mirror of https://github.com/open-mmlab/mmyolo.git
57 lines
1.9 KiB
Python
57 lines
1.9 KiB
Python
_base_ = 'yolov7_tiny_syncbn_fast_8x16b-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/yolov7/yolov7_tiny_syncbn_fast_8x16b-300e_coco/yolov7_tiny_syncbn_fast_8x16b-300e_coco_20221126_102719-0ee5bbdf.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
|