Spaces:
Running
on
T4
Running
on
T4
_base_ = './rtmdet-r_l_syncbn_fast_2xb4-36e_dota.py' | |
checkpoint = 'https://download.openmmlab.com/mmdetection/v3.0/rtmdet/cspnext_rsb_pretrain/cspnext-tiny_imagenet_600e.pth' # noqa | |
# ========================modified parameters====================== | |
deepen_factor = 0.167 | |
widen_factor = 0.375 | |
# Batch size of a single GPU during training | |
train_batch_size_per_gpu = 8 | |
# Submission dir for result submit | |
submission_dir = './work_dirs/{{fileBasenameNoExtension}}/submission' | |
# =======================Unmodified in most cases================== | |
model = dict( | |
backbone=dict( | |
deepen_factor=deepen_factor, | |
widen_factor=widen_factor, | |
init_cfg=dict(checkpoint=checkpoint)), | |
neck=dict(deepen_factor=deepen_factor, widen_factor=widen_factor), | |
bbox_head=dict(head_module=dict(widen_factor=widen_factor))) | |
train_dataloader = dict(batch_size=train_batch_size_per_gpu) | |
# Inference on test dataset and format the output results | |
# for submission. Note: the test set has no annotation. | |
# test_dataloader = dict( | |
# dataset=dict( | |
# data_root=_base_.data_root, | |
# ann_file='', # test set has no annotation | |
# data_prefix=dict(img_path=_base_.test_data_prefix), | |
# pipeline=_base_.test_pipeline)) | |
# test_evaluator = dict( | |
# type='mmrotate.DOTAMetric', | |
# format_only=True, | |
# merge_patches=True, | |
# outfile_prefix=submission_dir) | |