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[Docs] Update Recog Models () 

* init

* update

* update abinet

* update abinet

* update abinet

* update abinet

* apply comments

Co-authored-by: Tong Gao <gaotongxiao@gmail.com>

* apply comments

Co-authored-by: Tong Gao <gaotongxiao@gmail.com>

* fix

Co-authored-by: Tong Gao <gaotongxiao@gmail.com>
pull/1434/head
Xinyu Wang 2022-10-08 15:02:19 +08:00 committed by GitHub
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12 changed files with 161 additions and 126 deletions

12
configs/textrecog/abinet/README.md
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@ -34,13 +34,11 @@ Linguistic knowledge is of great benefit to scene text recognition. However, how
## Results and models
Coming Soon!
| methods | pretrained | | Regular Text | | | Irregular Text | | download |
| :----------------------------------------------------------------------: | :--------------: | :----: | :----------: | :--: | :--: | :------------: | :--: | :----------------------- |
| | | IIIT5K | SVT | IC13 | IC15 | SVTP | CT80 | |
| [ABINet-Vision](/configs/textrecog/abinet/abinet-vision_20e_st-an_mj.py) | - | | | | | | | [model](<>) \| [log](<>) |
| [ABINet](/configs/textrecog/abinet/abinet_20e_st-an_mj.py) | [Pretrained](<>) | | | | | | | [model](<>) \| [log](<>) |
| methods | pretrained | | Regular Text | | | Irregular Text | | download |
| :----------------------------------------------: | :--------------------------------------------------: | :----: | :----------: | :----: | :----: | :------------: | :----: | :------------------------------------------------- |
| | | IIIT5K | SVT | IC13 | IC15 | SVTP | CT80 | |
| [ABINet-Vision](/configs/textrecog/abinet/abinet-vision_20e_st-an_mj.py) | - | 0.9523 | 0.9057 | 0.9369 | 0.7886 | 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](/configs/textrecog/abinet/abinet_20e_st-an_mj.py) | [Pretrained](https://download.openmmlab.com/mmocr/textrecog/abinet/abinet_pretrain-45deac15.pth) | 0.9603 | 0.9382 | 0.9547 | 0.8122 | 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) |
```{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.

56
configs/textrecog/abinet/metafile.yml
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@ -1,4 +1,19 @@
Collections:
- Name: ABINet-vision
Metadata:
Training Data: OCRDataset
Training Techniques:
- Adam
Epochs: 20
Batch Size: 1536
Training Resources: 2 x NVIDIA A100-SXM4-80GB
Architecture:
- ResNetABI
- ABIVisionModel
Paper:
URL: https://arxiv.org/pdf/2103.06495.pdf
Title: 'Read Like Humans: Autonomous, Bidirectional and Iterative Language Modeling for Scene Text Recognition'
README: configs/textrecog/abinet/README.md
- Name: ABINet
Metadata:
Training Data: OCRDataset
@ -6,7 +21,7 @@ Collections:
- Adam
Epochs: 20
Batch Size: 1536
Training Resources: 8x Tesla V100
Training Resources: 8 x NVIDIA A100-SXM4-80GB
Architecture:
- ResNetABI
- ABIVisionModel
@ -18,9 +33,9 @@ Collections:
README: configs/textrecog/abinet/README.md
Models:
- Name: abinet-vision_6e_st-an_mj
In Collection: ABINet
Config: configs/textrecog/abinet/abinet-vision_6e_st-an_mj.py
- Name: abinet-vision_20e_st-an_mj
In Collection: ABINet-vision
Config: configs/textrecog/abinet/abinet-vision_20e_st-an_mj.py
Metadata:
Training Data:
- SynthText
@ -29,32 +44,31 @@ Models:
- Task: Text Recognition
Dataset: IIIT5K
Metrics:
word_acc:
word_acc: 0.9523
- Task: Text Recognition
Dataset: SVT
Metrics:
word_acc:
word_acc: 0.9057
- Task: Text Recognition
Dataset: ICDAR2013
Metrics:
word_acc:
word_acc: 0.9369
- Task: Text Recognition
Dataset: ICDAR2015
Metrics:
word_acc:
word_acc: 0.7886
- Task: Text Recognition
Dataset: SVTP
Metrics:
word_acc:
word_acc: 0.8403
- Task: Text Recognition
Dataset: CT80
Metrics:
word_acc:
Weights:
- Name: abinet_6e_st-an_mj
word_acc: 0.8437
Weights: https://download.openmmlab.com/mmocr/textrecog/abinet/abinet-vision_20e_st-an_mj/abinet-vision_20e_st-an_mj_20220915_152445-85cfb03d.pth
- Name: abinet_20e_st-an_mj
In Collection: ABINet
Config: configs/textrecog/abinet/abinet_6e_st-an_mj.py
Config: configs/textrecog/abinet/abinet_20e_st-an_mj.py
Metadata:
Training Data:
- SynthText
@ -63,25 +77,25 @@ Models:
- Task: Text Recognition
Dataset: IIIT5K
Metrics:
word_acc:
word_acc: 0.9603
- Task: Text Recognition
Dataset: SVT
Metrics:
word_acc:
word_acc: 0.9382
- Task: Text Recognition
Dataset: ICDAR2013
Metrics:
word_acc:
word_acc: 0.9547
- Task: Text Recognition
Dataset: ICDAR2015
Metrics:
word_acc:
word_acc: 0.8122
- Task: Text Recognition
Dataset: SVTP
Metrics:
word_acc:
word_acc: 0.8868
- Task: Text Recognition
Dataset: CT80
Metrics:
word_acc:
Weights:
word_acc: 0.8785
Weights: https://download.openmmlab.com/mmocr/textrecog/abinet/abinet_20e_st-an_mj/abinet_20e_st-an_mj_20221005_012617-ead8c139.pth

10
configs/textrecog/master/README.md
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@ -35,12 +35,10 @@ Attention-based scene text recognizers have gained huge success, which leverages
## Results and Models
Coming Soon!
| 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 | | | | | | | | [model](<>) \| [log](<>) |
| 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 | 0.9490 | 0.8967 | 0.9517 | | 0.7631 | 0.8465 | 0.8854 | [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) |
## Citation

18
configs/textrecog/master/metafile.yml
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@ -5,8 +5,8 @@ Collections:
Training Techniques:
- Adam
Epochs: 12
Batch Size: 512
Training Resources: 4x Tesla A100
Batch Size: 2048
Training Resources: 4x NVIDIA A100-SXM4-80GB
Architecture:
- ResNet31-GCAModule
- MASTERDecoder
@ -28,25 +28,25 @@ Models:
- Task: Text Recognition
Dataset: IIIT5K
Metrics:
word_acc:
word_acc: 0.9490
- Task: Text Recognition
Dataset: SVT
Metrics:
word_acc:
word_acc: 0.8967
- Task: Text Recognition
Dataset: ICDAR2013
Metrics:
word_acc:
word_acc: 0.9517
- Task: Text Recognition
Dataset: ICDAR2015
Metrics:
word_acc:
word_acc: 0.7631
- Task: Text Recognition
Dataset: SVTP
Metrics:
word_acc:
word_acc: 0.8465
- Task: Text Recognition
Dataset: CT80
Metrics:
word_acc:
Weights:
word_acc: 0.8854
Weights: https://download.openmmlab.com/mmocr/textrecog/master/master_resnet31_12e_st_mj_sa/master_resnet31_12e_st_mj_sa_20220915_152443-f4a5cabc.pth

13
configs/textrecog/nrtr/README.md
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@ -34,13 +34,12 @@ Scene text recognition has attracted a great many researches due to its importan
## Results and Models
Coming Soon!
| 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 | | | | | | | | [model](<>) \| [log](<>) |
| [NRTR](/configs/textrecog/nrtr/nrtr_resnet31-1by8-1by4_6e_st_mj.py) | R31-1/8-1/4 | | | | | | | | [model](<>) \| [log](<>) |
| Methods | Backbone | | Regular Text | | | | Irregular Text | | download |
| :------------------------------------------------------------: | :-------------------: | :----: | :----------: | :----: | :-: | :----: | :------------: | :----: | :--------------------------------------------------------------: |
| | | IIIT5K | SVT | IC13 | | IC15 | SVTP | CT80 | |
| [NRTR](/configs/textrecog/nrtr/nrtr_modality-transform_6e_st_mj.py) | NRTRModalityTransform | 0.9150 | 0.8825 | 0.9369 | | 0.7232 | 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](/configs/textrecog/nrtr/nrtr_resnet31-1by8-1by4_6e_st_mj.py) | R31-1/8-1/4 | 0.9483 | 0.8825 | 0.9507 | | 0.7559 | 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](/configs/textrecog/nrtr/nrtr_resnet31-1by16-1by8_6e_st_mj.py) | R31-1/16-1/8 | 0.9470 | 0.8964 | 0.9399 | | 0.7357 | 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) |
## Citation

70
configs/textrecog/nrtr/metafile.yml
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@ -5,8 +5,8 @@ Collections:
Training Techniques:
- Adam
Epochs: 6
Batch Size: 6144
Training Resources: 1x Tesla A100
Batch Size: 384
Training Resources: 1x NVIDIA A100-SXM4-80GB
Architecture:
- CNN
- NRTREncoder
@ -17,9 +17,9 @@ Collections:
README: configs/textrecog/nrtr/README.md
Models:
- Name: nrtr_resnet31-1by16-1by8_6e_st_mj
- Name: nrtr_modality-transform_6e_st_mj
In Collection: NRTR
Config: configs/textrecog/nrtr/nrtr_resnet31-1by16-1by8_6e_st_mj.py
Config: configs/textrecog/nrtr/nrtr_modality-transform_6e_st_mj.py
Metadata:
Training Data:
- SynthText
@ -28,29 +28,28 @@ Models:
- Task: Text Recognition
Dataset: IIIT5K
Metrics:
word_acc:
word_acc: 0.9150
- Task: Text Recognition
Dataset: SVT
Metrics:
word_acc:
word_acc: 0.8825
- Task: Text Recognition
Dataset: ICDAR2013
Metrics:
word_acc:
word_acc: 0.9369
- Task: Text Recognition
Dataset: ICDAR2015
Metrics:
word_acc:
word_acc: 0.7232
- Task: Text Recognition
Dataset: SVTP
Metrics:
word_acc:
word_acc: 0.7783
- Task: Text Recognition
Dataset: CT80
Metrics:
word_acc:
Weights:
word_acc: 0.7500
Weights: https://download.openmmlab.com/mmocr/textrecog/nrtr/nrtr_modality-transform_6e_st_mj/nrtr_modality-transform_6e_st_mj_20220916_103322-bd9425be.pth
- Name: nrtr_resnet31-1by8-1by4_6e_st_mj
In Collection: NRTR
Config: configs/textrecog/nrtr/nrtr_resnet31-1by8-1by4_6e_st_mj.py
@ -62,25 +61,58 @@ Models:
- Task: Text Recognition
Dataset: IIIT5K
Metrics:
word_acc:
word_acc: 0.9483
- Task: Text Recognition
Dataset: SVT
Metrics:
word_acc:
word_acc: 0.8825
- Task: Text Recognition
Dataset: ICDAR2013
Metrics:
word_acc:
word_acc: 0.9507
- Task: Text Recognition
Dataset: ICDAR2015
Metrics:
word_acc:
word_acc: 0.7559
- Task: Text Recognition
Dataset: SVTP
Metrics:
word_acc:
word_acc: 0.8016
- Task: Text Recognition
Dataset: CT80
Metrics:
word_acc:
Weights:
word_acc: 0.8889
Weights: 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
- Name: nrtr_resnet31-1by16-1by8_6e_st_mj
In Collection: NRTR
Config: configs/textrecog/nrtr/nrtr_resnet31-1by16-1by8_6e_st_mj.py
Metadata:
Training Data:
- SynthText
- Syn90k
Results:
- Task: Text Recognition
Dataset: IIIT5K
Metrics:
word_acc: 0.9470
- Task: Text Recognition
Dataset: SVT
Metrics:
word_acc: 0.8964
- Task: Text Recognition
Dataset: ICDAR2013
Metrics:
word_acc: 0.9399
- Task: Text Recognition
Dataset: ICDAR2015
Metrics:
word_acc: 0.7357
- Task: Text Recognition
Dataset: SVTP
Metrics:
word_acc: 0.7969
- Task: Text Recognition
Dataset: CT80
Metrics:
word_acc: 0.8854
Weights: 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

10
configs/textrecog/robust_scanner/README.md
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@ -40,12 +40,10 @@ The attention-based encoder-decoder framework has recently achieved impressive r
## Results and Models
Coming Soon!
| Methods | GPUs | | Regular Text | | | | Irregular Text | | download |
| :--------------------------------------------------------------------------------------------------: | :--: | :----: | :----------: | :--: | :-: | :--: | :------------: | :--: | :----------------------: |
| | | IIIT5K | SVT | IC13 | | IC15 | SVTP | CT80 | |
| [RobustScanner](configs/textrecog/robust_scanner/robustscanner_resnet31_5e_st-sub_mj-sub_sa_real.py) | | | | | | | | | [model](<>) \| [log](<>) |
| Methods | GPUs | | Regular Text | | | | Irregular Text | | download |
| :---------------------------------------------------------------------: | :--: | :----: | :----------: | :----: | :-: | :----: | :------------: | :----: | :----------------------------------------------------------------------: |
| | | IIIT5K | SVT | IC13 | | IC15 | SVTP | CT80 | |
| [RobustScanner](/configs/textrecog/robust_scanner/robustscanner_resnet31_5e_st-sub_mj-sub_sa_real.py) | 4 | 0.9510 | 0.8934 | 0.9320 | | 0.7559 | 0.8078 | 0.8715 | [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) |
## References

16
configs/textrecog/robust_scanner/metafile.yml
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@ -6,7 +6,7 @@ Collections:
- Adam
Epochs: 5
Batch Size: 1024
Training Resources: 16x GeForce GTX 1080 Ti
Training Resources: 4x NVIDIA A100-SXM4-80GB
Architecture:
- ResNet31OCR
- ChannelReductionEncoder
@ -34,25 +34,25 @@ Models:
- Task: Text Recognition
Dataset: IIIT5K
Metrics:
word_acc:
word_acc: 0.9510
- Task: Text Recognition
Dataset: SVT
Metrics:
word_acc:
word_acc: 0.8934
- Task: Text Recognition
Dataset: ICDAR2013
Metrics:
word_acc:
word_acc: 0.9320
- Task: Text Recognition
Dataset: ICDAR2015
Metrics:
word_acc:
word_acc: 0.7559
- Task: Text Recognition
Dataset: SVTP
Metrics:
word_acc:
word_acc: 0.8078
- Task: Text Recognition
Dataset: CT80
Metrics:
word_acc:
Weights:
word_acc: 0.8715
Weights: 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

12
configs/textrecog/sar/README.md
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@ -40,13 +40,11 @@ Recognizing irregular text in natural scene images is challenging due to the lar
## Results and Models
Coming Soon!
| Methods | Backbone | Decoder | | Regular Text | | | | Irregular Text | | download |
| :-----------------------------------------------------------------: | :---------: | :------------------: | :----: | :----------: | :--: | :-: | :--: | :------------: | :--: | :----------------------: |
| | | | IIIT5K | SVT | IC13 | | IC15 | SVTP | CT80 | |
| [SAR](/configs/textrecog/sar/sar_r31_parallel_decoder_academic.py) | R31-1/8-1/4 | ParallelSARDecoder | | | | | | | | [model](<>) \| [log](<>) |
| [SAR](configs/textrecog/sar/sar_r31_sequential_decoder_academic.py) | R31-1/8-1/4 | SequentialSARDecoder | | | | | | | | [model](<>) \| [log](<>) |
| Methods | Backbone | Decoder | | Regular Text | | | | Irregular Text | | download |
| :-------------------------------------------------------: | :---------: | :------------------: | :----: | :----------: | :----: | :-: | :----: | :------------: | :----: | :---------------------------------------------------------: |
| | | | IIIT5K | SVT | IC13 | | IC15 | SVTP | CT80 | |
| [SAR](/configs/textrecog/sar/sar_r31_parallel_decoder_academic.py) | R31-1/8-1/4 | ParallelSARDecoder | 0.9533 | 0.8841 | 0.9369 | | 0.7602 | 0.8326 | 0.9028 | [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](/configs/textrecog/sar/sar_r31_sequential_decoder_academic.py) | R31-1/8-1/4 | SequentialSARDecoder | 0.9553 | 0.8717 | 0.9409 | | 0.7737 | 0.8093 | 0.8924 | [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) |
## Citation

30
configs/textrecog/sar/metafile.yml
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@ -4,7 +4,7 @@ Collections:
Training Data: OCRDataset
Training Techniques:
- Adam
Training Resources: 48x GeForce GTX 1080 Ti
Training Resources: 8x NVIDIA A100-SXM4-80GB
Epochs: 5
Batch Size: 3072
Architecture:
@ -34,28 +34,28 @@ Models:
- Task: Text Recognition
Dataset: IIIT5K
Metrics:
word_acc:
word_acc: 0.9533
- Task: Text Recognition
Dataset: SVT
Metrics:
word_acc:
word_acc: 0.8841
- Task: Text Recognition
Dataset: ICDAR2013
Metrics:
word_acc:
word_acc: 0.9369
- Task: Text Recognition
Dataset: ICDAR2015
Metrics:
word_acc:
word_acc: 0.7602
- Task: Text Recognition
Dataset: SVTP
Metrics:
word_acc:
word_acc: 0.8326
- Task: Text Recognition
Dataset: CT80
Metrics:
word_acc:
Weights:
word_acc: 0.9028
Weights: 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
- Name: sar_resnet31_sequential-decoder_5e_st-sub_mj-sub_sa_real
In Collection: SAR
@ -74,25 +74,25 @@ Models:
- Task: Text Recognition
Dataset: IIIT5K
Metrics:
word_acc:
word_acc: 0.9553
- Task: Text Recognition
Dataset: SVT
Metrics:
word_acc:
word_acc: 0.8717
- Task: Text Recognition
Dataset: ICDAR2013
Metrics:
word_acc:
word_acc: 0.9409
- Task: Text Recognition
Dataset: ICDAR2015
Metrics:
word_acc:
word_acc: 0.7737
- Task: Text Recognition
Dataset: SVTP
Metrics:
word_acc:
word_acc: 0.8093
- Task: Text Recognition
Dataset: CT80
Metrics:
word_acc:
Weights:
word_acc: 0.8924
Weights: 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

12
configs/textrecog/satrn/README.md
View File

@ -34,13 +34,11 @@ Scene text recognition (STR) is the task of recognizing character sequences in n
## Results and Models
Coming Soon!
| Methods | | Regular Text | | | | Irregular Text | | download |
| :---------------------------------------------------------------------: | :----: | :----------: | :--: | :-: | :--: | :------------: | :--: | :----------------------: |
| | IIIT5K | SVT | IC13 | | IC15 | SVTP | CT80 | |
| [Satrn](/configs/textrecog/satrn/satrn_shallow_5e_st_mj.py) | | | | | | | | [model](<>) \| [log](<>) |
| [Satrn_small](/configs/textrecog/satrn/satrn_shallow-small_5e_st_mj.py) | | | | | | | | [model](<>) \| [log](<>) |
| Methods | | Regular Text | | | | Irregular Text | | download |
| :---------------------------------------------------------------------: | :----: | :----------: | :----: | :-: | :----: | :------------: | :----: | :--------------------------------------------------------------------------: |
| | IIIT5K | SVT | IC13 | | IC15 | SVTP | CT80 | |
| [Satrn](/configs/textrecog/satrn/satrn_shallow_5e_st_mj.py) | 0.9600 | 0.9196 | 0.9606 | | 0.8031 | 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_small](/configs/textrecog/satrn/satrn_shallow-small_5e_st_mj.py) | 0.9423 | 0.8995 | 0.9567 | | 0.7877 | 0.8574 | 0.8507 | [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) |
## Citation

28
configs/textrecog/satrn/metafile.yml
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@ -28,28 +28,28 @@ Models:
- Task: Text Recognition
Dataset: IIIT5K
Metrics:
word_acc:
word_acc: 0.9600
- Task: Text Recognition
Dataset: SVT
Metrics:
word_acc:
word_acc: 0.9196
- Task: Text Recognition
Dataset: ICDAR2013
Metrics:
word_acc:
word_acc: 0.9606
- Task: Text Recognition
Dataset: ICDAR2015
Metrics:
word_acc:
word_acc: 0.8031
- Task: Text Recognition
Dataset: SVTP
Metrics:
word_acc:
word_acc: 0.8837
- Task: Text Recognition
Dataset: CT80
Metrics:
word_acc:
Weights:
word_acc: 0.8993
Weights: https://download.openmmlab.com/mmocr/textrecog/satrn/satrn_shallow_5e_st_mj/satrn_shallow_5e_st_mj_20220915_152443-5fd04a4c.pth
- Name: satrn_shallow-small_5e_st_mj
In Collection: SATRN
@ -62,25 +62,25 @@ Models:
- Task: Text Recognition
Dataset: IIIT5K
Metrics:
word_acc:
word_acc: 0.9423
- Task: Text Recognition
Dataset: SVT
Metrics:
word_acc:
word_acc: 0.8995
- Task: Text Recognition
Dataset: ICDAR2013
Metrics:
word_acc:
word_acc: 0.9567
- Task: Text Recognition
Dataset: ICDAR2015
Metrics:
word_acc:
word_acc: 0.7877
- Task: Text Recognition
Dataset: SVTP
Metrics:
word_acc:
word_acc: 0.8574
- Task: Text Recognition
Dataset: CT80
Metrics:
word_acc:
Weights:
word_acc: 0.8507
Weights: https://download.openmmlab.com/mmocr/textrecog/satrn/satrn_shallow-small_5e_st_mj/satrn_shallow-small_5e_st_mj_20220915_152442-5591bf27.pth