Commit b320d0ab authored by yhcao6's avatar yhcao6
Browse files

fix

parent 450cfb73
# model settings
input_size = 300
model = dict(
type='SingleStageDetector',
pretrained='data/vgg_backbone.pth',
backbone=dict(
type='SSDVGG',
input_size=300,
input_size=input_size,
depth=16,
with_last_pool=False,
ceil_mode=True,
......@@ -14,7 +15,7 @@ model = dict(
neck=None,
bbox_head=dict(
type='SSDHead',
input_size=300,
input_size=input_size,
in_channels=(512, 1024, 512, 256, 256, 256),
num_classes=21,
anchor_strides=(8, 16, 32, 64, 100, 300),
......@@ -50,6 +51,7 @@ data = dict(
workers_per_gpu=3,
train=dict(
type='RepeatDataset',
times=20,
dataset=dict(
type=dataset_type,
ann_file=[
......@@ -77,8 +79,7 @@ data = dict(
ratio_range=(1, 4)),
random_crop=dict(
min_ious=(0.1, 0.3, 0.5, 0.7, 0.9), min_crop_size=0.3)),
resize_keep_ratio=False),
times=20),
resize_keep_ratio=False)),
val=dict(
type=dataset_type,
ann_file=data_root + 'VOC2007/ImageSets/Main/test.txt',
......
# model settings
input_size = 512
model = dict(
type='SingleStageDetector',
pretrained='data/vgg_backbone.pth',
backbone=dict(
type='SSDVGG',
input_size=512,
input_size=input_size,
depth=16,
with_last_pool=False,
ceil_mode=True,
......@@ -14,7 +15,7 @@ model = dict(
neck=None,
bbox_head=dict(
type='SSDHead',
input_size=512,
input_size=input_size,
in_channels=(512, 1024, 512, 256, 256, 256, 256),
num_classes=81,
anchor_strides=(8, 16, 32, 64, 128, 256, 512),
......@@ -42,18 +43,22 @@ test_cfg = dict(
max_per_img=200)
# model training and testing settings
# dataset settings
dataset_type = 'CocoDataset'
data_root = 'data/coco/'
dataset_type = 'VOCDataset'
data_root = 'data/VOCdevkit/'
img_norm_cfg = dict(mean=[123.675, 116.28, 103.53], std=[1, 1, 1], to_rgb=True)
data = dict(
imgs_per_gpu=8,
workers_per_gpu=3,
train=dict(
type='RepeatDataset',
times=20,
dataset=dict(
type=dataset_type,
ann_file=data_root + 'annotations/instances_train2017.json',
img_prefix=data_root + 'train2017/',
ann_file=[
data_root + 'VOC2007/ImageSets/Main/trainval.txt',
data_root + 'VOC2012/ImageSets/Main/trainval.txt'
],
img_prefix=[data_root + 'VOC2007/', data_root + 'VOC2012/'],
img_scale=(512, 512),
img_norm_cfg=img_norm_cfg,
size_divisor=None,
......@@ -74,12 +79,11 @@ data = dict(
ratio_range=(1, 4)),
random_crop=dict(
min_ious=(0.1, 0.3, 0.5, 0.7, 0.9), min_crop_size=0.3)),
resize_keep_ratio=False),
times=10),
resize_keep_ratio=False)),
val=dict(
type=dataset_type,
ann_file=data_root + 'annotations/instances_val2017.json',
img_prefix=data_root + 'val2017/',
ann_file=data_root + 'VOC2007/ImageSets/Main/test.txt',
img_prefix=data_root + 'VOC2007/',
img_scale=(512, 512),
img_norm_cfg=img_norm_cfg,
size_divisor=None,
......@@ -90,8 +94,8 @@ data = dict(
resize_keep_ratio=False),
test=dict(
type=dataset_type,
ann_file=data_root + 'annotations/instances_val2017.json',
img_prefix=data_root + 'val2017/',
ann_file=data_root + 'VOC2007/ImageSets/Main/test.txt',
img_prefix=data_root + 'VOC2007/',
img_scale=(512, 512),
img_norm_cfg=img_norm_cfg,
size_divisor=None,
......
# model settings
input_size = 300
model = dict(
type='SingleStageDetector',
pretrained='data/vgg_backbone.pth',
backbone=dict(
type='SSDVGG',
input_size=300,
input_size=input_size,
depth=16,
with_last_pool=False,
ceil_mode=True,
......@@ -14,7 +15,7 @@ model = dict(
neck=None,
bbox_head=dict(
type='SSDHead',
input_size=300,
input_size=input_size,
in_channels=(512, 1024, 512, 256, 256, 256),
num_classes=81,
anchor_strides=(8, 16, 32, 64, 100, 300),
......@@ -50,6 +51,7 @@ data = dict(
workers_per_gpu=3,
train=dict(
type='RepeatDataset',
times=10,
dataset=dict(
type=dataset_type,
ann_file=data_root + 'annotations/instances_train2017.json',
......@@ -74,8 +76,7 @@ data = dict(
ratio_range=(1, 4)),
random_crop=dict(
min_ious=(0.1, 0.3, 0.5, 0.7, 0.9), min_crop_size=0.3)),
resize_keep_ratio=False),
times=10),
resize_keep_ratio=False)),
val=dict(
type=dataset_type,
ann_file=data_root + 'annotations/instances_val2017.json',
......
# model settings
input_size = 512
model = dict(
type='SingleStageDetector',
pretrained='data/vgg_backbone.pth',
backbone=dict(
type='SSDVGG',
input_size=512,
input_size=input_size,
depth=16,
with_last_pool=False,
ceil_mode=True,
......@@ -14,7 +15,7 @@ model = dict(
neck=None,
bbox_head=dict(
type='SSDHead',
input_size=512,
input_size=input_size,
in_channels=(512, 1024, 512, 256, 256, 256, 256),
num_classes=81,
anchor_strides=(8, 16, 32, 64, 128, 256, 512),
......@@ -50,6 +51,7 @@ data = dict(
workers_per_gpu=3,
train=dict(
type='RepeatDataset',
times=10,
dataset=dict(
type=dataset_type,
ann_file=data_root + 'annotations/instances_train2017.json',
......@@ -74,8 +76,7 @@ data = dict(
ratio_range=(1, 4)),
random_crop=dict(
min_ious=(0.1, 0.3, 0.5, 0.7, 0.9), min_crop_size=0.3)),
resize_keep_ratio=False),
times=10),
resize_keep_ratio=False)),
val=dict(
type=dataset_type,
ann_file=data_root + 'annotations/instances_val2017.json',
......
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