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https://github.com/open-mmlab/mmyolo.git
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4b996f10a5
* init * add cfg * update align * update * fix regularize box * fix comment * update config * remove ckpt * update * make mmrotate optional * fix doc * add mmrotate req * support large_demo with rbbox * add ut * update * add doc v01 * update doc * fix doc * update * update * update readme * update comments * fix * fix doc * fix doc * fix * update * update * fix large * update doc * update readme * fix config * fix configs * inprove * update doc * update assigner * update ut * remove rdsl assigner * rename aug config * speedup ut * add comment * fix data root * remove doc * remove empty folder * add docs * rename configs * fix readme * fix readme * fix configs * revert * fix name * fix table * fix doc link * fix doc link * update * update * update * Refactor dota splits * add shapely * fix typo * fix ci * change * fix type * uppdata link * uppdata link * add some comment * update --------- Co-authored-by: huanghaian <huanghaian@sensetime.com>
39 lines
1.4 KiB
Python
39 lines
1.4 KiB
Python
_base_ = './rtmdet-r_l_syncbn_fast_2xb4-36e_dota.py'
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checkpoint = 'https://download.openmmlab.com/mmdetection/v3.0/rtmdet/cspnext_rsb_pretrain/cspnext-tiny_imagenet_600e.pth' # noqa
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# ========================modified parameters======================
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deepen_factor = 0.167
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widen_factor = 0.375
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# Batch size of a single GPU during training
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train_batch_size_per_gpu = 8
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# Submission dir for result submit
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submission_dir = './work_dirs/{{fileBasenameNoExtension}}/submission'
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# =======================Unmodified in most cases==================
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model = dict(
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backbone=dict(
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deepen_factor=deepen_factor,
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widen_factor=widen_factor,
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init_cfg=dict(checkpoint=checkpoint)),
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neck=dict(deepen_factor=deepen_factor, widen_factor=widen_factor),
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bbox_head=dict(head_module=dict(widen_factor=widen_factor)))
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train_dataloader = dict(batch_size=train_batch_size_per_gpu)
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# Inference on test dataset and format the output results
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# for submission. Note: the test set has no annotation.
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# test_dataloader = dict(
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# dataset=dict(
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# data_root=_base_.data_root,
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# ann_file='', # test set has no annotation
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# data_prefix=dict(img_path=_base_.test_data_prefix),
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# pipeline=_base_.test_pipeline))
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# test_evaluator = dict(
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# type='mmrotate.DOTAMetric',
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# format_only=True,
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# merge_patches=True,
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# outfile_prefix=submission_dir)
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