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hatched_juvenile_config.py
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hatched_juvenile_config.py
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_base_ = './configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py'
model = dict(
roi_head=dict(
bbox_head=dict(
num_classes=3)),
backbone=dict(
norm_cfg=dict(requires_grad=False),
norm_eval=True,
style='caffe',
init_cfg=dict(
type='Pretrained',
checkpoint='open-mmlab://detectron2/resnet50_caffe')))
# use caffe img_norm
img_norm_cfg = dict(
mean=[103.530, 116.280, 123.675], 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), (1333, 672), (1333, 704), (1333, 736),
(1333, 768), (1333, 800)],
multiscale_mode='value',
keep_ratio=True),
dict(type='RandomFlip', flip_ratio=0.5),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(1333, 800),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='RandomFlip'),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img']),
])
]
val_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(1333, 800),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='RandomFlip'),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img']),
])
]
# learning policy
optimizer = dict(lr = 0.02/8)
lr_config = dict(warmup = None)
log_config = dict(interval=10)
evaluation = dict(metric = ['bbox'],interval = 10)
checkpoint_config = dict(interval = 100)
# classes = ('Juvenile',)
classes = ('Hatched', 'Unhatched', 'Juvenile')
data_root = "./mm_images"
data = dict(
workers_per_gpu=3,
samples_per_gpu=3,
train=dict(
pipeline=train_pipeline,
img_prefix=f'{data_root}/training/images',
classes=classes,
ann_file=f'{data_root}/training/annotations/instances_default_juvenile_unhatched.json'),
val=dict(
pipeline=test_pipeline,
img_prefix=f'{data_root}/training/images',
classes=classes,
ann_file=f'{data_root}/training/annotations/instances_default_juvenile_unhatched.json'),
test=dict(
pipeline=test_pipeline,
img_prefix=f'{data_root}/training/images',
classes=classes,
ann_file=f'{data_root}/training/annotations/instances_default_juvenile_unhatched.json'))
seed = 0
runner = dict(max_epochs=500)
# data_root = '../data/2022_02_14_coco_format_select_vids/'
load_from = './checkpoints/mask_rcnn_r50_caffe_fpn_mstrain-poly_3x_coco_bbox_mAP-0.408__segm_mAP-0.37_20200504_163245-42aa3d00.pth'
work_dir = './work_dir/juvenile_hatched_unhatched_b_3'