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[Config] Update paths to pretrain weights (#1416)

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Tong Gao 2022-09-29 16:26:52 +08:00 committed by GitHub
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5 changed files with 7 additions and 3 deletions
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2
configs/textdet/dbnet/README.md
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@ -19,7 +19,7 @@ Recently, segmentation-based methods are quite popular in scene text detection,
| Method | Pretrained Model | Training set | Test set | #epochs | Test size | Precision | Recall | Hmean | Download |
| :--------------------------------------: | :-------------------------------------------------: | :-------------: | :------------: | :-----: | :-------: | :-------: | :----: | :----: | :-----------------------------------------: |
| [DBNet_r18](/configs/textdet/dbnet/dbnet_resnet18_fpnc_1200e_icdar2015.py) | ImageNet | ICDAR2015 Train | ICDAR2015 Test | 1200 | 736 | 0.8853 | 0.7583 | 0.8169 | [model](https://download.openmmlab.com/mmocr/textdet/dbnet/dbnet_resnet18_fpnc_1200e_icdar2015/dbnet_resnet18_fpnc_1200e_icdar2015_20220825_221614-7c0e94f2.pth) \| [log](https://download.openmmlab.com/mmocr/textdet/dbnet/dbnet_resnet18_fpnc_1200e_icdar2015/20220825_221614.log) |
| [DBNet_r50dcn](/configs/textdet/dbnet/dbnet_resnet50-dcnv2_fpnc_1200e_icdar2015.py) | [Synthtext](https://download.openmmlab.com/mmocr/textdet/dbnet/tmp_1.0_pretrain/dbnet_r50dcnv2_fpnc_sbn_2e_synthtext_20210325-aa96e477.pth) | ICDAR2015 Train | ICDAR2015 Test | 1200 | 1024 | 0.8784 | 0.8315 | 0.8543 | [model](https://download.openmmlab.com/mmocr/textdet/dbnet/dbnet_resnet50-dcnv2_fpnc_1200e_icdar2015/dbnet_resnet50-dcnv2_fpnc_1200e_icdar2015_20220828_124917-452c443c.pth) \| [log](https://download.openmmlab.com/mmocr/textdet/dbnet/dbnet_resnet50-dcnv2_fpnc_1200e_icdar2015/20220828_124917.log) |
| [DBNet_r50dcn](/configs/textdet/dbnet/dbnet_resnet50-dcnv2_fpnc_1200e_icdar2015.py) | [Synthtext](https://download.openmmlab.com/mmocr/textdet/dbnet/tmp_1.0_pretrain/dbnet_r50dcnv2_fpnc_sbn_2e_synthtext_20210325-ed322016.pth) | ICDAR2015 Train | ICDAR2015 Test | 1200 | 1024 | 0.8784 | 0.8315 | 0.8543 | [model](https://download.openmmlab.com/mmocr/textdet/dbnet/dbnet_resnet50-dcnv2_fpnc_1200e_icdar2015/dbnet_resnet50-dcnv2_fpnc_1200e_icdar2015_20220828_124917-452c443c.pth) \| [log](https://download.openmmlab.com/mmocr/textdet/dbnet/dbnet_resnet50-dcnv2_fpnc_1200e_icdar2015/20220828_124917.log) |
## Citation

2
configs/textdet/dbnet/dbnet_resnet50-dcnv2_fpnc_1200e_icdar2015.py
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@ -6,7 +6,7 @@ _base_ = [
]
# TODO: Replace the link
load_from = 'https://download.openmmlab.com/mmocr/textdet/dbnet/dbnet_r50dcnv2_fpnc_sbn_2e_synthtext_20210325-aa96e477.pth' # noqa
load_from = 'https://download.openmmlab.com/mmocr/textdet/dbnet/tmp_1.0_pretrain/dbnet_r50dcnv2_fpnc_sbn_2e_synthtext_20210325-ed322016.pth' # noqa
# dataset settings
ic15_det_train = _base_.ic15_det_train

2
configs/textdet/dbnetpp/README.md
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@ -18,7 +18,7 @@ Recently, segmentation-based scene text detection methods have drawn extensive a
| Method | Pretrained Model | Training set | Test set | #epochs | Test size | Precision | Recall | Hmean | Download |
| :--------------------------------------: | :-------------------------------------------------: | :-------------: | :------------: | :-----: | :-------: | :-------: | :----: | :----: | :-----------------------------------------: |
| [DBNetpp_r50dcn](/configs/textdet/dbnetpp/dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015.py) | [Synthtext](/configs/textdet/dbnetpp/dbnetpp_resnet50-dcnv2_fpnc_100k_synthtext.py) ([model](https://download.openmmlab.com/mmocr/textdet/dbnet/dbnetpp_r50dcnv2_fpnc_100k_iter_synthtext-20220502-db297554.pth)) | ICDAR2015 Train | ICDAR2015 Test | 1200 | 1024 | 0.9116 | 0.8291 | 0.8684 | [model](https://download.openmmlab.com/mmocr/textdet/dbnetpp/dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015/dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108-f289bd20.pth) \| [log](https://download.openmmlab.com/mmocr/textdet/dbnetpp/dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015/20220829_230108.log) |
| [DBNetpp_r50dcn](/configs/textdet/dbnetpp/dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015.py) | [Synthtext](/configs/textdet/dbnetpp/dbnetpp_resnet50-dcnv2_fpnc_100k_synthtext.py) ([model](https://download.openmmlab.com/mmocr/textdet/dbnetpp/tmp_1.0_pretrain/dbnetpp_r50dcnv2_fpnc_100k_iter_synthtext-20220502-352fec8a.pth)) | ICDAR2015 Train | ICDAR2015 Test | 1200 | 1024 | 0.9116 | 0.8291 | 0.8684 | [model](https://download.openmmlab.com/mmocr/textdet/dbnetpp/dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015/dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015_20220829_230108-f289bd20.pth) \| [log](https://download.openmmlab.com/mmocr/textdet/dbnetpp/dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015/20220829_230108.log) |
## Citation

2
configs/textdet/dbnetpp/dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015.py
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@ -5,6 +5,8 @@ _base_ = [
'../_base_/schedules/schedule_sgd_1200e.py',
]
load_from = 'https://download.openmmlab.com/mmocr/textdet/dbnetpp/tmp_1.0_pretrain/dbnetpp_r50dcnv2_fpnc_100k_iter_synthtext-20220502-352fec8a.pth' # noqa
# dataset settings
train_list = [_base_.ic15_det_train]
test_list = [_base_.ic15_det_test]

2
configs/textrecog/abinet/abinet_20e_st-an_mj.py
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@ -12,6 +12,8 @@ _base_ = [
'_base_abinet.py',
]
load_from = 'https://download.openmmlab.com/mmocr/textrecog/abinet/abinet_pretrain-45deac15.pth' # noqa
optim_wrapper = dict(optimizer=dict(lr=1e-4))
train_cfg = dict(max_epochs=20)
# learning policy