anet-master/configs/fast_rcnn_r50_caffe_c4_1x.py | # model settings
norm_cfg = dict(type='BN', requires_grad=False)
model = dict(
type='FastRCNN',
pretrained='open-mmlab://resnet50_caffe',
backbone=dict(
type='ResNet',
depth=50,
num_stages=3,
strides=(1, 2, 2),
dilations=(1, 1, 1),
out_indices=(2, ),
f... | 4,525 | 30.65035 | 78 | py |
s2anet | s2anet-master/configs/cascade_mask_rcnn_r50_caffe_c4_1x.py | # model settings
norm_cfg = dict(type='BN', requires_grad=False)
model = dict(
type='CascadeRCNN',
num_stages=3,
pretrained='open-mmlab://resnet50_caffe',
backbone=dict(
type='ResNet',
depth=50,
num_stages=3,
strides=(1, 2, 2),
dilations=(1, 1, 1),
out_ind... | 7,929 | 29.976563 | 78 | py |
s2anet | s2anet-master/configs/cascade_mask_rcnn_r101_fpn_1x.py | # model settings
model = dict(
type='CascadeRCNN',
num_stages=3,
pretrained='torchvision://resnet101',
backbone=dict(
type='ResNet',
depth=101,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='pytorch'),
neck=dict(
type='FPN',
... | 7,979 | 30.417323 | 78 | py |
s2anet | s2anet-master/configs/cascade_rcnn_r101_fpn_1x.py | # model settings
model = dict(
type='CascadeRCNN',
num_stages=3,
pretrained='torchvision://resnet101',
backbone=dict(
type='ResNet',
depth=101,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='pytorch'),
neck=dict(
type='FPN',
... | 7,380 | 30.408511 | 78 | py |
s2anet | s2anet-master/configs/rpn_r101_fpn_1x.py | # model settings
model = dict(
type='RPN',
pretrained='torchvision://resnet101',
backbone=dict(
type='ResNet',
depth=101,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='pytorch'),
neck=dict(
type='FPN',
in_channels=[256, 51... | 3,870 | 28.776923 | 78 | py |
s2anet | s2anet-master/configs/ghm/retinanet_ghm_r50_fpn_1x.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,
style='pytorch'),
neck=dict(
type='FPN',
in_channels=[256... | 3,805 | 28.053435 | 77 | py |
s2anet | s2anet-master/configs/dcn/faster_rcnn_mdconv_c3-c5_group4_r50_fpn_1x.py | # model settings
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,
style='pytorch',
dcn=dict(
modulated=True, deformable_gr... | 5,492 | 29.859551 | 78 | py |
s2anet | s2anet-master/configs/dcn/mask_rcnn_dconv_c3-c5_r50_fpn_1x.py | # model settings
model = dict(
type='MaskRCNN',
pretrained='torchvision://resnet50',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='pytorch',
dcn=dict(
modulated=False, deformable_gro... | 5,901 | 29.739583 | 78 | py |
s2anet | s2anet-master/configs/dcn/cascade_rcnn_dconv_c3-c5_r50_fpn_1x.py | # model settings
model = dict(
type='CascadeRCNN',
num_stages=3,
pretrained='torchvision://resnet50',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='pytorch',
dcn=dict(
modulated=... | 7,534 | 30.659664 | 78 | py |
s2anet | s2anet-master/configs/dcn/faster_rcnn_mdconv_c3-c5_r50_fpn_1x.py | # model settings
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,
style='pytorch',
dcn=dict(
modulated=True, deformable_gr... | 5,485 | 29.820225 | 78 | py |
s2anet | s2anet-master/configs/dcn/faster_rcnn_dconv_c3-c5_x101_32x4d_fpn_1x.py | # model settings
model = dict(
type='FasterRCNN',
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',
d... | 5,589 | 29.546448 | 78 | py |
s2anet | s2anet-master/configs/dcn/faster_rcnn_dpool_r50_fpn_1x.py | # model settings
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,
style='pytorch'),
neck=dict(
type='FPN',
in_channels=[25... | 5,468 | 29.21547 | 78 | py |
s2anet | s2anet-master/configs/dcn/faster_rcnn_mdpool_r50_fpn_1x.py | # model settings
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,
style='pytorch'),
neck=dict(
type='FPN',
in_channels=[25... | 5,478 | 29.270718 | 78 | py |
s2anet | s2anet-master/configs/dcn/cascade_mask_rcnn_dconv_c3-c5_r50_fpn_1x.py | # model settings
model = dict(
type='CascadeRCNN',
num_stages=3,
pretrained='torchvision://resnet50',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='pytorch',
dcn=dict(
modulated=... | 8,133 | 30.649805 | 78 | py |
s2anet | s2anet-master/configs/dcn/faster_rcnn_dconv_c3-c5_r50_fpn_1x.py | # model settings
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,
style='pytorch',
dcn=dict(
modulated=False, deformable_g... | 5,485 | 29.820225 | 78 | py |
s2anet | s2anet-master/configs/htc/htc_r101_fpn_20e.py | # model settings
model = dict(
type='HybridTaskCascade',
num_stages=3,
pretrained='torchvision://resnet101',
interleaved=True,
mask_info_flow=True,
backbone=dict(
type='ResNet',
depth=101,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
st... | 8,666 | 30.402174 | 79 | py |
s2anet | s2anet-master/configs/htc/htc_x101_32x4d_fpn_20e_16gpu.py | # model settings
model = dict(
type='HybridTaskCascade',
num_stages=3,
pretrained='open-mmlab://resnext101_32x4d',
interleaved=True,
mask_info_flow=True,
backbone=dict(
type='ResNeXt',
depth=101,
groups=32,
base_width=4,
num_stages=4,
out_indices=(... | 8,720 | 30.370504 | 79 | py |
s2anet | s2anet-master/configs/htc/htc_r50_fpn_20e.py | # model settings
model = dict(
type='HybridTaskCascade',
num_stages=3,
pretrained='torchvision://resnet50',
interleaved=True,
mask_info_flow=True,
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
styl... | 8,663 | 30.391304 | 79 | py |
s2anet | s2anet-master/configs/htc/htc_x101_64x4d_fpn_20e_16gpu.py | # model settings
model = dict(
type='HybridTaskCascade',
num_stages=3,
pretrained='open-mmlab://resnext101_64x4d',
interleaved=True,
mask_info_flow=True,
backbone=dict(
type='ResNeXt',
depth=101,
groups=64,
base_width=4,
num_stages=4,
out_indices=(... | 8,720 | 30.370504 | 79 | py |
s2anet | s2anet-master/configs/htc/htc_dconv_c3-c5_mstrain_400_1400_x101_64x4d_fpn_20e.py | # model settings
model = dict(
type='HybridTaskCascade',
num_stages=3,
pretrained='open-mmlab://resnext101_64x4d',
interleaved=True,
mask_info_flow=True,
backbone=dict(
type='ResNeXt',
depth=101,
groups=64,
base_width=4,
num_stages=4,
out_indices=(... | 9,015 | 30.305556 | 79 | py |
s2anet | s2anet-master/configs/htc/htc_r50_fpn_1x.py | # model settings
model = dict(
type='HybridTaskCascade',
num_stages=3,
pretrained='torchvision://resnet50',
interleaved=True,
mask_info_flow=True,
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
styl... | 8,661 | 30.384058 | 79 | py |
s2anet | s2anet-master/configs/htc/htc_without_semantic_r50_fpn_1x.py | # model settings
model = dict(
type='HybridTaskCascade',
num_stages=3,
pretrained='torchvision://resnet50',
interleaved=True,
mask_info_flow=True,
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
styl... | 8,049 | 30.445313 | 78 | py |
s2anet | s2anet-master/configs/reppoints/bbox_r50_grid_fpn_1x.py | # model settings
norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
model = dict(
type='RepPointsDetector',
pretrained='torchvision://resnet50',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style=... | 4,342 | 28.344595 | 79 | py |
s2anet | s2anet-master/configs/reppoints/reppoints_moment_x101_dcn_fpn_2x.py | # model settings
norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
model = dict(
type='RepPointsDetector',
pretrained='open-mmlab://resnext101_32x4d',
backbone=dict(
type='ResNeXt',
depth=101,
groups=32,
base_width=4,
num_stages=4,
out_indices=(0,... | 4,457 | 28.72 | 79 | py |
s2anet | s2anet-master/configs/reppoints/reppoints_moment_r101_fpn_2x_mt.py | # model settings
norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
model = dict(
type='RepPointsDetector',
pretrained='torchvision://resnet101',
backbone=dict(
type='ResNet',
depth=101,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
styl... | 4,289 | 28.383562 | 79 | py |
s2anet | s2anet-master/configs/reppoints/reppoints_moment_x101_dcn_fpn_2x_mt.py | # model settings
norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
model = dict(
type='RepPointsDetector',
pretrained='open-mmlab://resnext101_32x4d',
backbone=dict(
type='ResNeXt',
depth=101,
groups=32,
base_width=4,
num_stages=4,
out_indices=(0,... | 4,533 | 28.441558 | 79 | py |
s2anet | s2anet-master/configs/reppoints/reppoints_minmax_r50_fpn_1x.py | # model settings
norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
model = dict(
type='RepPointsDetector',
pretrained='torchvision://resnet50',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style=... | 4,209 | 28.647887 | 79 | py |
s2anet | s2anet-master/configs/reppoints/reppoints_partial_minmax_r50_fpn_1x.py | # model settings
norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
model = dict(
type='RepPointsDetector',
pretrained='torchvision://resnet50',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style=... | 4,225 | 28.760563 | 79 | py |
s2anet | s2anet-master/configs/reppoints/bbox_r50_grid_center_fpn_1x.py | # model settings
norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
model = dict(
type='RepPointsDetector',
pretrained='torchvision://resnet50',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style=... | 4,239 | 28.65035 | 79 | py |
s2anet | s2anet-master/configs/reppoints/reppoints_moment_r101_dcn_fpn_2x.py | # model settings
norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
model = dict(
type='RepPointsDetector',
pretrained='torchvision://resnet101',
backbone=dict(
type='ResNet',
depth=101,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
styl... | 4,362 | 29.089655 | 79 | py |
s2anet | s2anet-master/configs/reppoints/reppoints_moment_r50_fpn_2x.py | # model settings
norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
model = dict(
type='RepPointsDetector',
pretrained='torchvision://resnet50',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style=... | 4,210 | 28.65493 | 79 | py |
s2anet | s2anet-master/configs/reppoints/reppoints_moment_r101_dcn_fpn_2x_mt.py | # model settings
norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
model = dict(
type='RepPointsDetector',
pretrained='torchvision://resnet101',
backbone=dict(
type='ResNet',
depth=101,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
styl... | 4,438 | 28.791946 | 79 | py |
s2anet | s2anet-master/configs/reppoints/reppoints_moment_r101_fpn_2x.py | # model settings
norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
model = dict(
type='RepPointsDetector',
pretrained='torchvision://resnet101',
backbone=dict(
type='ResNet',
depth=101,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
styl... | 4,213 | 28.676056 | 79 | py |
s2anet | s2anet-master/configs/reppoints/reppoints_moment_r50_fpn_1x.py | # model settings
norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
model = dict(
type='RepPointsDetector',
pretrained='torchvision://resnet50',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style=... | 4,209 | 28.647887 | 79 | py |
s2anet | s2anet-master/configs/reppoints/reppoints_moment_r50_fpn_2x_mt.py | # model settings
norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
model = dict(
type='RepPointsDetector',
pretrained='torchvision://resnet50',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style=... | 4,286 | 28.363014 | 79 | py |
s2anet | s2anet-master/configs/fp16/faster_rcnn_r50_fpn_fp16_1x.py | # fp16 settings
fp16 = dict(loss_scale=512.)
# model settings
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,
style='pytorch'),
neck=dic... | 5,379 | 29.224719 | 78 | py |
s2anet | s2anet-master/configs/fp16/retinanet_r50_fpn_fp16_1x.py | # fp16 settings
fp16 = dict(loss_scale=512.)
# 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,
style='pytorch'),
neck=dict... | 3,850 | 27.954887 | 77 | py |
s2anet | s2anet-master/configs/fp16/mask_rcnn_r50_fpn_fp16_1x.py | # fp16 settings
fp16 = dict(loss_scale=512.)
# model settings
model = dict(
type='MaskRCNN',
pretrained='torchvision://resnet50',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='pytorch'),
neck=dict(... | 5,825 | 29.186528 | 78 | py |
s2anet | s2anet-master/configs/fcos/fcos_mstrain_640_800_x101_64x4d_fpn_gn_2x.py | # model settings
model = dict(
type='FCOS',
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='pytorch'),
neck=dict(... | 3,982 | 27.45 | 77 | py |
s2anet | s2anet-master/configs/fcos/fcos_mstrain_640_800_r101_caffe_fpn_gn_2x_4gpu.py | # model settings
model = dict(
type='FCOS',
pretrained='open-mmlab://resnet101_caffe',
backbone=dict(
type='ResNet',
depth=101,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=False),
style='caffe'),
... | 3,995 | 27.748201 | 75 | py |
s2anet | s2anet-master/configs/fcos/fcos_r50_caffe_fpn_gn_1x_4gpu.py | # model settings
model = dict(
type='FCOS',
pretrained='open-mmlab://resnet50_caffe',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=False),
style='caffe'),
ne... | 3,901 | 27.903704 | 75 | py |
s2anet | s2anet-master/configs/hrsc2016/retinanet_obb_r50_fpn_6x_hrsc2016.py | PI = 3.141592653
# 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,
style='pytorch'),
neck=dict(
type='FPN',
... | 4,338 | 29.556338 | 89 | py |
s2anet | s2anet-master/configs/hrsc2016/cascade_s2anet_2s_r50_fpn_3x_hrsc2016.py | # model settings
model = dict(
type='CascadeS2ANetDetector',
pretrained='torchvision://resnet50',
num_stages=2,
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='pytorch'),
neck=dict(
type='... | 5,813 | 30.597826 | 89 | py |
s2anet | s2anet-master/configs/hrsc2016/cascade_s2anet_1s_r50_fpn_4x_hrsc2016.py | # model settings
model = dict(
type='CascadeS2ANetDetector',
pretrained='torchvision://resnet50',
num_stages=1,
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='pytorch'),
neck=dict(
type='... | 4,488 | 29.331081 | 89 | py |
s2anet | s2anet-master/configs/hrsc2016/s2anet_r101_fpn_3x_hrsc2016.py | # model settings
model = dict(
type='S2ANetDetector',
pretrained='torchvision://resnet101',
backbone=dict(
type='ResNet',
depth=101,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='pytorch'),
neck=dict(
type='FPN',
in_channe... | 5,011 | 30.325 | 89 | py |
s2anet | s2anet-master/configs/hrsc2016/s2anet_r50_fpn_3x_hrsc2016.py | # model settings
model = dict(
type='S2ANetDetector',
pretrained='torchvision://resnet50',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='pytorch'),
neck=dict(
type='FPN',
in_channels... | 5,009 | 30.3125 | 89 | py |
s2anet | s2anet-master/configs/ms_rcnn/ms_rcnn_r101_caffe_fpn_1x.py | # model settings
model = dict(
type='MaskScoringRCNN',
pretrained='open-mmlab://resnet101_caffe',
backbone=dict(
type='ResNet',
depth=101,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=False),
style='ca... | 6,072 | 29.365 | 78 | py |
s2anet | s2anet-master/configs/ms_rcnn/ms_rcnn_x101_64x4d_fpn_1x.py | # model settings
model = dict(
type='MaskScoringRCNN',
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='pytorch'),
... | 6,064 | 29.174129 | 78 | py |
s2anet | s2anet-master/configs/ms_rcnn/ms_rcnn_r50_caffe_fpn_1x.py | # model settings
model = dict(
type='MaskScoringRCNN',
pretrained='open-mmlab://resnet50_caffe',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=False),
style='caff... | 6,069 | 29.35 | 78 | py |
s2anet | s2anet-master/configs/rotated_iou/retinanet_obb_r50_fpn_6x_hrsc2016_iouloss.py | PI = 3.141592653
# 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,
style='pytorch'),
neck=dict(
type='FPN',
... | 4,454 | 29.513699 | 89 | py |
s2anet | s2anet-master/configs/rotated_iou/cascade_s2anet_2s_r50_fpn_1x_dota_iouloss.py | # model settings
model = dict(
type='CascadeS2ANetDetector',
pretrained='torchvision://resnet50',
num_stages=2,
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='pytorch'),
neck=dict(
type='... | 6,444 | 32.221649 | 85 | py |
s2anet | s2anet-master/configs/hrnet/cascade_rcnn_hrnetv2p_w32_20e.py | # model settings
model = dict(
type='CascadeRCNN',
num_stages=3,
pretrained='open-mmlab://msra/hrnetv2_w32',
backbone=dict(
type='HRNet',
extra=dict(
stage1=dict(
num_modules=1,
num_branches=1,
block='BOTTLENECK',
... | 8,039 | 31.031873 | 78 | py |
s2anet | s2anet-master/configs/hrnet/mask_rcnn_hrnetv2p_w18_1x.py | # model settings
model = dict(
type='MaskRCNN',
pretrained='open-mmlab://msra/hrnetv2_w18',
backbone=dict(
type='HRNet',
extra=dict(
stage1=dict(
num_modules=1,
num_branches=1,
block='BOTTLENECK',
num_blocks=(4, ),
... | 6,463 | 30.378641 | 78 | py |
s2anet | s2anet-master/configs/hrnet/faster_rcnn_hrnetv2p_w32_1x.py | # model settings
model = dict(
type='FasterRCNN',
pretrained='open-mmlab://msra/hrnetv2_w32',
backbone=dict(
type='HRNet',
extra=dict(
stage1=dict(
num_modules=1,
num_branches=1,
block='BOTTLENECK',
num_blocks=(4, ),... | 5,991 | 30.371728 | 78 | py |
s2anet | s2anet-master/configs/hrnet/faster_rcnn_hrnetv2p_w18_1x.py | # model settings
model = dict(
type='FasterRCNN',
pretrained='open-mmlab://msra/hrnetv2_w18',
backbone=dict(
type='HRNet',
extra=dict(
stage1=dict(
num_modules=1,
num_branches=1,
block='BOTTLENECK',
num_blocks=(4, ),... | 5,988 | 30.356021 | 78 | py |
s2anet | s2anet-master/configs/hrnet/cascade_mask_rcnn_hrnetv2p_w32_20e.py | # model settings
model = dict(
type='CascadeRCNN',
num_stages=3,
pretrained='open-mmlab://msra/hrnetv2_w32',
backbone=dict(
type='HRNet',
extra=dict(
stage1=dict(
num_modules=1,
num_branches=1,
block='BOTTLENECK',
... | 8,641 | 31.007407 | 78 | py |
s2anet | s2anet-master/configs/hrnet/fcos_hrnetv2p_w32_gn_1x_4gpu.py | # model settings
model = dict(
type='FCOS',
pretrained='open-mmlab://msra/hrnetv2_w32',
backbone=dict(
type='HRNet',
extra=dict(
stage1=dict(
num_modules=1,
num_branches=1,
block='BOTTLENECK',
num_blocks=(4, ),
... | 4,426 | 28.317881 | 75 | py |
s2anet | s2anet-master/configs/hrnet/faster_rcnn_hrnetv2p_w40_1x.py | # model settings
model = dict(
type='FasterRCNN',
pretrained='open-mmlab://msra/hrnetv2_w40',
backbone=dict(
type='HRNet',
extra=dict(
stage1=dict(
num_modules=1,
num_branches=1,
block='BOTTLENECK',
num_blocks=(4, ),... | 5,991 | 30.371728 | 78 | py |
s2anet | s2anet-master/configs/hrnet/mask_rcnn_hrnetv2p_w32_1x.py | # model settings
model = dict(
type='MaskRCNN',
pretrained='open-mmlab://msra/hrnetv2_w32',
backbone=dict(
type='HRNet',
extra=dict(
stage1=dict(
num_modules=1,
num_branches=1,
block='BOTTLENECK',
num_blocks=(4, ),
... | 6,373 | 30.245098 | 78 | py |
s2anet | s2anet-master/configs/hrnet/htc_hrnetv2p_w32_20e.py | # model settings
model = dict(
type='HybridTaskCascade',
num_stages=3,
pretrained='open-mmlab://msra/hrnetv2_w32',
interleaved=True,
mask_info_flow=True,
backbone=dict(
type='HRNet',
extra=dict(
stage1=dict(
num_modules=1,
num_branches=... | 9,326 | 30.941781 | 79 | py |
s2anet | s2anet-master/configs/empirical_attention/faster_rcnn_r50_fpn_attention_1111_dcn_1x.py | # model settings
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,
style='pytorch',
gen_attention=dict(
spatial_range=-1, n... | 5,680 | 30.214286 | 79 | py |
s2anet | s2anet-master/configs/empirical_attention/faster_rcnn_r50_fpn_attention_1111_1x.py | # model settings
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,
style='pytorch',
gen_attention=dict(
spatial_range=-1, n... | 5,531 | 29.905028 | 79 | py |
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