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[Docs] Update readme config links of Mask R-CNN, PSENet, FCENet, SATRN, NRTR, MASTER (#1308)

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* update readme cfg

* Apply suggestions from code review

Co-authored-by: Tong Gao <gaotongxiao@gmail.com>
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Xinyu Wang 2022-08-22 19:06:53 +08:00 committed by GitHub
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4
configs/textdet/fcenet/README.md
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@ -18,13 +18,13 @@ One of the main challenges for arbitrary-shaped text detection is to design a go
| Method | Backbone | Pretrained Model | Training set | Test set | #epochs | Test size | Recall | Precision | Hmean | Download |
| :-------------------------------------------------: | :--------------: | :--------------: | :-----------: | :----------: | :-----: | :---------: | :----: | :-------: | :----: | :---------------------------------------------------: |
| [FCENet](/configs/textdet/fcenet/fcenet_r50dcnv2_fpn_1500e_ctw1500.py) | ResNet50 + DCNv2 | ImageNet | CTW1500 Train | CTW1500 Test | 1500 | (736, 1080) | 0.8468 | 0.8532 | 0.8500 | [model](https://download.openmmlab.com/mmocr/textdet/fcenet/fcenet_r50dcnv2_fpn_1500e_ctw1500_20211022-e326d7ec.pth) \| [log](https://download.openmmlab.com/mmocr/textdet/fcenet/20210511_181328.log.json) |
| [FCENet](/configs/textdet/fcenet/fcenet_resnet50-dcnv2_fpn_1500e_ctw1500.py) | ResNet50 + DCNv2 | ImageNet | CTW1500 Train | CTW1500 Test | 1500 | (736, 1080) | 0.8468 | 0.8532 | 0.8500 | [model](https://download.openmmlab.com/mmocr/textdet/fcenet/fcenet_r50dcnv2_fpn_1500e_ctw1500_20211022-e326d7ec.pth) \| [log](https://download.openmmlab.com/mmocr/textdet/fcenet/20210511_181328.log.json) |
### ICDAR2015
| Method | Backbone | Pretrained Model | Training set | Test set | #epochs | Test size | Recall | Precision | Hmean | Download |
| :------------------------------------------------------: | :------: | :--------------: | :----------: | :-------: | :-----: | :----------: | :----: | :-------: | :----: | :---------------------------------------------------------: |
| [FCENet](/configs/textdet/fcenet/fcenet_r50_fpn_1500e_icdar2015.py) | ResNet50 | ImageNet | IC15 Train | IC15 Test | 1500 | (2260, 2260) | 0.8243 | 0.8834 | 0.8528 | [model](https://download.openmmlab.com/mmocr/textdet/fcenet/fcenet_r50_fpn_1500e_icdar2015_20211022-daefb6ed.pth) \| [log](https://download.openmmlab.com/mmocr/textdet/fcenet/20210601_222655.log.json) |
| [FCENet](/configs/textdet/fcenet/fcenet_resnet50_fpn_1500e_icdar2015.py) | ResNet50 | ImageNet | IC15 Train | IC15 Test | 1500 | (2260, 2260) | 0.8243 | 0.8834 | 0.8528 | [model](https://download.openmmlab.com/mmocr/textdet/fcenet/fcenet_r50_fpn_1500e_icdar2015_20211022-daefb6ed.pth) \| [log](https://download.openmmlab.com/mmocr/textdet/fcenet/20210601_222655.log.json) |
## Citation

6
configs/textdet/maskrcnn/README.md
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@ -18,19 +18,19 @@ We present a conceptually simple, flexible, and general framework for object ins
| Method | Pretrained Model | Training set | Test set | #epochs | Test size | Recall | Precision | Hmean | Download |
| :----------------------------------------------------------: | :--------------: | :-----------: | :----------: | :-----: | :-------: | :----: | :-------: | :----: | :------------------------------------------------------------: |
| [MaskRCNN](/configs/textdet/maskrcnn/mask_rcnn_r50_fpn_160e_ctw1500.py) | ImageNet | CTW1500 Train | CTW1500 Test | 160 | 1600 | 0.7714 | 0.7272 | 0.7486 | [model](https://download.openmmlab.com/mmocr/textdet/maskrcnn/mask_rcnn_r50_fpn_160e_ctw1500_20210219-96497a76.pth) \| [log](https://download.openmmlab.com/mmocr/textdet/maskrcnn/mask_rcnn_r50_fpn_160e_ctw1500_20210219-96497a76.log.json) |
| [MaskRCNN](/configs/textdet/maskrcnn/mask-rcnn_resnet50_fpn_160e_ctw1500.py) | ImageNet | CTW1500 Train | CTW1500 Test | 160 | 1600 | 0.7714 | 0.7272 | 0.7486 | [model](https://download.openmmlab.com/mmocr/textdet/maskrcnn/mask_rcnn_r50_fpn_160e_ctw1500_20210219-96497a76.pth) \| [log](https://download.openmmlab.com/mmocr/textdet/maskrcnn/mask_rcnn_r50_fpn_160e_ctw1500_20210219-96497a76.log.json) |
### ICDAR2015
| Method | Pretrained Model | Training set | Test set | #epochs | Test size | Recall | Precision | Hmean | Download |
| :--------------------------------------------------------: | :--------------: | :-------------: | :------------: | :-----: | :-------: | :----: | :-------: | :----: | :----------------------------------------------------------: |
| [MaskRCNN](/configs/textdet/maskrcnn/mask_rcnn_r50_fpn_160e_icdar2015.py) | ImageNet | ICDAR2015 Train | ICDAR2015 Test | 160 | 1920 | 0.8045 | 0.8530 | 0.8280 | [model](https://download.openmmlab.com/mmocr/textdet/maskrcnn/mask_rcnn_r50_fpn_160e_icdar2015_20210219-8eb340a3.pth) \| [log](https://download.openmmlab.com/mmocr/textdet/maskrcnn/mask_rcnn_r50_fpn_160e_icdar2015_20210219-8eb340a3.log.json) |
| [MaskRCNN](/configs/textdet/maskrcnn/mask-rcnn_resnet50_fpn_160e_icdar2015.py) | ImageNet | ICDAR2015 Train | ICDAR2015 Test | 160 | 1920 | 0.8045 | 0.8530 | 0.8280 | [model](https://download.openmmlab.com/mmocr/textdet/maskrcnn/mask_rcnn_r50_fpn_160e_icdar2015_20210219-8eb340a3.pth) \| [log](https://download.openmmlab.com/mmocr/textdet/maskrcnn/mask_rcnn_r50_fpn_160e_icdar2015_20210219-8eb340a3.log.json) |
### ICDAR2017
| Method | Pretrained Model | Training set | Test set | #epochs | Test size | Recall | Precision | Hmean | Download |
| :---------------------------------------------------------: | :--------------: | :-------------: | :-----------: | :-----: | :-------: | :----: | :-------: | :---: | :-----------------------------------------------------------: |
| [MaskRCNN](/configs/textdet/maskrcnn/mask_rcnn_r50_fpn_160e_icdar2017.py) | ImageNet | ICDAR2017 Train | ICDAR2017 Val | 160 | 1600 | 0.754 | 0.827 | 0.789 | [model](https://download.openmmlab.com/mmocr/textdet/maskrcnn/mask_rcnn_r50_fpn_160e_icdar2017_20210218-c6ec3ebb.pth) \| [log](https://download.openmmlab.com/mmocr/textdet/maskrcnn/mask_rcnn_r50_fpn_160e_icdar2017_20210218-c6ec3ebb.log.json) |
| [MaskRCNN](/configs/textdet/maskrcnn/mask-rcnn_resnet50_fpn_160e_icdar2017.py) | ImageNet | ICDAR2017 Train | ICDAR2017 Val | 160 | 1600 | 0.754 | 0.827 | 0.789 | [model](https://download.openmmlab.com/mmocr/textdet/maskrcnn/mask_rcnn_r50_fpn_160e_icdar2017_20210218-c6ec3ebb.pth) \| [log](https://download.openmmlab.com/mmocr/textdet/maskrcnn/mask_rcnn_r50_fpn_160e_icdar2017_20210218-c6ec3ebb.log.json) |
```{note}
We tuned parameters with the techniques in [Pyramid Mask Text Detector](https://arxiv.org/abs/1903.11800)

6
configs/textdet/psenet/README.md
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@ -18,14 +18,14 @@ Scene text detection has witnessed rapid progress especially with the recent dev
| Method | Backbone | Extra Data | Training set | Test set | #epochs | Test size | Recall | Precision | Hmean | Download |
| :------------------------------------------------: | :------: | :--------: | :-----------: | :----------: | :-----: | :-------: | :-----------: | :-----------: | :-----------: | :--------------------------------------------------: |
| [PSENet-4s](configs/textdet/psenet/psenet_r50_fpnf_600e_ctw1500.py) | ResNet50 | - | CTW1500 Train | CTW1500 Test | 600 | 1280 | 0.728 (0.717) | 0.849 (0.852) | 0.784 (0.779) | [model](https://download.openmmlab.com/mmocr/textdet/psenet/psenet_r50_fpnf_600e_ctw1500_20210401-216fed50.pth) \| [log](https://download.openmmlab.com/mmocr/textdet/psenet/20210401_215421.log.json) |
| [PSENet-4s](/configs/textdet/psenet/psenet_resnet50_fpnf_600e_ctw1500.py) | ResNet50 | - | CTW1500 Train | CTW1500 Test | 600 | 1280 | 0.728 (0.717) | 0.849 (0.852) | 0.784 (0.779) | [model](https://download.openmmlab.com/mmocr/textdet/psenet/psenet_r50_fpnf_600e_ctw1500_20210401-216fed50.pth) \| [log](https://download.openmmlab.com/mmocr/textdet/psenet/20210401_215421.log.json) |
### ICDAR2015
| Method | Backbone | Extra Data | Training set | Test set | #epochs | Test size | Recall | Precision | Hmean | Download |
| :-----------------------------------------: | :------: | :---------------------------------------------: | :----------: | :-------: | :-----: | :-------: | :----: | :-------: | :---: | :-------------------------------------------: |
| [PSENet-4s](configs/textdet/psenet/psenet_r50_fpnf_600e_icdar2015.py) | ResNet50 | - | IC15 Train | IC15 Test | 600 | 2240 | 0.766 | 0.840 | 0.806 | [model](https://download.openmmlab.com/mmocr/textdet/psenet/psenet_r50_fpnf_600e_icdar2015-c6131f0d.pth) \| [log](https://download.openmmlab.com/mmocr/textdet/psenet/20210331_214145.log.json) |
| [PSENet-4s](configs/textdet/psenet/psenet_r50_fpnf_600e_icdar2015.py) | ResNet50 | pretrain on IC17 MLT [model](https://download.openmmlab.com/mmocr/textdet/psenet/psenet_r50_fpnf_600e_icdar2017_as_pretrain-3bd6056c.pth) | IC15 Train | IC15 Test | 600 | 2240 | 0.834 | 0.861 | 0.847 | [model](https://download.openmmlab.com/mmocr/textdet/psenet/psenet_r50_fpnf_600e_icdar2015_pretrain-eefd8fe6.pth) \| [log](<>) |
| [PSENet-4s](/configs/textdet/psenet/psenet_resnet50_fpnf_600e_icdar2015.py) | ResNet50 | - | IC15 Train | IC15 Test | 600 | 2240 | 0.766 | 0.840 | 0.806 | [model](https://download.openmmlab.com/mmocr/textdet/psenet/psenet_r50_fpnf_600e_icdar2015-c6131f0d.pth) \| [log](https://download.openmmlab.com/mmocr/textdet/psenet/20210331_214145.log.json) |
| [PSENet-4s](/configs/textdet/psenet/psenet_resnet50_fpnf_600e_icdar2015.py) | ResNet50 | pretrain on IC17 MLT [model](https://download.openmmlab.com/mmocr/textdet/psenet/psenet_r50_fpnf_600e_icdar2017_as_pretrain-3bd6056c.pth) | IC15 Train | IC15 Test | 600 | 2240 | 0.834 | 0.861 | 0.847 | [model](https://download.openmmlab.com/mmocr/textdet/psenet/psenet_r50_fpnf_600e_icdar2015_pretrain-eefd8fe6.pth) \| [log](<>) |
## Citation

8
configs/textrecog/master/README.md
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@ -35,10 +35,10 @@ Attention-based scene text recognizers have gained huge success, which leverages
## Results and Models
| Methods | Backbone | | Regular Text | | | | Irregular Text | | download |
| :------------------------------------------------------------: | :-----------: | :----: | :----------: | :---: | :-: | :---: | :------------: | :---: | :-------------------------------------------------------------------------: |
| | | IIIT5K | SVT | IC13 | | IC15 | SVTP | CT80 | |
| [MASTER](/configs/textrecog/master/master_r31_12e_ST_MJ_SA.py) | R31-GCAModule | 94.63 | 90.42 | 94.98 | | 75.54 | 82.79 | 88.54 | [model](https://download.openmmlab.com/mmocr/textrecog/master/master_r31_12e_ST_MJ_SA-787edd36.pth) \| [log](https://download.openmmlab.com/mmocr/textrecog/master/master_r31_12e_ST_MJ_SA-787edd36.log.json) |
| Methods | Backbone | | Regular Text | | | | Irregular Text | | download |
| :-----------------------------------------------------------------: | :-----------: | :----: | :----------: | :---: | :-: | :---: | :------------: | :---: | :--------------------------------------------------------------------: |
| | | IIIT5K | SVT | IC13 | | IC15 | SVTP | CT80 | |
| [MASTER](/configs/textrecog/master/master_resnet31_12e_st_mj_sa.py) | R31-GCAModule | 94.63 | 90.42 | 94.98 | | 75.54 | 82.79 | 88.54 | [model](https://download.openmmlab.com/mmocr/textrecog/master/master_r31_12e_ST_MJ_SA-787edd36.pth) \| [log](https://download.openmmlab.com/mmocr/textrecog/master/master_r31_12e_ST_MJ_SA-787edd36.log.json) |
## Citation

10
configs/textrecog/nrtr/README.md
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@ -34,11 +34,11 @@ 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 | | IC15 | SVTP | CT80 | |
| [NRTR](/configs/textrecog/nrtr/nrtr_r31_1by16_1by8_academic.py) | R31-1/16-1/8 | 94.8 | 89.03 | 93.79 | | 74.19 | 80.31 | 87.15 | [model](https://download.openmmlab.com/mmocr/textrecog/nrtr/nrtr_r31_1by16_1by8_academic_20211124-f60cebf4.pth) \| [log](https://download.openmmlab.com/mmocr/textrecog/nrtr/20211124_002420.log.json) |
| [NRTR](/configs/textrecog/nrtr/nrtr_r31_1by8_1by4_academic.py) | R31-1/8-1/4 | 95.5 | 90.01 | 94.38 | | 74.05 | 79.53 | 87.15 | [model](https://download.openmmlab.com/mmocr/textrecog/nrtr/nrtr_r31_1by8_1by4_academic_20211123-e1fdb322.pth) \| [log](https://download.openmmlab.com/mmocr/textrecog/nrtr/20211123_232151.log.json) |
| Methods | Backbone | | Regular Text | | | | Irregular Text | | download |
| :------------------------------------------------------------------: | :----------: | :----: | :----------: | :---: | :-: | :---: | :------------: | :---: | :--------------------------------------------------------------------: |
| | | IIIT5K | SVT | IC13 | | IC15 | SVTP | CT80 | |
| [NRTR](/configs/textrecog/nrtr/nrtr_resnet31-1by16-1by8_6e_st_mj.py) | R31-1/16-1/8 | 94.8 | 89.03 | 93.79 | | 74.19 | 80.31 | 87.15 | [model](https://download.openmmlab.com/mmocr/textrecog/nrtr/nrtr_r31_1by16_1by8_academic_20211124-f60cebf4.pth) \| [log](https://download.openmmlab.com/mmocr/textrecog/nrtr/20211124_002420.log.json) |
| [NRTR](/configs/textrecog/nrtr/nrtr_resnet31-1by8-1by4_6e_st_mj.py) | R31-1/8-1/4 | 95.5 | 90.01 | 94.38 | | 74.05 | 79.53 | 87.15 | [model](https://download.openmmlab.com/mmocr/textrecog/nrtr/nrtr_r31_1by8_1by4_academic_20211123-e1fdb322.pth) \| [log](https://download.openmmlab.com/mmocr/textrecog/nrtr/20211123_232151.log.json) |
```{note}

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configs/textrecog/satrn/README.md
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@ -34,11 +34,11 @@ 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 | | IC15 | SVTP | CT80 | |
| [Satrn](/configs/textrecog/satrn/satrn_academic.py) | 95.1 | 92.0 | 95.8 | | 81.4 | 87.6 | 90.6 | [model](https://download.openmmlab.com/mmocr/textrecog/satrn/satrn_academic_20211009-cb8b1580.pth) \| [log](https://download.openmmlab.com/mmocr/textrecog/satrn/20210809_093244.log.json) |
| [Satrn_small](/configs/textrecog/satrn/satrn_small.py) | 94.7 | 91.3 | 95.4 | | 81.9 | 85.9 | 86.5 | [model](https://download.openmmlab.com/mmocr/textrecog/satrn/satrn_small_20211009-2cf13355.pth) \| [log](https://download.openmmlab.com/mmocr/textrecog/satrn/20210811_053047.log.json) |
| Methods | | Regular Text | | | | Irregular Text | | download |
| :---------------------------------------------------------------------: | :----: | :----------: | :--: | :-: | :--: | :------------: | :--: | :--------------------------------------------------------------------------------: |
| | IIIT5K | SVT | IC13 | | IC15 | SVTP | CT80 | |
| [Satrn](/configs/textrecog/satrn/satrn_shallow_5e_st_mj.py) | 95.1 | 92.0 | 95.8 | | 81.4 | 87.6 | 90.6 | [model](https://download.openmmlab.com/mmocr/textrecog/satrn/satrn_academic_20211009-cb8b1580.pth) \| [log](https://download.openmmlab.com/mmocr/textrecog/satrn/20210809_093244.log.json) |
| [Satrn_small](/configs/textrecog/satrn/satrn_shallow-small_5e_st_mj.py) | 94.7 | 91.3 | 95.4 | | 81.9 | 85.9 | 86.5 | [model](https://download.openmmlab.com/mmocr/textrecog/satrn/satrn_small_20211009-2cf13355.pth) \| [log](https://download.openmmlab.com/mmocr/textrecog/satrn/20210811_053047.log.json) |
## Citation