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ppyoloe_s_fast_8xb32-300e_coco.py
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ppyoloe_s_fast_8xb32-300e_coco.py
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_base_ = './ppyoloe_plus_s_fast_8xb8-80e_coco.py'
# The pretrained model is geted and converted from official PPYOLOE.
# https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.5/configs/ppyoloe/README.md
checkpoint = 'https://download.openmmlab.com/mmyolo/v0/ppyoloe/ppyoloe_pretrain/cspresnet_s_imagenet1k_pretrained-2be81763.pth' # noqa
train_batch_size_per_gpu = 32
max_epochs = 300
# Base learning rate for optim_wrapper
base_lr = 0.01
model = dict(
data_preprocessor=dict(
mean=[0.485 * 255, 0.456 * 255, 0.406 * 255],
std=[0.229 * 255., 0.224 * 255., 0.225 * 255.]),
backbone=dict(
block_cfg=dict(use_alpha=False),
init_cfg=dict(
type='Pretrained',
prefix='backbone.',
checkpoint=checkpoint,
map_location='cpu')),
train_cfg=dict(initial_epoch=100))
train_dataloader = dict(batch_size=train_batch_size_per_gpu)
optim_wrapper = dict(optimizer=dict(lr=base_lr))
default_hooks = dict(param_scheduler=dict(total_epochs=int(max_epochs * 1.2)))
train_cfg = dict(max_epochs=max_epochs)
# PPYOLOE plus use obj365 pretrained model, but PPYOLOE not,
# `load_from` need to set to None.
load_from = None