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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D2Det | D2Det-master/tools/publish_model.py | import argparse
import subprocess
import torch
def parse_args():
parser = argparse.ArgumentParser(
description='Process a checkpoint to be published')
parser.add_argument('in_file', help='input checkpoint filename')
parser.add_argument('out_file', help='output checkpoint filename')
args = par... | 1,029 | 26.837838 | 77 | py |
D2Det | D2Det-master/tools/upgrade_model_version.py | import argparse
import re
from collections import OrderedDict
import torch
def convert(in_file, out_file):
"""Convert keys in checkpoints.
There can be some breaking changes during the development of mmdetection,
and this tool is used for upgrading checkpoints trained with old versions
to the latest... | 1,322 | 29.767442 | 77 | py |
D2Det | D2Det-master/tools/test_robustness.py | import argparse
import copy
import os
import os.path as osp
import shutil
import tempfile
import mmcv
import torch
import torch.distributed as dist
from mmcv.parallel import MMDataParallel, MMDistributedDataParallel
from mmcv.runner import get_dist_info, init_dist, load_checkpoint
from pycocotools.coco import COCO
fro... | 16,728 | 36.341518 | 79 | py |
D2Det | D2Det-master/tools/train.py | from __future__ import division
import argparse
import copy
import os
import os.path as osp
import sys
sys.path.insert(0, osp.join(osp.dirname(osp.abspath(__file__)), '../'))
import time
import mmcv
import torch
from mmcv import Config
from mmcv.runner import init_dist
from mmdet import __version__
from mmdet.apis im... | 4,827 | 32.296552 | 77 | py |
D2Det | D2Det-master/tools/detectron2pytorch.py | import argparse
from collections import OrderedDict
import mmcv
import torch
arch_settings = {50: (3, 4, 6, 3), 101: (3, 4, 23, 3)}
def convert_bn(blobs, state_dict, caffe_name, torch_name, converted_names):
# detectron replace bn with affine channel layer
state_dict[torch_name + '.bias'] = torch.from_numpy... | 3,830 | 42.044944 | 78 | py |
D2Det | D2Det-master/tests/async_benchmark.py | import asyncio
import os
import shutil
import urllib
import mmcv
import torch
from mmdet.apis import (async_inference_detector, inference_detector,
init_detector, show_result)
from mmdet.utils.contextmanagers import concurrent
from mmdet.utils.profiling import profile_time
async def main():
... | 3,112 | 29.223301 | 79 | py |
D2Det | D2Det-master/tests/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 |
D2Det | D2Det-master/tests/test_forward.py | """
pytest tests/test_forward.py
"""
import copy
from os.path import dirname, exists, join
import numpy as np
import torch
def _get_config_directory():
""" Find the predefined detector config directory """
try:
# Assume we are running in the source mmdetection repo
repo_dpath = dirname(dirnam... | 12,250 | 30.493573 | 79 | py |
D2Det | D2Det-master/tests/test_async.py | """Tests for async interface."""
import asyncio
import os
import sys
import asynctest
import mmcv
import torch
from mmdet.apis import async_inference_detector, init_detector
if sys.version_info >= (3, 7):
from mmdet.utils.contextmanagers import concurrent
class AsyncTestCase(asynctest.TestCase):
use_defau... | 2,332 | 28.531646 | 74 | py |
D2Det | D2Det-master/tests/test_nms.py | """
CommandLine:
pytest tests/test_nms.py
"""
import numpy as np
import torch
from mmdet.ops.nms.nms_wrapper import nms
def test_nms_device_and_dtypes_cpu():
"""
CommandLine:
xdoctest -m tests/test_nms.py test_nms_device_and_dtypes_cpu
"""
iou_thr = 0.7
base_dets = np.array([[49.1, 32... | 2,297 | 31.366197 | 68 | py |
D2Det | D2Det-master/tests/test_config.py | from os.path import dirname, exists, join
def _get_config_directory():
""" Find the predefined detector config directory """
try:
# Assume we are running in the source mmdetection repo
repo_dpath = dirname(dirname(__file__))
except NameError:
# For IPython development when this __f... | 7,293 | 41.16185 | 79 | py |
D2Det | D2Det-master/tests/test_sampler.py | import torch
from mmdet.core import MaxIoUAssigner
from mmdet.core.bbox.samplers import OHEMSampler, RandomSampler
def test_random_sampler():
assigner = MaxIoUAssigner(
pos_iou_thr=0.5,
neg_iou_thr=0.5,
ignore_iof_thr=0.5,
ignore_wrt_candidates=False,
)
bboxes = torch.Floa... | 7,320 | 28.284 | 79 | py |
D2Det | D2Det-master/tests/test_heads.py | import mmcv
import torch
from mmdet.core import build_assigner, build_sampler
from mmdet.models.anchor_heads import AnchorHead
from mmdet.models.bbox_heads import BBoxHead
def test_anchor_head_loss():
"""
Tests anchor head loss when truth is empty and non-empty
"""
self = AnchorHead(num_classes=4, in... | 10,899 | 30.964809 | 79 | py |
D2Det | D2Det-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 import MaxIoUAssigner
from mmdet.core.bbox.assigners import ApproxMaxIoUAssigner, PointAssigner
def test_max_iou_assigner():
self = MaxIoUAssigner(
... | 8,605 | 26.407643 | 79 | py |
D2Det | D2Det-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 |
D2Det | D2Det-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... | 5,373 | 29.534091 | 78 | py |
D2Det | D2Det-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... | 7,590 | 30.110656 | 78 | py |
D2Det | D2Det-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... | 3,847 | 28.374046 | 77 | py |
D2Det | D2Det-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,... | 4,956 | 30.980645 | 77 | py |
D2Det | D2Det-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... | 5,430 | 29.511236 | 78 | py |
D2Det | D2Det-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='... | 7,452 | 30.447257 | 78 | py |
D2Det | D2Det-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... | 5,430 | 29.511236 | 78 | py |
D2Det | D2Det-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... | 5,802 | 29.542105 | 78 | py |
D2Det | D2Det-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, ),
... | 5,822 | 29.015464 | 78 | py |
D2Det | D2Det-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, ),
... | 5,445 | 29.088398 | 78 | py |
D2Det | D2Det-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=... | 3,901 | 28.338346 | 77 | py |
D2Det | D2Det-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... | 4,959 | 31 | 77 | py |
D2Det | D2Det-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(
... | 3,978 | 28.917293 | 78 | py |
D2Det | D2Det-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... | 5,371 | 29.522727 | 78 | py |
D2Det | D2Det-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,... | 5,799 | 29.526316 | 78 | py |
D2Det | D2Det-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... | 4,004 | 28.448529 | 79 | py |
D2Det | D2Det-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=[... | 5,376 | 29.551136 | 78 | py |
D2Det | D2Det-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... | 5,856 | 29.505208 | 78 | py |
D2Det | D2Det-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',
... | 7,395 | 30.47234 | 78 | py |
D2Det | D2Det-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='... | 8,061 | 30.492188 | 78 | py |
D2Det | D2Det-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(
... | 3,978 | 28.917293 | 78 | py |
D2Det | D2Det-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... | 4,389 | 31.279412 | 78 | py |
D2Det | D2Det-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,... | 3,921 | 28.938931 | 78 | py |
D2Det | D2Det-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... | 3,844 | 28.351145 | 77 | py |
D2Det | D2Det-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=... | 3,901 | 28.338346 | 77 | py |
D2Det | D2Det-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,... | 4,386 | 31.257353 | 78 | py |
D2Det | D2Det-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',
... | 8,004 | 30.515748 | 78 | py |
D2Det | D2Det-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... | 3,947 | 29.137405 | 78 | py |
D2Det | D2Det-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='... | 7,452 | 30.447257 | 78 | py |
D2Det | D2Det-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... | 4,708 | 29.577922 | 75 | py |
D2Det | D2Det-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='... | 8,061 | 30.492188 | 78 | py |
D2Det | D2Det-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... | 3,987 | 28.323529 | 79 | py |
D2Det | D2Det-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... | 5,856 | 29.505208 | 78 | py |
D2Det | D2Det-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... | 4,570 | 30.743056 | 78 | py |
D2Det | D2Det-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... | 7,957 | 30.085938 | 78 | py |
D2Det | D2Det-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',
... | 8,007 | 30.527559 | 78 | py |
D2Det | D2Det-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',
... | 7,398 | 30.485106 | 78 | py |
D2Det | D2Det-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... | 3,924 | 28.961832 | 78 | py |
D2Det | D2Det-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... | 3,850 | 28.174242 | 77 | py |
D2Det | D2Det-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(type='DCNv2', deformable_groups=4, fallbac... | 5,522 | 30.02809 | 78 | py |
D2Det | D2Det-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(type='DCN', deformable_groups=1, fallback_on... | 5,938 | 29.932292 | 78 | py |
D2Det | D2Det-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(type='DCN', deformable_... | 7,534 | 30.793249 | 78 | py |
D2Det | D2Det-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(type='DCNv2', deformable_groups=1, fallbac... | 5,515 | 29.988764 | 78 | py |
D2Det | D2Det-master/configs/dcn/mask_rcnn_mdconv_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(type='DCNv2', deformable_groups=1, fallback_... | 5,940 | 29.942708 | 78 | py |
D2Det | D2Det-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... | 5,569 | 29.944444 | 78 | py |
D2Det | D2Det-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... | 5,513 | 29.296703 | 78 | py |
D2Det | D2Det-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... | 5,523 | 29.351648 | 78 | py |
D2Det | D2Det-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(type='DCN', deformable_... | 8,143 | 30.8125 | 78 | py |
D2Det | D2Det-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(type='DCN', deformable_groups=1, fallback_... | 5,512 | 29.97191 | 78 | py |
D2Det | D2Det-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... | 9,290 | 30.494915 | 79 | py |
D2Det | D2Det-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=(... | 9,344 | 30.464646 | 79 | py |
D2Det | D2Det-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... | 9,287 | 30.484746 | 79 | py |
D2Det | D2Det-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=(... | 9,344 | 30.464646 | 79 | py |
D2Det | D2Det-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=(... | 9,634 | 30.384365 | 79 | py |
D2Det | D2Det-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... | 9,285 | 30.477966 | 79 | py |
D2Det | D2Det-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... | 8,686 | 30.589091 | 78 | py |
D2Det | D2Det-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=... | 4,387 | 28.449664 | 79 | py |
D2Det | D2Det-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,... | 4,502 | 28.821192 | 79 | py |
D2Det | D2Det-master/configs/reppoints/reppoints_moment_r50_no_gn_fpn_1x.py | # model settings
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='pytorch'),
neck=dict(
type='FPN',
in_chann... | 4,098 | 28.702899 | 79 | py |
D2Det | D2Det-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... | 4,334 | 28.489796 | 79 | py |
D2Det | D2Det-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,... | 4,578 | 28.541935 | 79 | py |
D2Det | D2Det-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=... | 4,254 | 28.755245 | 79 | py |
D2Det | D2Det-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=... | 4,270 | 28.867133 | 79 | py |
D2Det | D2Det-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=... | 4,284 | 28.756944 | 79 | py |
D2Det | D2Det-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... | 4,407 | 29.191781 | 79 | py |
D2Det | D2Det-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=... | 4,255 | 28.762238 | 79 | py |
D2Det | D2Det-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... | 4,483 | 28.893333 | 79 | py |
D2Det | D2Det-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... | 4,258 | 28.783217 | 79 | py |
D2Det | D2Det-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=... | 4,254 | 28.755245 | 79 | py |
D2Det | D2Det-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=... | 4,331 | 28.469388 | 79 | py |
D2Det | D2Det-master/configs/nas_fpn/retinanet_crop640_r50_nasfpn_50e.py | cudnn_benchmark = True
# model settings
norm_cfg = dict(type='BN', requires_grad=True)
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_cf... | 4,247 | 27.32 | 77 | py |
D2Det | D2Det-master/configs/nas_fpn/retinanet_crop640_r50_fpn_50e.py | cudnn_benchmark = True
# model settings
norm_cfg = dict(type='BN', requires_grad=True)
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_cf... | 4,314 | 27.576159 | 77 | py |
D2Det | D2Det-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... | 5,424 | 29.307263 | 78 | py |
D2Det | D2Det-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... | 3,895 | 28.074627 | 77 | py |
D2Det | D2Det-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(... | 5,850 | 29.316062 | 78 | py |
D2Det | D2Det-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(... | 4,027 | 27.567376 | 77 | py |
D2Det | D2Det-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'),
... | 4,040 | 27.864286 | 75 | py |
D2Det | D2Det-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... | 3,947 | 28.029412 | 75 | py |
D2Det | D2Det-master/configs/carafe/faster_rcnn_r50_fpn_carafe_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_CARAFE',
in_chann... | 5,751 | 29.433862 | 78 | py |
D2Det | D2Det-master/configs/carafe/mask_rcnn_r50_fpn_carafe_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_CARAFE',
in_channel... | 6,407 | 29.369668 | 78 | py |
D2Det | D2Det-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... | 6,127 | 29.487562 | 78 | py |
D2Det | D2Det-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'),
... | 6,119 | 29.29703 | 78 | py |
D2Det | D2Det-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... | 6,124 | 29.472637 | 78 | py |
D2Det | D2Det-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... | 5,707 | 30.362637 | 79 | py |
D2Det | D2Det-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... | 5,576 | 29.983333 | 79 | py |
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