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Running
on
T4
_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 | |