diff --git a/configs/textdet/dbnet/README.md b/configs/textdet/dbnet/README.md index 07c91edba..545bae0ea 100644 --- a/configs/textdet/dbnet/README.md +++ b/configs/textdet/dbnet/README.md @@ -16,24 +16,24 @@ Recently, segmentation-based methods are quite popular in scene text detection, ### SynthText -| Method | Backbone | Training set | #iters | Download | -| :-----------------------------------------------------------------------: | :------: | :----------: | :-----: | :--------------------------------------------------------------------------------------------------: | -| [DBNet_r18](/configs/textdet/dbnet/dbnet_resnet18_fpnc_100k_synthtext.py) | ResNet18 | SynthText | 100,000 | [model](https://download.openmmlab.com/mmocr/textdet/dbnet/dbnet_resnet18_fpnc_100k_synthtext/dbnet_resnet18_fpnc_100k_synthtext-2e9bf392.pth) \| [log](https://download.openmmlab.com/mmocr/textdet/dbnet/dbnet_resnet18_fpnc_100k_synthtext/20221214_150351.log) | +| Method | Backbone | Training set | #iters | Download | Batch Size | +| :-----------------------------------------------------------------------: | :------: | :----------: | :-----: | :----------------------------------------------------------------------------------------: | :--------: | +| [DBNet_r18](/configs/textdet/dbnet/dbnet_resnet18_fpnc_100k_synthtext.py) | ResNet18 | SynthText | 100,000 | [model](https://download.openmmlab.com/mmocr/textdet/dbnet/dbnet_resnet18_fpnc_100k_synthtext/dbnet_resnet18_fpnc_100k_synthtext-2e9bf392.pth) \| [log](https://download.openmmlab.com/mmocr/textdet/dbnet/dbnet_resnet18_fpnc_100k_synthtext/20221214_150351.log) | 1xb16 | ### ICDAR2015 -| Method | Backbone | Pretrained Model | Training set | Test set | #epochs | Test size | Precision | Recall | Hmean | Download | -| :----------------------------: | :------------------------------: | :--------------------------------------: | :-------------: | :------------: | :-----: | :-------: | :-------: | :----: | :----: | :------------------------------: | -| [DBNet_r18](/configs/textdet/dbnet/dbnet_resnet18_fpnc_1200e_icdar2015.py) | ResNet18 | - | 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_r50](/configs/textdet/dbnet/dbnet_resnet50_1200e_icdar2015.py) | ResNet50 | - | ICDAR2015 Train | ICDAR2015 Test | 1200 | 1024 | 0.8744 | 0.8276 | 0.8504 | [model](https://download.openmmlab.com/mmocr/textdet/dbnet/dbnet_resnet50_1200e_icdar2015/dbnet_resnet50_1200e_icdar2015_20221102_115917-54f50589.pth) \| [log](https://download.openmmlab.com/mmocr/textdet/dbnet/dbnet_resnet50_1200e_icdar2015/20221102_115917.log) | -| [DBNet_r50dcn](/configs/textdet/dbnet/dbnet_resnet50-dcnv2_fpnc_1200e_icdar2015.py) | ResNet50-DCN | [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) | -| [DBNet_r50-oclip](/configs/textdet/dbnet/dbnet_resnet50-oclip_1200e_icdar2015.py) | [ResNet50-oCLIP](https://download.openmmlab.com/mmocr/backbone/resnet50-oclip-7ba0c533.pth) | - | ICDAR2015 Train | ICDAR2015 Test | 1200 | 1024 | 0.9052 | 0.8272 | 0.8644 | [model](https://download.openmmlab.com/mmocr/textdet/dbnet/dbnet_resnet50-oclip_1200e_icdar2015/dbnet_resnet50-oclip_1200e_icdar2015_20221102_115917-bde8c87a.pth) \| [log](https://download.openmmlab.com/mmocr/textdet/dbnet/dbnet_resnet50-oclip_1200e_icdar2015/20221102_115917.log) | +| Method | Backbone | Pretrained Model | Training set | Test set | #epochs | Test size | Precision | Recall | Hmean | Download | Batch Size | +| :-------------------------: | :---------------------------: | :------------------------------------: | :-------------: | :------------: | :-----: | :-------: | :-------: | :----: | :----: | :----------------------------: | :--------: | +| [DBNet_r18](/configs/textdet/dbnet/dbnet_resnet18_fpnc_1200e_icdar2015.py) | ResNet18 | - | 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) | 1xb16 | +| [DBNet_r50](/configs/textdet/dbnet/dbnet_resnet50_1200e_icdar2015.py) | ResNet50 | - | ICDAR2015 Train | ICDAR2015 Test | 1200 | 1024 | 0.8744 | 0.8276 | 0.8504 | [model](https://download.openmmlab.com/mmocr/textdet/dbnet/dbnet_resnet50_1200e_icdar2015/dbnet_resnet50_1200e_icdar2015_20221102_115917-54f50589.pth) \| [log](https://download.openmmlab.com/mmocr/textdet/dbnet/dbnet_resnet50_1200e_icdar2015/20221102_115917.log) | 1xb16 | +| [DBNet_r50dcn](/configs/textdet/dbnet/dbnet_resnet50-dcnv2_fpnc_1200e_icdar2015.py) | ResNet50-DCN | [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) | 1xb16 | +| [DBNet_r50-oclip](/configs/textdet/dbnet/dbnet_resnet50-oclip_1200e_icdar2015.py) | [ResNet50-oCLIP](https://download.openmmlab.com/mmocr/backbone/resnet50-oclip-7ba0c533.pth) | - | ICDAR2015 Train | ICDAR2015 Test | 1200 | 1024 | 0.9052 | 0.8272 | 0.8644 | [model](https://download.openmmlab.com/mmocr/textdet/dbnet/dbnet_resnet50-oclip_1200e_icdar2015/dbnet_resnet50-oclip_1200e_icdar2015_20221102_115917-bde8c87a.pth) \| [log](https://download.openmmlab.com/mmocr/textdet/dbnet/dbnet_resnet50-oclip_1200e_icdar2015/20221102_115917.log) | 1xb16 | ### Total Text -| Method | Backbone | Pretrained Model | Training set | Test set | #epochs | Test size | Precision | Recall | Hmean | Download | -| :----------------------------------------------------: | :------: | :--------------: | :-------------: | :------------: | :-----: | :-------: | :-------: | :----: | :----: | :------------------------------------------------------: | -| [DBNet_r18](/configs/textdet/dbnet/dbnet_resnet18_fpnc_1200e_totaltext.py) | ResNet18 | - | Totaltext Train | Totaltext Test | 1200 | 736 | 0.8640 | 0.7770 | 0.8182 | [model](https://download.openmmlab.com/mmocr/textdet/dbnet/dbnet_resnet18_fpnc_1200e_totaltext/dbnet_resnet18_fpnc_1200e_totaltext-3ed3233c.pth) \| [log](https://download.openmmlab.com/mmocr/textdet/dbnet/dbnet_resnet18_fpnc_1200e_totaltext/20221219_201038.log) | +| Method | Backbone | Pretrained Model | Training set | Test set | #epochs | Test size | Precision | Recall | Hmean | Download | Batch Size | +| :-----------------------------------------------: | :------: | :--------------: | :-------------: | :------------: | :-----: | :-------: | :-------: | :----: | :----: | :-------------------------------------------------: | :--------: | +| [DBNet_r18](/configs/textdet/dbnet/dbnet_resnet18_fpnc_1200e_totaltext.py) | ResNet18 | - | Totaltext Train | Totaltext Test | 1200 | 736 | 0.8640 | 0.7770 | 0.8182 | [model](https://download.openmmlab.com/mmocr/textdet/dbnet/dbnet_resnet18_fpnc_1200e_totaltext/dbnet_resnet18_fpnc_1200e_totaltext-3ed3233c.pth) \| [log](https://download.openmmlab.com/mmocr/textdet/dbnet/dbnet_resnet18_fpnc_1200e_totaltext/20221219_201038.log) | 1xb16 | ## Citation diff --git a/configs/textdet/dbnetpp/README.md b/configs/textdet/dbnetpp/README.md index 7f9b668a4..00060a19d 100644 --- a/configs/textdet/dbnetpp/README.md +++ b/configs/textdet/dbnetpp/README.md @@ -16,17 +16,17 @@ Recently, segmentation-based scene text detection methods have drawn extensive a ### SynthText -| Method | BackBone | Training set | #iters | Download | -| :--------------------------------------------------------------------------------: | :------------: | :----------: | :-----: | :-----------------------------------------------------------------------------------: | -| [DBNetpp_r50dcn](/configs/textdet/dbnetpp/dbnetpp_resnet50-dcnv2_fpnc_100k_synthtext.py) | ResNet50-dcnv2 | SynthText | 100,000 | [model](https://download.openmmlab.com/mmocr/textdet/dbnetpp/dbnetpp_resnet50-dcnv2_fpnc_100k_synthtext/dbnetpp_resnet50-dcnv2_fpnc_100k_synthtext-00f0a80b.pth) \| [log](https://download.openmmlab.com/mmocr/textdet/dbnetpp/dbnetpp_resnet50-dcnv2_fpnc_100k_synthtext/20221215_013531.log) | +| Method | BackBone | Training set | #iters | Download | Batch Size | +| :---------------------------------------------------------------------------: | :------------: | :----------: | :-----: | :------------------------------------------------------------------------------: | :--------: | +| [DBNetpp_r50dcn](/configs/textdet/dbnetpp/dbnetpp_resnet50-dcnv2_fpnc_100k_synthtext.py) | ResNet50-dcnv2 | SynthText | 100,000 | [model](https://download.openmmlab.com/mmocr/textdet/dbnetpp/dbnetpp_resnet50-dcnv2_fpnc_100k_synthtext/dbnetpp_resnet50-dcnv2_fpnc_100k_synthtext-00f0a80b.pth) \| [log](https://download.openmmlab.com/mmocr/textdet/dbnetpp/dbnetpp_resnet50-dcnv2_fpnc_100k_synthtext/20221215_013531.log) | 1xb16 | ### ICDAR2015 -| Method | BackBone | Pretrained Model | Training set | Test set | #epochs | Test size | Precision | Recall | Hmean | Download | -| :----------------------------: | :------------------------------: | :--------------------------------------: | :-------------: | :------------: | :-----: | :-------: | :-------: | :----: | :----: | :------------------------------: | -| [DBNetpp_r50](/configs/textdet/dbnetpp/dbnetpp_resnet50_fpnc_1200e_icdar2015.py) | ResNet50 | - | ICDAR2015 Train | ICDAR2015 Test | 1200 | 1024 | 0.9079 | 0.8209 | 0.8622 | [model](https://download.openmmlab.com/mmocr/textdet/dbnetpp/dbnetpp_resnet50_fpnc_1200e_icdar2015/dbnetpp_resnet50_fpnc_1200e_icdar2015_20221025_185550-013730aa.pth) \| [log](https://download.openmmlab.com/mmocr/textdet/dbnetpp/dbnetpp_resnet50_fpnc_1200e_icdar2015/20221025_185550.log) | -| [DBNetpp_r50dcn](/configs/textdet/dbnetpp/dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015.py) | ResNet50-dcnv2 | [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) | -| [DBNetpp_r50-oclip](/configs/textdet/dbnetpp/dbnetpp_resnet50-oclip_fpnc_1200e_icdar2015.py) | [ResNet50-oCLIP](https://download.openmmlab.com/mmocr/backbone/resnet50-oclip-7ba0c533.pth) | - | ICDAR2015 Train | ICDAR2015 Test | 1200 | 1024 | 0.9174 | 0.8609 | 0.8882 | [model](https://download.openmmlab.com/mmocr/textdet/dbnetpp/dbnetpp_resnet50-oclip_fpnc_1200e_icdar2015/dbnetpp_resnet50-oclip_fpnc_1200e_icdar2015_20221101_124139-4ecb39ac.pth) \| [log](https://download.openmmlab.com/mmocr/textdet/dbnetpp/dbnetpp_resnet50-oclip_fpnc_1200e_icdar2015/20221101_124139.log) | +| Method | BackBone | Pretrained Model | Training set | Test set | #epochs | Test size | Precision | Recall | Hmean | Download | Batch Size | +| :-------------------------: | :---------------------------: | :------------------------------------: | :-------------: | :------------: | :-----: | :-------: | :-------: | :----: | :----: | :----------------------------: | :--------: | +| [DBNetpp_r50](/configs/textdet/dbnetpp/dbnetpp_resnet50_fpnc_1200e_icdar2015.py) | ResNet50 | - | ICDAR2015 Train | ICDAR2015 Test | 1200 | 1024 | 0.9079 | 0.8209 | 0.8622 | [model](https://download.openmmlab.com/mmocr/textdet/dbnetpp/dbnetpp_resnet50_fpnc_1200e_icdar2015/dbnetpp_resnet50_fpnc_1200e_icdar2015_20221025_185550-013730aa.pth) \| [log](https://download.openmmlab.com/mmocr/textdet/dbnetpp/dbnetpp_resnet50_fpnc_1200e_icdar2015/20221025_185550.log) | 1xb16 | +| [DBNetpp_r50dcn](/configs/textdet/dbnetpp/dbnetpp_resnet50-dcnv2_fpnc_1200e_icdar2015.py) | ResNet50-dcnv2 | [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) | 1xb16 | +| [DBNetpp_r50-oclip](/configs/textdet/dbnetpp/dbnetpp_resnet50-oclip_fpnc_1200e_icdar2015.py) | [ResNet50-oCLIP](https://download.openmmlab.com/mmocr/backbone/resnet50-oclip-7ba0c533.pth) | - | ICDAR2015 Train | ICDAR2015 Test | 1200 | 1024 | 0.9174 | 0.8609 | 0.8882 | [model](https://download.openmmlab.com/mmocr/textdet/dbnetpp/dbnetpp_resnet50-oclip_fpnc_1200e_icdar2015/dbnetpp_resnet50-oclip_fpnc_1200e_icdar2015_20221101_124139-4ecb39ac.pth) \| [log](https://download.openmmlab.com/mmocr/textdet/dbnetpp/dbnetpp_resnet50-oclip_fpnc_1200e_icdar2015/20221101_124139.log) | 1xb16 | ## Citation diff --git a/configs/textdet/drrg/README.md b/configs/textdet/drrg/README.md index 0a056fc5f..89694e661 100644 --- a/configs/textdet/drrg/README.md +++ b/configs/textdet/drrg/README.md @@ -16,10 +16,10 @@ Arbitrary shape text detection is a challenging task due to the high variety and ### CTW1500 -| Method | BackBone | Pretrained Model | Training set | Test set | #epochs | Test size | Precision | Recall | Hmean | Download | -| :-------------------------------------: | :---------------------------------------: | :--------------: | :-----------: | :----------: | :-----: | :-------: | :-------: | :----: | :----: | :----------------------------------------: | -| [DRRG](/configs/textdet/drrg/drrg_resnet50_fpn-unet_1200e_ctw1500.py) | ResNet50 | - | CTW1500 Train | CTW1500 Test | 1200 | 640 | 0.8775 | 0.8179 | 0.8467 | [model](https://download.openmmlab.com/mmocr/textdet/drrg/drrg_resnet50_fpn-unet_1200e_ctw1500/drrg_resnet50_fpn-unet_1200e_ctw1500_20220827_105233-d5c702dd.pth) \\ [log](https://download.openmmlab.com/mmocr/textdet/drrg/drrg_resnet50_fpn-unet_1200e_ctw1500/20220827_105233.log) | -| [DRRG_r50-oclip](/configs/textdet/drrg/drrg_resnet50-oclip_fpn-unet_1200e_ctw1500.py) | [ResNet50-oCLIP](https://download.openmmlab.com/mmocr/backbone/resnet50-oclip-7ba0c533.pth) | - | CTW1500 Train | CTW1500 Test | 1200 | | | | | [model](<>) \\ [log](<>) | +| Method | BackBone | Pretrained Model | Training set | Test set | #epochs | Test size | Precision | Recall | Hmean | Download | Batch Size | +| :----------------------------------: | :------------------------------------: | :--------------: | :-----------: | :----------: | :-----: | :-------: | :-------: | :----: | :----: | :------------------------------------: | :--------: | +| [DRRG](/configs/textdet/drrg/drrg_resnet50_fpn-unet_1200e_ctw1500.py) | ResNet50 | - | CTW1500 Train | CTW1500 Test | 1200 | 640 | 0.8775 | 0.8179 | 0.8467 | [model](https://download.openmmlab.com/mmocr/textdet/drrg/drrg_resnet50_fpn-unet_1200e_ctw1500/drrg_resnet50_fpn-unet_1200e_ctw1500_20220827_105233-d5c702dd.pth) \\ [log](https://download.openmmlab.com/mmocr/textdet/drrg/drrg_resnet50_fpn-unet_1200e_ctw1500/20220827_105233.log) | 4xb4 | +| [DRRG_r50-oclip](/configs/textdet/drrg/drrg_resnet50-oclip_fpn-unet_1200e_ctw1500.py) | [ResNet50-oCLIP](https://download.openmmlab.com/mmocr/backbone/resnet50-oclip-7ba0c533.pth) | - | CTW1500 Train | CTW1500 Test | 1200 | | | | | [model](<>) \\ [log](<>) | | ## Citation diff --git a/configs/textdet/fcenet/README.md b/configs/textdet/fcenet/README.md index 34beec1e2..c4ed08a1b 100644 --- a/configs/textdet/fcenet/README.md +++ b/configs/textdet/fcenet/README.md @@ -16,23 +16,23 @@ One of the main challenges for arbitrary-shaped text detection is to design a go ### CTW1500 -| Method | Backbone | Pretrained Model | Training set | Test set | #epochs | Test size | Precision | Recall | Hmean | Download | -| :------------------------------------: | :---------------------------------------: | :--------------: | :-----------: | :----------: | :-----: | :---------: | :-------: | :----: | :----: | :---------------------------------------: | -| [FCENet_r50dcn](/configs/textdet/fcenet/fcenet_resnet50-dcnv2_fpn_1500e_ctw1500.py) | ResNet50 + DCNv2 | - | CTW1500 Train | CTW1500 Test | 1500 | (736, 1080) | 0.8689 | 0.8296 | 0.8488 | [model](https://download.openmmlab.com/mmocr/textdet/fcenet/fcenet_resnet50-dcnv2_fpn_1500e_ctw1500/fcenet_resnet50-dcnv2_fpn_1500e_ctw1500_20220825_221510-4d705392.pth) \| [log](https://download.openmmlab.com/mmocr/textdet/fcenet/fcenet_resnet50-dcnv2_fpn_1500e_ctw1500/20220825_221510.log) | -| [FCENet_r50-oclip](/configs/textdet/fcenet/fcenet_resnet50-oclip-dcnv2_fpn_1500e_ctw1500.py) | [ResNet50-oCLIP](https://download.openmmlab.com/mmocr/backbone/resnet50-oclip-7ba0c533.pth) | - | CTW1500 Train | CTW1500 Test | 1500 | (736, 1080) | 0.8383 | 0.801 | 0.8192 | [model](https://download.openmmlab.com/mmocr/textdet/fcenet/fcenet_resnet50-oclip_fpn_1500e_ctw1500/fcenet_resnet50-oclip_fpn_1500e_ctw1500_20221102_121909-101df7e6.pth) \| [log](https://download.openmmlab.com/mmocr/textdet/fcenet/fcenet_resnet50-oclip_fpn_1500e_ctw1500/20221102_121909.log) | +| Method | Backbone | Pretrained Model | Training set | Test set | #epochs | Test size | Precision | Recall | Hmean | Download | Batch Size | +| :---------------------------------: | :-----------------------------------: | :--------------: | :-----------: | :----------: | :-----: | :---------: | :-------: | :----: | :----: | :------------------------------------: | :--------: | +| [FCENet_r50dcn](/configs/textdet/fcenet/fcenet_resnet50-dcnv2_fpn_1500e_ctw1500.py) | ResNet50 + DCNv2 | - | CTW1500 Train | CTW1500 Test | 1500 | (736, 1080) | 0.8689 | 0.8296 | 0.8488 | [model](https://download.openmmlab.com/mmocr/textdet/fcenet/fcenet_resnet50-dcnv2_fpn_1500e_ctw1500/fcenet_resnet50-dcnv2_fpn_1500e_ctw1500_20220825_221510-4d705392.pth) \| [log](https://download.openmmlab.com/mmocr/textdet/fcenet/fcenet_resnet50-dcnv2_fpn_1500e_ctw1500/20220825_221510.log) | 1xb8 | +| [FCENet_r50-oclip](/configs/textdet/fcenet/fcenet_resnet50-oclip-dcnv2_fpn_1500e_ctw1500.py) | [ResNet50-oCLIP](https://download.openmmlab.com/mmocr/backbone/resnet50-oclip-7ba0c533.pth) | - | CTW1500 Train | CTW1500 Test | 1500 | (736, 1080) | 0.8383 | 0.801 | 0.8192 | [model](https://download.openmmlab.com/mmocr/textdet/fcenet/fcenet_resnet50-oclip_fpn_1500e_ctw1500/fcenet_resnet50-oclip_fpn_1500e_ctw1500_20221102_121909-101df7e6.pth) \| [log](https://download.openmmlab.com/mmocr/textdet/fcenet/fcenet_resnet50-oclip_fpn_1500e_ctw1500/20221102_121909.log) | 1xb8 | ### ICDAR2015 -| Method | Backbone | Pretrained Model | Training set | Test set | #epochs | Test size | Precision | Recall | Hmean | Download | -| :---------------------------------------------------: | :------------: | :--------------: | :----------: | :-------: | :-----: | :----------: | :-------: | :----: | :----: | :------------------------------------------------------: | -| [FCENet_r50](/configs/textdet/fcenet/fcenet_resnet50_fpn_1500e_icdar2015.py) | ResNet50 | - | IC15 Train | IC15 Test | 1500 | (2260, 2260) | 0.8243 | 0.8834 | 0.8528 | [model](https://download.openmmlab.com/mmocr/textdet/fcenet/fcenet_resnet50_fpn_1500e_icdar2015/fcenet_resnet50_fpn_1500e_icdar2015_20220826_140941-167d9042.pth) \| [log](https://download.openmmlab.com/mmocr/textdet/fcenet/fcenet_resnet50_fpn_1500e_icdar2015/20220826_140941.log) | -| [FCENet_r50-oclip](/configs/textdet/fcenet/fcenet_resnet50-oclip_fpn_1500e_icdar2015.py) | ResNet50-oCLIP | - | IC15 Train | IC15 Test | 1500 | (2260, 2260) | 0.9176 | 0.8098 | 0.8604 | [model](https://download.openmmlab.com/mmocr/textdet/fcenet/fcenet_resnet50-oclip_fpn_1500e_icdar2015/fcenet_resnet50-oclip_fpn_1500e_icdar2015_20221101_150145-5a6fc412.pth) \| [log](https://download.openmmlab.com/mmocr/textdet/fcenet/fcenet_resnet50-oclip_fpn_1500e_icdar2015/20221101_150145.log) | +| Method | Backbone | Pretrained Model | Training set | Test set | #epochs | Test size | Precision | Recall | Hmean | Download | Batch Size | +| :----------------------------------------------: | :------------: | :--------------: | :----------: | :-------: | :-----: | :----------: | :-------: | :----: | :----: | :-------------------------------------------------: | :--------: | +| [FCENet_r50](/configs/textdet/fcenet/fcenet_resnet50_fpn_1500e_icdar2015.py) | ResNet50 | - | IC15 Train | IC15 Test | 1500 | (2260, 2260) | 0.8243 | 0.8834 | 0.8528 | [model](https://download.openmmlab.com/mmocr/textdet/fcenet/fcenet_resnet50_fpn_1500e_icdar2015/fcenet_resnet50_fpn_1500e_icdar2015_20220826_140941-167d9042.pth) \| [log](https://download.openmmlab.com/mmocr/textdet/fcenet/fcenet_resnet50_fpn_1500e_icdar2015/20220826_140941.log) | 1xb8 | +| [FCENet_r50-oclip](/configs/textdet/fcenet/fcenet_resnet50-oclip_fpn_1500e_icdar2015.py) | ResNet50-oCLIP | - | IC15 Train | IC15 Test | 1500 | (2260, 2260) | 0.9176 | 0.8098 | 0.8604 | [model](https://download.openmmlab.com/mmocr/textdet/fcenet/fcenet_resnet50-oclip_fpn_1500e_icdar2015/fcenet_resnet50-oclip_fpn_1500e_icdar2015_20221101_150145-5a6fc412.pth) \| [log](https://download.openmmlab.com/mmocr/textdet/fcenet/fcenet_resnet50-oclip_fpn_1500e_icdar2015/20221101_150145.log) | 1xb8 | ### Total Text -| Method | Backbone | Pretrained Model | Training set | Test set | #epochs | Test size | Precision | Recall | Hmean | Download | -| :---------------------------------------------------: | :------: | :--------------: | :-------------: | :------------: | :-----: | :---------: | :-------: | :----: | :----: | :-----------------------------------------------------: | -| [FCENet_r50](/configs/textdet/fcenet/fcenet_resnet50_fpn_1500e_totaltext.py) | ResNet50 | - | Totaltext Train | Totaltext Test | 1500 | (1280, 960) | 0.8485 | 0.7810 | 0.8134 | [model](https://download.openmmlab.com/mmocr/textdet/fcenet/fcenet_resnet50_fpn_1500e_totaltext/fcenet_resnet50_fpn_1500e_totaltext-91bd37af.pth) \| [log](https://download.openmmlab.com/mmocr/textdet/fcenet/fcenet_resnet50_fpn_1500e_totaltext/20221219_201107.log) | +| Method | Backbone | Pretrained Model | Training set | Test set | #epochs | Test size | Precision | Recall | Hmean | Download | Batch Size | +| :----------------------------------------------: | :------: | :--------------: | :-------------: | :------------: | :-----: | :---------: | :-------: | :----: | :----: | :------------------------------------------------: | :--------: | +| [FCENet_r50](/configs/textdet/fcenet/fcenet_resnet50_fpn_1500e_totaltext.py) | ResNet50 | - | Totaltext Train | Totaltext Test | 1500 | (1280, 960) | 0.8485 | 0.7810 | 0.8134 | [model](https://download.openmmlab.com/mmocr/textdet/fcenet/fcenet_resnet50_fpn_1500e_totaltext/fcenet_resnet50_fpn_1500e_totaltext-91bd37af.pth) \| [log](https://download.openmmlab.com/mmocr/textdet/fcenet/fcenet_resnet50_fpn_1500e_totaltext/20221219_201107.log) | 1xb8 | ## Citation diff --git a/configs/textdet/maskrcnn/README.md b/configs/textdet/maskrcnn/README.md index d520d7370..d27023d88 100644 --- a/configs/textdet/maskrcnn/README.md +++ b/configs/textdet/maskrcnn/README.md @@ -16,17 +16,17 @@ We present a conceptually simple, flexible, and general framework for object ins ### CTW1500 -| Method | BackBone | Pretrained Model | Training set | Test set | #epochs | Test size | Precision | Recall | Hmean | Download | -| :-------------------------------------: | :---------------------------------------: | :--------------: | :-----------: | :----------: | :-----: | :-------: | :-------: | :----: | :----: | :----------------------------------------: | -| [MaskRCNN](/configs/textdet/maskrcnn/mask-rcnn_resnet50_fpn_160e_ctw1500.py) | - | - | CTW1500 Train | CTW1500 Test | 160 | 1600 | 0.7165 | 0.7776 | 0.7458 | [model](https://download.openmmlab.com/mmocr/textdet/maskrcnn/mask-rcnn_resnet50_fpn_160e_ctw1500/mask-rcnn_resnet50_fpn_160e_ctw1500_20220826_154755-ce68ee8e.pth) \| [log](https://download.openmmlab.com/mmocr/textdet/maskrcnn/mask-rcnn_resnet50_fpn_160e_ctw1500/20220826_154755.log) | -| [MaskRCNN_r50-oclip](/configs/textdet/maskrcnn/mask-rcnn_resnet50-oclip_fpn_160e_ctw1500.py) | [ResNet50-oCLIP](https://download.openmmlab.com/mmocr/backbone/resnet50-oclip-7ba0c533.pth) | - | CTW1500 Train | CTW1500 Test | 160 | 1600 | 0.753 | 0.7593 | 0.7562 | [model](https://download.openmmlab.com/mmocr/textdet/maskrcnn/mask-rcnn_resnet50-oclip_fpn_160e_ctw1500/mask-rcnn_resnet50-oclip_fpn_160e_ctw1500_20221101_154448-6e9e991c.pth) \| [log](https://download.openmmlab.com/mmocr/textdet/maskrcnn/mask-rcnn_resnet50-oclip_fpn_160e_ctw1500/20221101_154448.log) | +| Method | BackBone | Pretrained Model | Training set | Test set | #epochs | Test size | Precision | Recall | Hmean | Download | Batch Size | +| :----------------------------------: | :------------------------------------: | :--------------: | :-----------: | :----------: | :-----: | :-------: | :-------: | :----: | :----: | :------------------------------------: | :--------: | +| [MaskRCNN](/configs/textdet/maskrcnn/mask-rcnn_resnet50_fpn_160e_ctw1500.py) | - | - | CTW1500 Train | CTW1500 Test | 160 | 1600 | 0.7165 | 0.7776 | 0.7458 | [model](https://download.openmmlab.com/mmocr/textdet/maskrcnn/mask-rcnn_resnet50_fpn_160e_ctw1500/mask-rcnn_resnet50_fpn_160e_ctw1500_20220826_154755-ce68ee8e.pth) \| [log](https://download.openmmlab.com/mmocr/textdet/maskrcnn/mask-rcnn_resnet50_fpn_160e_ctw1500/20220826_154755.log) | 1xb8 | +| [MaskRCNN_r50-oclip](/configs/textdet/maskrcnn/mask-rcnn_resnet50-oclip_fpn_160e_ctw1500.py) | [ResNet50-oCLIP](https://download.openmmlab.com/mmocr/backbone/resnet50-oclip-7ba0c533.pth) | - | CTW1500 Train | CTW1500 Test | 160 | 1600 | 0.753 | 0.7593 | 0.7562 | [model](https://download.openmmlab.com/mmocr/textdet/maskrcnn/mask-rcnn_resnet50-oclip_fpn_160e_ctw1500/mask-rcnn_resnet50-oclip_fpn_160e_ctw1500_20221101_154448-6e9e991c.pth) \| [log](https://download.openmmlab.com/mmocr/textdet/maskrcnn/mask-rcnn_resnet50-oclip_fpn_160e_ctw1500/20221101_154448.log) | 1xb8 | ### ICDAR2015 -| Method | BackBone | Pretrained Model | Training set | Test set | #epochs | Test size | Precision | Recall | Hmean | Download | -| :------------------------------------: | :--------------------------------------: | :--------------: | :-------------: | :------------: | :-----: | :-------: | :-------: | :----: | :----: | :--------------------------------------: | -| [MaskRCNN](/configs/textdet/maskrcnn/mask-rcnn_resnet50_fpn_160e_icdar2015.py) | ResNet50 | - | ICDAR2015 Train | ICDAR2015 Test | 160 | 1920 | 0.8644 | 0.7766 | 0.8182 | [model](https://download.openmmlab.com/mmocr/textdet/maskrcnn/mask-rcnn_resnet50_fpn_160e_icdar2015/mask-rcnn_resnet50_fpn_160e_icdar2015_20220826_154808-ff5c30bf.pth) \| [log](https://download.openmmlab.com/mmocr/textdet/maskrcnn/mask-rcnn_resnet50_fpn_160e_icdar2015/20220826_154808.log) | -| [MaskRCNN_r50-oclip](/configs/textdet/maskrcnn/mask-rcnn_resnet50-oclip_fpn_160e_icdar2015.py) | [ResNet50-oCLIP](https://download.openmmlab.com/mmocr/backbone/resnet50-oclip-7ba0c533.pth) | - | ICDAR2015 Train | ICDAR2015 Test | 160 | 1920 | 0.8695 | 0.8339 | 0.8513 | [model](https://download.openmmlab.com/mmocr/textdet/maskrcnn/mask-rcnn_resnet50-oclip_fpn_160e_icdar2015/mask-rcnn_resnet50-oclip_fpn_160e_icdar2015_20221101_131357-a19f7802.pth) \| [log](https://download.openmmlab.com/mmocr/textdet/maskrcnn/mask-rcnn_resnet50-oclip_fpn_160e_icdar2015/20221101_131357.log) | +| Method | BackBone | Pretrained Model | Training set | Test set | #epochs | Test size | Precision | Recall | Hmean | Download | Batch Size | +| :--------------------------------: | :-----------------------------------: | :--------------: | :-------------: | :------------: | :-----: | :-------: | :-------: | :----: | :----: | :-----------------------------------: | :--------: | +| [MaskRCNN](/configs/textdet/maskrcnn/mask-rcnn_resnet50_fpn_160e_icdar2015.py) | ResNet50 | - | ICDAR2015 Train | ICDAR2015 Test | 160 | 1920 | 0.8644 | 0.7766 | 0.8182 | [model](https://download.openmmlab.com/mmocr/textdet/maskrcnn/mask-rcnn_resnet50_fpn_160e_icdar2015/mask-rcnn_resnet50_fpn_160e_icdar2015_20220826_154808-ff5c30bf.pth) \| [log](https://download.openmmlab.com/mmocr/textdet/maskrcnn/mask-rcnn_resnet50_fpn_160e_icdar2015/20220826_154808.log) | 1xb8 | +| [MaskRCNN_r50-oclip](/configs/textdet/maskrcnn/mask-rcnn_resnet50-oclip_fpn_160e_icdar2015.py) | [ResNet50-oCLIP](https://download.openmmlab.com/mmocr/backbone/resnet50-oclip-7ba0c533.pth) | - | ICDAR2015 Train | ICDAR2015 Test | 160 | 1920 | 0.8695 | 0.8339 | 0.8513 | [model](https://download.openmmlab.com/mmocr/textdet/maskrcnn/mask-rcnn_resnet50-oclip_fpn_160e_icdar2015/mask-rcnn_resnet50-oclip_fpn_160e_icdar2015_20221101_131357-a19f7802.pth) \| [log](https://download.openmmlab.com/mmocr/textdet/maskrcnn/mask-rcnn_resnet50-oclip_fpn_160e_icdar2015/20221101_131357.log) | 1xb8 | ## Citation diff --git a/configs/textdet/panet/README.md b/configs/textdet/panet/README.md index 571539057..6be3e0596 100644 --- a/configs/textdet/panet/README.md +++ b/configs/textdet/panet/README.md @@ -16,15 +16,15 @@ Scene text detection, an important step of scene text reading systems, has witne ### CTW1500 -| Method | Pretrained Model | Training set | Test set | #epochs | Test size | Precision | Recall | Hmean | Download | -| :----------------------------------------------------------: | :--------------: | :-----------: | :----------: | :-----: | :-------: | :-------: | :----: | :----: | :------------------------------------------------------------: | -| [PANet](/configs/textdet/panet/panet_resnet18_fpem-ffm_600e_ctw1500.py) | ImageNet | CTW1500 Train | CTW1500 Test | 600 | 640 | 0.8208 | 0.7376 | 0.7770 | [model](https://download.openmmlab.com/mmocr/textdet/panet/panet_resnet18_fpem-ffm_600e_ctw1500/panet_resnet18_fpem-ffm_600e_ctw1500_20220826_144818-980f32d0.pth) \| [log](https://download.openmmlab.com/mmocr/textdet/panet/panet_resnet18_fpem-ffm_600e_ctw1500/20220826_144818.log) | +| Method | Pretrained Model | Training set | Test set | #epochs | Test size | Precision | Recall | Hmean | Download | Batch Size | +| :-----------------------------------------------------: | :--------------: | :-----------: | :----------: | :-----: | :-------: | :-------: | :----: | :----: | :-------------------------------------------------------: | :--------: | +| [PANet](/configs/textdet/panet/panet_resnet18_fpem-ffm_600e_ctw1500.py) | ImageNet | CTW1500 Train | CTW1500 Test | 600 | 640 | 0.8208 | 0.7376 | 0.7770 | [model](https://download.openmmlab.com/mmocr/textdet/panet/panet_resnet18_fpem-ffm_600e_ctw1500/panet_resnet18_fpem-ffm_600e_ctw1500_20220826_144818-980f32d0.pth) \| [log](https://download.openmmlab.com/mmocr/textdet/panet/panet_resnet18_fpem-ffm_600e_ctw1500/20220826_144818.log) | 1xb16 | ### ICDAR2015 -| Method | Pretrained Model | Training set | Test set | #epochs | Test size | Precision | Recall | Hmean | Download | -| :--------------------------------------------------------: | :--------------: | :-------------: | :------------: | :-----: | :-------: | :-------: | :----: | :----: | :----------------------------------------------------------: | -| [PANet](/configs/textdet/panet/panet_resnet18_fpem-ffm_600e_icdar2015.py) | ImageNet | ICDAR2015 Train | ICDAR2015 Test | 600 | 736 | 0.8455 | 0.7323 | 0.7848 | [model](https://download.openmmlab.com/mmocr/textdet/panet/panet_resnet18_fpem-ffm_600e_icdar2015/panet_resnet18_fpem-ffm_600e_icdar2015_20220826_144817-be2acdb4.pth) \| [log](https://download.openmmlab.com/mmocr/textdet/panet/panet_resnet18_fpem-ffm_600e_icdar2015/20220826_144817.log) | +| Method | Pretrained Model | Training set | Test set | #epochs | Test size | Precision | Recall | Hmean | Download | Batch Size | +| :---------------------------------------------------: | :--------------: | :-------------: | :------------: | :-----: | :-------: | :-------: | :----: | :----: | :-----------------------------------------------------: | :--------: | +| [PANet](/configs/textdet/panet/panet_resnet18_fpem-ffm_600e_icdar2015.py) | ImageNet | ICDAR2015 Train | ICDAR2015 Test | 600 | 736 | 0.8455 | 0.7323 | 0.7848 | [model](https://download.openmmlab.com/mmocr/textdet/panet/panet_resnet18_fpem-ffm_600e_icdar2015/panet_resnet18_fpem-ffm_600e_icdar2015_20220826_144817-be2acdb4.pth) \| [log](https://download.openmmlab.com/mmocr/textdet/panet/panet_resnet18_fpem-ffm_600e_icdar2015/20220826_144817.log) | 1xb16 | ## Citation diff --git a/configs/textdet/psenet/README.md b/configs/textdet/psenet/README.md index b389f71f8..56c559ec6 100644 --- a/configs/textdet/psenet/README.md +++ b/configs/textdet/psenet/README.md @@ -16,17 +16,17 @@ Scene text detection has witnessed rapid progress especially with the recent dev ### CTW1500 -| Method | Backbone | Pretrained Model | Training set | Test set | #epochs | Test size | Precision | Recall | Hmean | Download | -| :-------------------------------------: | :---------------------------------------: | :--------------: | :-----------: | :----------: | :-----: | :-------: | :-------: | :----: | :----: | :----------------------------------------: | -| [PSENet](/configs/textdet/psenet/psenet_resnet50_fpnf_600e_ctw1500.py) | ResNet50 | - | CTW1500 Train | CTW1500 Test | 600 | 1280 | 0.7705 | 0.7883 | 0.7793 | [model](https://download.openmmlab.com/mmocr/textdet/psenet/psenet_resnet50_fpnf_600e_ctw1500/psenet_resnet50_fpnf_600e_ctw1500_20220825_221459-7f974ac8.pth) \| [log](https://download.openmmlab.com/mmocr/textdet/psenet/psenet_resnet50_fpnf_600e_ctw1500/20220825_221459.log) | -| [PSENet_r50-oclip](/configs/textdet/psenet/psenet_resnet50-oclip_fpnf_600e_ctw1500.py) | [ResNet50-oCLIP](https://download.openmmlab.com/mmocr/backbone/resnet50-oclip-7ba0c533.pth) | - | CTW1500 Train | CTW1500 Test | 600 | 1280 | 0.8483 | 0.7636 | 0.8037 | [model](https://download.openmmlab.com/mmocr/textdet/psenet/psenet_resnet50-oclip_fpnf_600e_ctw1500/psenet_resnet50-oclip_fpnf_600e_ctw1500_20221101_140406-d431710d.pth) \| [log](https://download.openmmlab.com/mmocr/textdet/psenet/psenet_resnet50-oclip_fpnf_600e_ctw1500/20221101_140406.log) | +| Method | Backbone | Pretrained Model | Training set | Test set | #epochs | Test size | Precision | Recall | Hmean | Download | Batch Size | +| :----------------------------------: | :------------------------------------: | :--------------: | :-----------: | :----------: | :-----: | :-------: | :-------: | :----: | :----: | :------------------------------------: | :--------: | +| [PSENet](/configs/textdet/psenet/psenet_resnet50_fpnf_600e_ctw1500.py) | ResNet50 | - | CTW1500 Train | CTW1500 Test | 600 | 1280 | 0.7705 | 0.7883 | 0.7793 | [model](https://download.openmmlab.com/mmocr/textdet/psenet/psenet_resnet50_fpnf_600e_ctw1500/psenet_resnet50_fpnf_600e_ctw1500_20220825_221459-7f974ac8.pth) \| [log](https://download.openmmlab.com/mmocr/textdet/psenet/psenet_resnet50_fpnf_600e_ctw1500/20220825_221459.log) | 16xb16 | +| [PSENet_r50-oclip](/configs/textdet/psenet/psenet_resnet50-oclip_fpnf_600e_ctw1500.py) | [ResNet50-oCLIP](https://download.openmmlab.com/mmocr/backbone/resnet50-oclip-7ba0c533.pth) | - | CTW1500 Train | CTW1500 Test | 600 | 1280 | 0.8483 | 0.7636 | 0.8037 | [model](https://download.openmmlab.com/mmocr/textdet/psenet/psenet_resnet50-oclip_fpnf_600e_ctw1500/psenet_resnet50-oclip_fpnf_600e_ctw1500_20221101_140406-d431710d.pth) \| [log](https://download.openmmlab.com/mmocr/textdet/psenet/psenet_resnet50-oclip_fpnf_600e_ctw1500/20221101_140406.log) | 16xb16 | ### ICDAR2015 -| Method | Backbone | Pretrained Model | Training set | Test set | #epochs | Test size | Precision | Recall | Hmean | Download | -| :--------------------------------------: | :-----------------------------------------: | :--------------: | :----------: | :-------: | :-----: | :-------: | :-------: | :----: | :----: | :-----------------------------------------: | -| [PSENet](/configs/textdet/psenet/psenet_resnet50_fpnf_600e_icdar2015.py) | ResNet50 | - | IC15 Train | IC15 Test | 600 | 2240 | 0.8396 | 0.7636 | 0.7998 | [model](https://download.openmmlab.com/mmocr/textdet/psenet/psenet_resnet50_fpnf_600e_icdar2015/psenet_resnet50_fpnf_600e_icdar2015_20220825_222709-b6741ec3.pth) \| [log](https://download.openmmlab.com/mmocr/textdet/psenet/psenet_resnet50_fpnf_600e_icdar2015/20220825_222709.log) | -| [PSENet_r50-oclip](/configs/textdet/psenet/psenet_resnet50-oclip_fpnf_600e_icdar2015.py) | [ResNet50-oCLIP](https://download.openmmlab.com/mmocr/backbone/resnet50-oclip-7ba0c533.pth) | - | IC15 Train | IC15 Test | 600 | 2240 | 0.8895 | 0.8098 | 0.8478 | [model](https://download.openmmlab.com/mmocr/textdet/psenet/psenet_resnet50-oclip_fpnf_600e_icdar2015/psenet_resnet50-oclip_fpnf_600e_icdar2015_20221101_131357-2bdca389.pth) \| [log](https://download.openmmlab.com/mmocr/textdet/psenet/psenet_resnet50-oclip_fpnf_600e_icdar2015/20221101_131357.log) | +| Method | Backbone | Pretrained Model | Training set | Test set | #epochs | Test size | Precision | Recall | Hmean | Download | Batch Size | +| :-----------------------------------: | :-------------------------------------: | :--------------: | :----------: | :-------: | :-----: | :-------: | :-------: | :----: | :----: | :--------------------------------------: | :--------: | +| [PSENet](/configs/textdet/psenet/psenet_resnet50_fpnf_600e_icdar2015.py) | ResNet50 | - | IC15 Train | IC15 Test | 600 | 2240 | 0.8396 | 0.7636 | 0.7998 | [model](https://download.openmmlab.com/mmocr/textdet/psenet/psenet_resnet50_fpnf_600e_icdar2015/psenet_resnet50_fpnf_600e_icdar2015_20220825_222709-b6741ec3.pth) \| [log](https://download.openmmlab.com/mmocr/textdet/psenet/psenet_resnet50_fpnf_600e_icdar2015/20220825_222709.log) | 16xb16 | +| [PSENet_r50-oclip](/configs/textdet/psenet/psenet_resnet50-oclip_fpnf_600e_icdar2015.py) | [ResNet50-oCLIP](https://download.openmmlab.com/mmocr/backbone/resnet50-oclip-7ba0c533.pth) | - | IC15 Train | IC15 Test | 600 | 2240 | 0.8895 | 0.8098 | 0.8478 | [model](https://download.openmmlab.com/mmocr/textdet/psenet/psenet_resnet50-oclip_fpnf_600e_icdar2015/psenet_resnet50-oclip_fpnf_600e_icdar2015_20221101_131357-2bdca389.pth) \| [log](https://download.openmmlab.com/mmocr/textdet/psenet/psenet_resnet50-oclip_fpnf_600e_icdar2015/20221101_131357.log) | 16xb16 | ## Citation diff --git a/configs/textdet/textsnake/README.md b/configs/textdet/textsnake/README.md index e1cd5d39d..cfb8d8f84 100644 --- a/configs/textdet/textsnake/README.md +++ b/configs/textdet/textsnake/README.md @@ -16,10 +16,10 @@ Driven by deep neural networks and large scale datasets, scene text detection me ### CTW1500 -| Method | BackBone | Pretrained Model | Training set | Test set | #epochs | Test size | Precision | Recall | Hmean | Download | -| :-------------------------------------: | :---------------------------------------: | :--------------: | :-----------: | :----------: | :-----: | :-------: | :-------: | :----: | :----: | :----------------------------------------: | -| [TextSnake](/configs/textdet/textsnake/textsnake_resnet50_fpn-unet_1200e_ctw1500.py) | ResNet50 | - | CTW1500 Train | CTW1500 Test | 1200 | 736 | 0.8535 | 0.8052 | 0.8286 | [model](https://download.openmmlab.com/mmocr/textdet/textsnake/textsnake_resnet50_fpn-unet_1200e_ctw1500/textsnake_resnet50_fpn-unet_1200e_ctw1500_20220825_221459-c0b6adc4.pth) \| [log](https://download.openmmlab.com/mmocr/textdet/textsnake/textsnake_resnet50_fpn-unet_1200e_ctw1500/20220825_221459.log) | -| [TextSnake_r50-oclip](/configs/textdet/textsnake/textsnake_resnet50-oclip_fpn-unet_1200e_ctw1500.py) | [ResNet50-oCLIP](https://download.openmmlab.com/mmocr/backbone/resnet50-oclip-7ba0c533.pth) | - | CTW1500 Train | CTW1500 Test | 1200 | 736 | 0.8869 | 0.8215 | 0.8529 | [model](https://download.openmmlab.com/mmocr/textdet/textsnake/textsnake_resnet50-oclip_fpn-unet_1200e_ctw1500/textsnake_resnet50-oclip_fpn-unet_1200e_ctw1500_20221101_134814-a216e5b2.pth) \| [log](https://download.openmmlab.com/mmocr/textdet/textsnake/textsnake_resnet50-oclip_fpn-unet_1200e_ctw1500/20221101_134814.log) | +| Method | BackBone | Pretrained Model | Training set | Test set | #epochs | Test size | Precision | Recall | Hmean | Download | Batch Size | +| :----------------------------------: | :------------------------------------: | :--------------: | :-----------: | :----------: | :-----: | :-------: | :-------: | :----: | :----: | :------------------------------------: | :--------: | +| [TextSnake](/configs/textdet/textsnake/textsnake_resnet50_fpn-unet_1200e_ctw1500.py) | ResNet50 | - | CTW1500 Train | CTW1500 Test | 1200 | 736 | 0.8535 | 0.8052 | 0.8286 | [model](https://download.openmmlab.com/mmocr/textdet/textsnake/textsnake_resnet50_fpn-unet_1200e_ctw1500/textsnake_resnet50_fpn-unet_1200e_ctw1500_20220825_221459-c0b6adc4.pth) \| [log](https://download.openmmlab.com/mmocr/textdet/textsnake/textsnake_resnet50_fpn-unet_1200e_ctw1500/20220825_221459.log) | 1xb4 | +| [TextSnake_r50-oclip](/configs/textdet/textsnake/textsnake_resnet50-oclip_fpn-unet_1200e_ctw1500.py) | [ResNet50-oCLIP](https://download.openmmlab.com/mmocr/backbone/resnet50-oclip-7ba0c533.pth) | - | CTW1500 Train | CTW1500 Test | 1200 | 736 | 0.8869 | 0.8215 | 0.8529 | [model](https://download.openmmlab.com/mmocr/textdet/textsnake/textsnake_resnet50-oclip_fpn-unet_1200e_ctw1500/textsnake_resnet50-oclip_fpn-unet_1200e_ctw1500_20221101_134814-a216e5b2.pth) \| [log](https://download.openmmlab.com/mmocr/textdet/textsnake/textsnake_resnet50-oclip_fpn-unet_1200e_ctw1500/20221101_134814.log) | 1xb4 | ## Citation diff --git a/configs/textrecog/abinet/README.md b/configs/textrecog/abinet/README.md index 6a7faadb3..d0b584181 100644 --- a/configs/textrecog/abinet/README.md +++ b/configs/textrecog/abinet/README.md @@ -34,13 +34,13 @@ Linguistic knowledge is of great benefit to scene text recognition. However, how ## Results and models -| methods | pretrained | | Regular Text | | | Irregular Text | | download | -| :--------------------------------------------: | :------------------------------------------------: | :----: | :----------: | :-------: | :-------: | :------------: | :----: | :----------------------------------------------- | -| | | IIIT5K | SVT | IC13-1015 | IC15-2077 | SVTP | CT80 | | -| [ABINet-Vision](/configs/textrecog/abinet/abinet-vision_20e_st-an_mj.py) | - | 0.9523 | 0.9196 | 0.9369 | 0.7896 | 0.8403 | 0.8437 | [model](https://download.openmmlab.com/mmocr/textrecog/abinet/abinet-vision_20e_st-an_mj/abinet-vision_20e_st-an_mj_20220915_152445-85cfb03d.pth) \| [log](https://download.openmmlab.com/mmocr/textrecog/abinet/abinet-vision_20e_st-an_mj/20220915_152445.log) | -| [ABINet-Vision-TTA](/configs/textrecog/abinet/abinet-vision_20e_st-an_mj.py) | - | 0.9523 | 0.9196 | 0.9360 | 0.8175 | 0.8450 | 0.8542 | | -| [ABINet](/configs/textrecog/abinet/abinet_20e_st-an_mj.py) | [Pretrained](https://download.openmmlab.com/mmocr/textrecog/abinet/abinet_pretrain-45deac15.pth) | 0.9603 | 0.9397 | 0.9557 | 0.8146 | 0.8868 | 0.8785 | [model](https://download.openmmlab.com/mmocr/textrecog/abinet/abinet_20e_st-an_mj/abinet_20e_st-an_mj_20221005_012617-ead8c139.pth) \| [log](https://download.openmmlab.com/mmocr/textrecog/abinet/abinet_20e_st-an_mj/20221005_012617.log) | -| [ABINet-TTA](/configs/textrecog/abinet/abinet_20e_st-an_mj.py) | [Pretrained](https://download.openmmlab.com/mmocr/textrecog/abinet/abinet_pretrain-45deac15.pth) | 0.9597 | 0.9397 | 0.9527 | 0.8426 | 0.8930 | 0.8854 | | +| methods | pretrained | | Regular Text | | | Irregular Text | | download | Batch Size | +| :-----------------------------------------: | :--------------------------------------------: | :----: | :----------: | :-------: | :-------: | :------------: | :----: | :-------------------------------------------- | :--------: | +| | | IIIT5K | SVT | IC13-1015 | IC15-2077 | SVTP | CT80 | | | +| [ABINet-Vision](/configs/textrecog/abinet/abinet-vision_20e_st-an_mj.py) | - | 0.9523 | 0.9196 | 0.9369 | 0.7896 | 0.8403 | 0.8437 | [model](https://download.openmmlab.com/mmocr/textrecog/abinet/abinet-vision_20e_st-an_mj/abinet-vision_20e_st-an_mj_20220915_152445-85cfb03d.pth) \| [log](https://download.openmmlab.com/mmocr/textrecog/abinet/abinet-vision_20e_st-an_mj/20220915_152445.log) | 8xb192 | +| [ABINet-Vision-TTA](/configs/textrecog/abinet/abinet-vision_20e_st-an_mj.py) | - | 0.9523 | 0.9196 | 0.9360 | 0.8175 | 0.8450 | 0.8542 | | 8xb192 | +| [ABINet](/configs/textrecog/abinet/abinet_20e_st-an_mj.py) | [Pretrained](https://download.openmmlab.com/mmocr/textrecog/abinet/abinet_pretrain-45deac15.pth) | 0.9603 | 0.9397 | 0.9557 | 0.8146 | 0.8868 | 0.8785 | [model](https://download.openmmlab.com/mmocr/textrecog/abinet/abinet_20e_st-an_mj/abinet_20e_st-an_mj_20221005_012617-ead8c139.pth) \| [log](https://download.openmmlab.com/mmocr/textrecog/abinet/abinet_20e_st-an_mj/20221005_012617.log) | 8xb192 | +| [ABINet-TTA](/configs/textrecog/abinet/abinet_20e_st-an_mj.py) | [Pretrained](https://download.openmmlab.com/mmocr/textrecog/abinet/abinet_pretrain-45deac15.pth) | 0.9597 | 0.9397 | 0.9527 | 0.8426 | 0.8930 | 0.8854 | | 8xb192 | ```{note} 1. ABINet allows its encoder to run and be trained without decoder and fuser. Its encoder is designed to recognize texts as a stand-alone model and therefore can work as an independent text recognizer. We release it as ABINet-Vision. diff --git a/configs/textrecog/aster/README.md b/configs/textrecog/aster/README.md index 0e795b7eb..5beb93d10 100644 --- a/configs/textrecog/aster/README.md +++ b/configs/textrecog/aster/README.md @@ -34,11 +34,11 @@ A challenging aspect of scene text recognition is to handle text with distortion ## Results and models -| Methods | Backbone | | Regular Text | | | | Irregular Text | | download | -| :--------------------------------------------------------------: | :------: | :----: | :----------: | :-------: | :-: | :-------: | :------------: | :----: | :-------------------------------------------------------------------: | -| | | IIIT5K | SVT | IC13-1015 | | IC15-2077 | SVTP | CT80 | | -| [ASTER](/configs/textrecog/aster/aster_resnet45_6e_st_mj.py) | ResNet45 | 0.9357 | 0.8949 | 0.9281 | | 0.7665 | 0.8062 | 0.8507 | [model](https://download.openmmlab.com/mmocr/textrecog/aster/aster_resnet45_6e_st_mj/aster_resnet45_6e_st_mj-cc56eca4.pth) \| [log](https://download.openmmlab.com/mmocr/textrecog/aster/aster_resnet45_6e_st_mj/20221214_232605.log) | -| [ASTER-TTA](/configs/textrecog/aster/aster_resnet45_6e_st_mj.py) | ResNet45 | 0.9337 | 0.8949 | 0.9251 | | 0.7925 | 0.8109 | 0.8507 | | +| Methods | Backbone | | Regular Text | | | | Irregular Text | | download | Batch Size | +| :-----------------------------------------------------------: | :------: | :----: | :----------: | :-------: | :-: | :-------: | :------------: | :----: | :------------------------------------------------------------: | :--------: | +| | | IIIT5K | SVT | IC13-1015 | | IC15-2077 | SVTP | CT80 | | | +| [ASTER](/configs/textrecog/aster/aster_resnet45_6e_st_mj.py) | ResNet45 | 0.9357 | 0.8949 | 0.9281 | | 0.7665 | 0.8062 | 0.8507 | [model](https://download.openmmlab.com/mmocr/textrecog/aster/aster_resnet45_6e_st_mj/aster_resnet45_6e_st_mj-cc56eca4.pth) \| [log](https://download.openmmlab.com/mmocr/textrecog/aster/aster_resnet45_6e_st_mj/20221214_232605.log) | | +| [ASTER-TTA](/configs/textrecog/aster/aster_resnet45_6e_st_mj.py) | ResNet45 | 0.9337 | 0.8949 | 0.9251 | | 0.7925 | 0.8109 | 0.8507 | | 1xb1024 | ## Citation diff --git a/configs/textrecog/crnn/README.md b/configs/textrecog/crnn/README.md index 0cac7756d..434a01b2d 100644 --- a/configs/textrecog/crnn/README.md +++ b/configs/textrecog/crnn/README.md @@ -33,11 +33,11 @@ Image-based sequence recognition has been a long-standing research topic in comp ## Results and models -| methods | | Regular Text | | | | Irregular Text | | download | -| :--------------------------------------------------------: | :----: | :----------: | :-------: | :-: | :-------: | :------------: | :----: | :---------------------------------------------------------------------------------: | -| methods | IIIT5K | SVT | IC13-1015 | | IC15-2077 | SVTP | CT80 | | -| [CRNN](/configs/textrecog/crnn/crnn_mini-vgg_5e_mj.py) | 0.8053 | 0.7991 | 0.8739 | | 0.5571 | 0.6093 | 0.5694 | [model](https://download.openmmlab.com/mmocr/textrecog/crnn/crnn_mini-vgg_5e_mj/crnn_mini-vgg_5e_mj_20220826_224120-8afbedbb.pth) \| [log](https://download.openmmlab.com/mmocr/textrecog/crnn/crnn_mini-vgg_5e_mj/20220826_224120.log) | -| [CRNN-TTA](/configs/textrecog/crnn/crnn_mini-vgg_5e_mj.py) | 0.8013 | 0.7975 | 0.8631 | | 0.5763 | 0.6093 | 0.5764 | [model](https://download.openmmlab.com/mmocr/textrecog/crnn/crnn_mini-vgg_5e_mj/crnn_mini-vgg_5e_mj_20220826_224120-8afbedbb.pth) \| [log](https://download.openmmlab.com/mmocr/textrecog/crnn/crnn_mini-vgg_5e_mj/20220826_224120.log) | +| methods | | Regular Text | | | | Irregular Text | | download | Batch Size | +| :--------------------------------------------------------: | :----: | :----------: | :-------: | :-: | :-------: | :------------: | :----: | :-----------------------------------------------------------------------: | :--------: | +| methods | IIIT5K | SVT | IC13-1015 | | IC15-2077 | SVTP | CT80 | | | +| [CRNN](/configs/textrecog/crnn/crnn_mini-vgg_5e_mj.py) | 0.8053 | 0.7991 | 0.8739 | | 0.5571 | 0.6093 | 0.5694 | [model](https://download.openmmlab.com/mmocr/textrecog/crnn/crnn_mini-vgg_5e_mj/crnn_mini-vgg_5e_mj_20220826_224120-8afbedbb.pth) \| [log](https://download.openmmlab.com/mmocr/textrecog/crnn/crnn_mini-vgg_5e_mj/20220826_224120.log) | | +| [CRNN-TTA](/configs/textrecog/crnn/crnn_mini-vgg_5e_mj.py) | 0.8013 | 0.7975 | 0.8631 | | 0.5763 | 0.6093 | 0.5764 | [model](https://download.openmmlab.com/mmocr/textrecog/crnn/crnn_mini-vgg_5e_mj/crnn_mini-vgg_5e_mj_20220826_224120-8afbedbb.pth) \| [log](https://download.openmmlab.com/mmocr/textrecog/crnn/crnn_mini-vgg_5e_mj/20220826_224120.log) | 4xb64 | ## Citation diff --git a/configs/textrecog/master/README.md b/configs/textrecog/master/README.md index 4f9fbcc29..3ccd3df18 100644 --- a/configs/textrecog/master/README.md +++ b/configs/textrecog/master/README.md @@ -35,11 +35,11 @@ Attention-based scene text recognizers have gained huge success, which leverages ## Results and Models -| Methods | Backbone | | Regular Text | | | | Irregular Text | | download | -| :-------------------------------------------------------------: | :-----------: | :----: | :----------: | :-------: | :-: | :-------: | :------------: | :----: | :---------------------------------------------------------------: | -| | | IIIT5K | SVT | IC13-1015 | | IC15-2077 | SVTP | CT80 | | -| [MASTER](/configs/textrecog/master/master_resnet31_12e_st_mj_sa.py) | R31-GCAModule | 0.9490 | 0.8887 | 0.9517 | | 0.7650 | 0.8465 | 0.8889 | [model](https://download.openmmlab.com/mmocr/textrecog/master/master_resnet31_12e_st_mj_sa/master_resnet31_12e_st_mj_sa_20220915_152443-f4a5cabc.pth) \| [log](https://download.openmmlab.com/mmocr/textrecog/master/master_resnet31_12e_st_mj_sa/20220915_152443.log) | -| [MASTER-TTA](/configs/textrecog/master/master_resnet31_12e_st_mj_sa.py) | R31-GCAModule | 0.9450 | 0.8887 | 0.9478 | | 0.7906 | 0.8481 | 0.8958 | | +| Methods | Backbone | | Regular Text | | | | Irregular Text | | download | Batch Size | +| :--------------------------------------------------------: | :-----------: | :----: | :----------: | :-------: | :-: | :-------: | :------------: | :----: | :----------------------------------------------------------: | :--------: | +| | | IIIT5K | SVT | IC13-1015 | | IC15-2077 | SVTP | CT80 | | | +| [MASTER](/configs/textrecog/master/master_resnet31_12e_st_mj_sa.py) | R31-GCAModule | 0.9490 | 0.8887 | 0.9517 | | 0.7650 | 0.8465 | 0.8889 | [model](https://download.openmmlab.com/mmocr/textrecog/master/master_resnet31_12e_st_mj_sa/master_resnet31_12e_st_mj_sa_20220915_152443-f4a5cabc.pth) \| [log](https://download.openmmlab.com/mmocr/textrecog/master/master_resnet31_12e_st_mj_sa/20220915_152443.log) | | +| [MASTER-TTA](/configs/textrecog/master/master_resnet31_12e_st_mj_sa.py) | R31-GCAModule | 0.9450 | 0.8887 | 0.9478 | | 0.7906 | 0.8481 | 0.8958 | | 4xb512 | ## Citation diff --git a/configs/textrecog/nrtr/README.md b/configs/textrecog/nrtr/README.md index b57cc7f8d..1145ae9ca 100644 --- a/configs/textrecog/nrtr/README.md +++ b/configs/textrecog/nrtr/README.md @@ -34,15 +34,15 @@ Scene text recognition has attracted a great many researches due to its importan ## Results and Models -| Methods | Backbone | | Regular Text | | | | Irregular Text | | download | -| :---------------------------------------------------------: | :-------------------: | :----: | :----------: | :-------: | :-: | :-------: | :------------: | :----: | :-----------------------------------------------------------: | -| | | IIIT5K | SVT | IC13-1015 | | IC15-2077 | SVTP | CT80 | | -| [NRTR](/configs/textrecog/nrtr/nrtr_modality-transform_6e_st_mj.py) | NRTRModalityTransform | 0.9147 | 0.8841 | 0.9369 | | 0.7246 | 0.7783 | 0.7500 | [model](https://download.openmmlab.com/mmocr/textrecog/nrtr/nrtr_modality-transform_6e_st_mj/nrtr_modality-transform_6e_st_mj_20220916_103322-bd9425be.pth) \| [log](https://download.openmmlab.com/mmocr/textrecog/nrtr/nrtr_modality-transform_6e_st_mj/20220916_103322.log) | -| [NRTR-TTA](/configs/textrecog/nrtr/nrtr_modality-transform_6e_st_mj.py) | NRTRModalityTransform | 0.9123 | 0.8825 | 0.9310 | | 0.7492 | 0.7798 | 0.7535 | | -| [NRTR](/configs/textrecog/nrtr/nrtr_resnet31-1by8-1by4_6e_st_mj.py) | R31-1/8-1/4 | 0.9483 | 0.8918 | 0.9507 | | 0.7578 | 0.8016 | 0.8889 | [model](https://download.openmmlab.com/mmocr/textrecog/nrtr/nrtr_resnet31-1by8-1by4_6e_st_mj/nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322-a6a2a123.pth) \| [log](https://download.openmmlab.com/mmocr/textrecog/nrtr/nrtr_resnet31-1by8-1by4_6e_st_mj/20220916_103322.log) | -| [NRTR-TTA](/configs/textrecog/nrtr/nrtr_resnet31-1by8-1by4_6e_st_mj.py) | R31-1/8-1/4 | 0.9443 | 0.8903 | 0.9478 | | 0.7790 | 0.8078 | 0.8854 | | -| [NRTR](/configs/textrecog/nrtr/nrtr_resnet31-1by16-1by8_6e_st_mj.py) | R31-1/16-1/8 | 0.9470 | 0.8918 | 0.9399 | | 0.7376 | 0.7969 | 0.8854 | [model](https://download.openmmlab.com/mmocr/textrecog/nrtr/nrtr_resnet31-1by16-1by8_6e_st_mj/nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358-43767036.pth) \| [log](https://download.openmmlab.com/mmocr/textrecog/nrtr/nrtr_resnet31-1by16-1by8_6e_st_mj/20220920_143358.log) | -| [NRTR-TTA](/configs/textrecog/nrtr/nrtr_resnet31-1by16-1by8_6e_st_mj.py) | R31-1/16-1/8 | 0.9423 | 0.8903 | 0.9360 | | 0.7641 | 0.8016 | 0.8854 | | +| Methods | Backbone | | Regular Text | | | | Irregular Text | | download | Batch Size | +| :----------------------------------------------------: | :-------------------: | :----: | :----------: | :-------: | :-: | :-------: | :------------: | :----: | :------------------------------------------------------: | :--------: | +| | | IIIT5K | SVT | IC13-1015 | | IC15-2077 | SVTP | CT80 | | | +| [NRTR](/configs/textrecog/nrtr/nrtr_modality-transform_6e_st_mj.py) | NRTRModalityTransform | 0.9147 | 0.8841 | 0.9369 | | 0.7246 | 0.7783 | 0.7500 | [model](https://download.openmmlab.com/mmocr/textrecog/nrtr/nrtr_modality-transform_6e_st_mj/nrtr_modality-transform_6e_st_mj_20220916_103322-bd9425be.pth) \| [log](https://download.openmmlab.com/mmocr/textrecog/nrtr/nrtr_modality-transform_6e_st_mj/20220916_103322.log) | 1xb384 | +| [NRTR-TTA](/configs/textrecog/nrtr/nrtr_modality-transform_6e_st_mj.py) | NRTRModalityTransform | 0.9123 | 0.8825 | 0.9310 | | 0.7492 | 0.7798 | 0.7535 | | 1xb384 | +| [NRTR](/configs/textrecog/nrtr/nrtr_resnet31-1by8-1by4_6e_st_mj.py) | R31-1/8-1/4 | 0.9483 | 0.8918 | 0.9507 | | 0.7578 | 0.8016 | 0.8889 | [model](https://download.openmmlab.com/mmocr/textrecog/nrtr/nrtr_resnet31-1by8-1by4_6e_st_mj/nrtr_resnet31-1by8-1by4_6e_st_mj_20220916_103322-a6a2a123.pth) \| [log](https://download.openmmlab.com/mmocr/textrecog/nrtr/nrtr_resnet31-1by8-1by4_6e_st_mj/20220916_103322.log) | 1xb384 | +| [NRTR-TTA](/configs/textrecog/nrtr/nrtr_resnet31-1by8-1by4_6e_st_mj.py) | R31-1/8-1/4 | 0.9443 | 0.8903 | 0.9478 | | 0.7790 | 0.8078 | 0.8854 | | 1xb384 | +| [NRTR](/configs/textrecog/nrtr/nrtr_resnet31-1by16-1by8_6e_st_mj.py) | R31-1/16-1/8 | 0.9470 | 0.8918 | 0.9399 | | 0.7376 | 0.7969 | 0.8854 | [model](https://download.openmmlab.com/mmocr/textrecog/nrtr/nrtr_resnet31-1by16-1by8_6e_st_mj/nrtr_resnet31-1by16-1by8_6e_st_mj_20220920_143358-43767036.pth) \| [log](https://download.openmmlab.com/mmocr/textrecog/nrtr/nrtr_resnet31-1by16-1by8_6e_st_mj/20220920_143358.log) | 1xb384 | +| [NRTR-TTA](/configs/textrecog/nrtr/nrtr_resnet31-1by16-1by8_6e_st_mj.py) | R31-1/16-1/8 | 0.9423 | 0.8903 | 0.9360 | | 0.7641 | 0.8016 | 0.8854 | | 1xb384 | ## Citation diff --git a/configs/textrecog/robust_scanner/README.md b/configs/textrecog/robust_scanner/README.md index bc7403e3a..d454896a7 100644 --- a/configs/textrecog/robust_scanner/README.md +++ b/configs/textrecog/robust_scanner/README.md @@ -40,11 +40,11 @@ The attention-based encoder-decoder framework has recently achieved impressive r ## Results and Models -| Methods | GPUs | | Regular Text | | | | Irregular Text | | download | -| :------------------------------------------------------------------: | :--: | :----: | :----------: | :-------: | :-: | :-------: | :------------: | :----: | :-------------------------------------------------------------------: | -| | | IIIT5K | SVT | IC13-1015 | | IC15-2077 | SVTP | CT80 | | -| [RobustScanner](/configs/textrecog/robust_scanner/robustscanner_resnet31_5e_st-sub_mj-sub_sa_real.py) | 4 | 0.9510 | 0.9011 | 0.9320 | | 0.7578 | 0.8078 | 0.8750 | [model](https://download.openmmlab.com/mmocr/textrecog/robust_scanner/robustscanner_resnet31_5e_st-sub_mj-sub_sa_real/robustscanner_resnet31_5e_st-sub_mj-sub_sa_real_20220915_152447-7fc35929.pth) \| [log](https://download.openmmlab.com/mmocr/textrecog/robust_scanner/robustscanner_resnet31_5e_st-sub_mj-sub_sa_real/20220915_152447.log) | -| [RobustScanner-TTA](/configs/textrecog/robust_scanner/robustscanner_resnet31_5e_st-sub_mj-sub_sa_real.py) | 4 | 0.9487 | 0.9011 | 0.9261 | | 0.7805 | 0.8124 | 0.8819 | | +| Methods | GPUs | | Regular Text | | | | Irregular Text | | download | Batch Size | +| :-------------------------------------------------------------: | :--: | :----: | :----------: | :-------: | :-: | :-------: | :------------: | :----: | :--------------------------------------------------------------: | :--------: | +| | | IIIT5K | SVT | IC13-1015 | | IC15-2077 | SVTP | CT80 | | | +| [RobustScanner](/configs/textrecog/robust_scanner/robustscanner_resnet31_5e_st-sub_mj-sub_sa_real.py) | 4 | 0.9510 | 0.9011 | 0.9320 | | 0.7578 | 0.8078 | 0.8750 | [model](https://download.openmmlab.com/mmocr/textrecog/robust_scanner/robustscanner_resnet31_5e_st-sub_mj-sub_sa_real/robustscanner_resnet31_5e_st-sub_mj-sub_sa_real_20220915_152447-7fc35929.pth) \| [log](https://download.openmmlab.com/mmocr/textrecog/robust_scanner/robustscanner_resnet31_5e_st-sub_mj-sub_sa_real/20220915_152447.log) | 4xb256 | +| [RobustScanner-TTA](/configs/textrecog/robust_scanner/robustscanner_resnet31_5e_st-sub_mj-sub_sa_real.py) | 4 | 0.9487 | 0.9011 | 0.9261 | | 0.7805 | 0.8124 | 0.8819 | | 4xb256 | ## References diff --git a/configs/textrecog/sar/README.md b/configs/textrecog/sar/README.md index 5678c80f1..517f27f52 100644 --- a/configs/textrecog/sar/README.md +++ b/configs/textrecog/sar/README.md @@ -40,13 +40,13 @@ Recognizing irregular text in natural scene images is challenging due to the lar ## Results and Models -| Methods | Backbone | Decoder | | Regular Text | | | | Irregular Text | | download | -| :----------------------------------------------------: | :---------: | :------------------: | :----: | :----------: | :-------: | :-: | :-------: | :------------: | :----: | :------------------------------------------------------: | -| | | | IIIT5K | SVT | IC13-1015 | | IC15-2077 | SVTP | CT80 | | -| [SAR](/configs/textrecog/sar/sar_r31_parallel_decoder_academic.py) | R31-1/8-1/4 | ParallelSARDecoder | 0.9533 | 0.8964 | 0.9369 | | 0.7602 | 0.8326 | 0.9062 | [model](https://download.openmmlab.com/mmocr/textrecog/sar/sar_resnet31_parallel-decoder_5e_st-sub_mj-sub_sa_real/sar_resnet31_parallel-decoder_5e_st-sub_mj-sub_sa_real_20220915_171910-04eb4e75.pth) \| [log](https://download.openmmlab.com/mmocr/textrecog/sar/sar_resnet31_parallel-decoder_5e_st-sub_mj-sub_sa_real/20220915_171910.log) | -| [SAR-TTA](/configs/textrecog/sar/sar_r31_parallel_decoder_academic.py) | R31-1/8-1/4 | ParallelSARDecoder | 0.9510 | 0.8964 | 0.9340 | | 0.7862 | 0.8372 | 0.9132 | | -| [SAR](/configs/textrecog/sar/sar_r31_sequential_decoder_academic.py) | R31-1/8-1/4 | SequentialSARDecoder | 0.9553 | 0.9073 | 0.9409 | | 0.7761 | 0.8093 | 0.8958 | [model](https://download.openmmlab.com/mmocr/textrecog/sar/sar_resnet31_sequential-decoder_5e_st-sub_mj-sub_sa_real/sar_resnet31_sequential-decoder_5e_st-sub_mj-sub_sa_real_20220915_185451-1fd6b1fc.pth) \| [log](https://download.openmmlab.com/mmocr/textrecog/sar/sar_resnet31_sequential-decoder_5e_st-sub_mj-sub_sa_real/20220915_185451.log) | -| [SAR-TTA](/configs/textrecog/sar/sar_r31_sequential_decoder_academic.py) | R31-1/8-1/4 | SequentialSARDecoder | 0.9530 | 0.9073 | 0.9389 | | 0.8002 | 0.8124 | 0.9028 | | +| Methods | Backbone | Decoder | | Regular Text | | | | Irregular Text | | download | Batch Size | +| :-----------------------------------------------: | :---------: | :------------------: | :----: | :----------: | :-------: | :-: | :-------: | :------------: | :----: | :-------------------------------------------------: | :--------: | +| | | | IIIT5K | SVT | IC13-1015 | | IC15-2077 | SVTP | CT80 | | | +| [SAR](/configs/textrecog/sar/sar_r31_parallel_decoder_academic.py) | R31-1/8-1/4 | ParallelSARDecoder | 0.9533 | 0.8964 | 0.9369 | | 0.7602 | 0.8326 | 0.9062 | [model](https://download.openmmlab.com/mmocr/textrecog/sar/sar_resnet31_parallel-decoder_5e_st-sub_mj-sub_sa_real/sar_resnet31_parallel-decoder_5e_st-sub_mj-sub_sa_real_20220915_171910-04eb4e75.pth) \| [log](https://download.openmmlab.com/mmocr/textrecog/sar/sar_resnet31_parallel-decoder_5e_st-sub_mj-sub_sa_real/20220915_171910.log) | 8xb384 | +| [SAR-TTA](/configs/textrecog/sar/sar_r31_parallel_decoder_academic.py) | R31-1/8-1/4 | ParallelSARDecoder | 0.9510 | 0.8964 | 0.9340 | | 0.7862 | 0.8372 | 0.9132 | | 8xb384 | +| [SAR](/configs/textrecog/sar/sar_r31_sequential_decoder_academic.py) | R31-1/8-1/4 | SequentialSARDecoder | 0.9553 | 0.9073 | 0.9409 | | 0.7761 | 0.8093 | 0.8958 | [model](https://download.openmmlab.com/mmocr/textrecog/sar/sar_resnet31_sequential-decoder_5e_st-sub_mj-sub_sa_real/sar_resnet31_sequential-decoder_5e_st-sub_mj-sub_sa_real_20220915_185451-1fd6b1fc.pth) \| [log](https://download.openmmlab.com/mmocr/textrecog/sar/sar_resnet31_sequential-decoder_5e_st-sub_mj-sub_sa_real/20220915_185451.log) | 8xb384 | +| [SAR-TTA](/configs/textrecog/sar/sar_r31_sequential_decoder_academic.py) | R31-1/8-1/4 | SequentialSARDecoder | 0.9530 | 0.9073 | 0.9389 | | 0.8002 | 0.8124 | 0.9028 | | 8xb384 | ## Citation diff --git a/configs/textrecog/satrn/README.md b/configs/textrecog/satrn/README.md index f0421c9e0..4730c5782 100644 --- a/configs/textrecog/satrn/README.md +++ b/configs/textrecog/satrn/README.md @@ -34,13 +34,13 @@ Scene text recognition (STR) is the task of recognizing character sequences in n ## Results and Models -| Methods | | Regular Text | | | | Irregular Text | | download | -| :--------------------------------------------------------------------: | :----: | :----------: | :-------: | :-: | :-------: | :------------: | :----: | :---------------------------------------------------------------------: | -| | IIIT5K | SVT | IC13-1015 | | IC15-2077 | SVTP | CT80 | | -| [Satrn](/configs/textrecog/satrn/satrn_shallow_5e_st_mj.py) | 0.9600 | 0.9181 | 0.9606 | | 0.8045 | 0.8837 | 0.8993 | [model](https://download.openmmlab.com/mmocr/textrecog/satrn/satrn_shallow_5e_st_mj/satrn_shallow_5e_st_mj_20220915_152443-5fd04a4c.pth) \| [log](https://download.openmmlab.com/mmocr/textrecog/satrn/satrn_shallow_5e_st_mj/20220915_152443.log) | -| [Satrn-TTA](/configs/textrecog/satrn/satrn_shallow_5e_st_mj.py) | 0.9530 | 0.9181 | 0.9527 | | 0.8276 | 0.8884 | 0.9028 | | -| [Satrn_small](/configs/textrecog/satrn/satrn_shallow-small_5e_st_mj.py) | 0.9423 | 0.9011 | 0.9567 | | 0.7886 | 0.8574 | 0.8472 | [model](https://download.openmmlab.com/mmocr/textrecog/satrn/satrn_shallow-small_5e_st_mj/satrn_shallow-small_5e_st_mj_20220915_152442-5591bf27.pth) \| [log](https://download.openmmlab.com/mmocr/textrecog/satrn/satrn_shallow-small_5e_st_mj/20220915_152442.log) | -| [Satrn_small-TTA](/configs/textrecog/satrn/satrn_shallow-small_5e_st_mj.py) | 0.9380 | 0.8995 | 0.9488 | | 0.8122 | 0.8620 | 0.8507 | | +| Methods | | Regular Text | | | | Irregular Text | | download | Batch Size | +| :---------------------------------------------------------------: | :----: | :----------: | :-------: | :-: | :-------: | :------------: | :----: | :----------------------------------------------------------------: | :--------: | +| | IIIT5K | SVT | IC13-1015 | | IC15-2077 | SVTP | CT80 | | | +| [Satrn](/configs/textrecog/satrn/satrn_shallow_5e_st_mj.py) | 0.9600 | 0.9181 | 0.9606 | | 0.8045 | 0.8837 | 0.8993 | [model](https://download.openmmlab.com/mmocr/textrecog/satrn/satrn_shallow_5e_st_mj/satrn_shallow_5e_st_mj_20220915_152443-5fd04a4c.pth) \| [log](https://download.openmmlab.com/mmocr/textrecog/satrn/satrn_shallow_5e_st_mj/20220915_152443.log) | 4xb128 | +| [Satrn-TTA](/configs/textrecog/satrn/satrn_shallow_5e_st_mj.py) | 0.9530 | 0.9181 | 0.9527 | | 0.8276 | 0.8884 | 0.9028 | | 4xb128 | +| [Satrn_small](/configs/textrecog/satrn/satrn_shallow-small_5e_st_mj.py) | 0.9423 | 0.9011 | 0.9567 | | 0.7886 | 0.8574 | 0.8472 | [model](https://download.openmmlab.com/mmocr/textrecog/satrn/satrn_shallow-small_5e_st_mj/satrn_shallow-small_5e_st_mj_20220915_152442-5591bf27.pth) \| [log](https://download.openmmlab.com/mmocr/textrecog/satrn/satrn_shallow-small_5e_st_mj/20220915_152442.log) | 4xb128 | +| [Satrn_small-TTA](/configs/textrecog/satrn/satrn_shallow-small_5e_st_mj.py) | 0.9380 | 0.8995 | 0.9488 | | 0.8122 | 0.8620 | 0.8507 | | 4xb128 | ## 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