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Running
on
T4
File size: 1,932 Bytes
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_base_ = 'yolov5_s-v61_syncbn_fast_8xb16-300e_coco.py'
data_root = './data/cat/'
class_name = ('cat', )
num_classes = len(class_name)
metainfo = dict(classes=class_name, palette=[(20, 220, 60)])
anchors = [
[(68, 69), (154, 91), (143, 162)], # P3/8
[(242, 160), (189, 287), (391, 207)], # P4/16
[(353, 337), (539, 341), (443, 432)] # P5/32
]
max_epochs = 40
train_batch_size_per_gpu = 12
train_num_workers = 4
load_from = 'https://download.openmmlab.com/mmyolo/v0/yolov5/yolov5_s-v61_syncbn_fast_8xb16-300e_coco/yolov5_s-v61_syncbn_fast_8xb16-300e_coco_20220918_084700-86e02187.pth' # noqa
model = dict(
backbone=dict(frozen_stages=4),
bbox_head=dict(
head_module=dict(num_classes=num_classes),
prior_generator=dict(base_sizes=anchors)))
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
_base_.optim_wrapper.optimizer.batch_size_per_gpu = train_batch_size_per_gpu
val_evaluator = dict(ann_file=data_root + 'annotations/test.json')
test_evaluator = val_evaluator
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)
# visualizer = dict(vis_backends = [dict(type='LocalVisBackend'), dict(type='WandbVisBackend')]) # noqa
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