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# Inherit and overwrite part of the config based on this config
_base_ = './faster-rcnn_r50_fpn_1x_coco.py'
data_root = 'data/' # dataset root
train_batch_size_per_gpu = 16
train_num_workers = 1
max_epochs = 105
base_lr = 0.00001
metainfo = {
'classes': ('orgaquant', ),
'palette': [
(220, 20, 60),
]
}
train_dataloader = dict(
batch_size=train_batch_size_per_gpu,
num_workers=train_num_workers,
dataset=dict(
data_root=data_root,
metainfo=metainfo,
data_prefix=dict(img='train/'),
ann_file='train.json'))
val_dataloader = dict(
dataset=dict(
data_root=data_root,
metainfo=metainfo,
data_prefix=dict(img='val/'),
ann_file='val.json'))
test_dataloader = val_dataloader
val_evaluator = dict(ann_file=data_root + 'val.json')
test_evaluator = val_evaluator
model = dict(
roi_head=dict(
bbox_head=dict(num_classes=1)))
train_pipeline = [
dict(type='LoadImageFromFile', backend_args=None),
dict(type='LoadAnnotations', with_bbox=True),
dict(type='RandomFlip', prob=0.5),
dict(type = 'RandomShift', prob = 0.5),
dict(type = 'RandomAffine'),
dict(type='PhotoMetricDistortion'),
dict(type='PackDetInputs')
]
# optimizer
optim_wrapper = dict(
_delete_=True,
type='OptimWrapper',
optimizer=dict(type='AdamW', lr=base_lr, weight_decay=0.05),
paramwise_cfg=dict(
norm_decay_mult=0, bias_decay_mult=0, bypass_duplicate=True))
default_hooks = dict(
checkpoint=dict(
interval=5,
max_keep_ckpts=2, # only keep latest 2 checkpoints
save_best='auto'
),
logger=dict(type='LoggerHook', interval=5))
# load COCO pre-trained weight
# load_from = './work_dirs/faster-rcnn_r50_fpn_organoid/best_coco_bbox_mAP_epoch_12.pth'
train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=max_epochs, val_interval=1)
visualizer = dict(vis_backends=[dict(type='LocalVisBackend'),dict(type='TensorboardVisBackend')])
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