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_base_ = './yolov6_s_syncbn_fast_8xb32-400e_coco.py'
data_root = './data/cat/'
class_name = ('cat', )
num_classes = len(class_name)
metainfo = dict(classes=class_name, palette=[(20, 220, 60)])
max_epochs = 40
train_batch_size_per_gpu = 12
train_num_workers = 4
num_last_epochs = 5
load_from = 'https://download.openmmlab.com/mmyolo/v0/yolov6/yolov6_s_syncbn_fast_8xb32-400e_coco/yolov6_s_syncbn_fast_8xb32-400e_coco_20221102_203035-932e1d91.pth' # noqa
model = dict(
backbone=dict(frozen_stages=4),
bbox_head=dict(head_module=dict(num_classes=num_classes)),
train_cfg=dict(
initial_assigner=dict(num_classes=num_classes),
assigner=dict(num_classes=num_classes)))
train_dataloader = dict(
batch_size=train_batch_size_per_gpu,
num_workers=train_num_workers,
dataset=dict(
data_root=data_root,
metainfo=metainfo,
ann_file='annotations/trainval.json',
data_prefix=dict(img='images/')))
val_dataloader = dict(
dataset=dict(
metainfo=metainfo,
data_root=data_root,
ann_file='annotations/test.json',
data_prefix=dict(img='images/')))
test_dataloader = val_dataloader
val_evaluator = dict(ann_file=data_root + 'annotations/test.json')
test_evaluator = val_evaluator
_base_.optim_wrapper.optimizer.batch_size_per_gpu = train_batch_size_per_gpu
_base_.custom_hooks[1].switch_epoch = max_epochs - num_last_epochs
default_hooks = dict(
checkpoint=dict(interval=10, max_keep_ckpts=2, save_best='auto'),
# The warmup_mim_iter parameter is critical.
# The default value is 1000 which is not suitable for cat datasets.
param_scheduler=dict(max_epochs=max_epochs, warmup_mim_iter=10),
logger=dict(type='LoggerHook', interval=5))
train_cfg = dict(
max_epochs=max_epochs,
val_interval=10,
dynamic_intervals=[(max_epochs - num_last_epochs, 1)])
# visualizer = dict(vis_backends = [dict(type='LocalVisBackend'), dict(type='WandbVisBackend')]) # noqa
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