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Hongbin Sun
GitHub
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@ -26,4 +26,4 @@
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| Method | Backbone | Extra Data | Training set | Test set | #epochs | Test size | Recall | Precision | Hmean | Download |
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| :--------------------------------------------------------------------: | :------: | :---------------------------------------------------------------------------------------------------------------------------------------: | :----------: | :-------: | :-----: | :-------: | :----: | :-------: | :---: | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
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| [PSENet-4s](/configs/textdet/psenet/psenet_r50_fpnf_600e_icdar2015.py) | ResNet50 | - | IC15 Train | IC15 Test | 600 | 2240 | 0.784 | 0.831 | 0.807 | [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) |
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| [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-0af6d62c.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-ac477383.pth) \| [log]() |
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| [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]() |
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47
configs/textrecog/nrtr/README.md
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47
configs/textrecog/nrtr/README.md
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# NRTR
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## Introduction
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[ALGORITHM]
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```bibtex
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@inproceedings{sheng2019nrtr,
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title={NRTR: A no-recurrence sequence-to-sequence model for scene text recognition},
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author={Sheng, Fenfen and Chen, Zhineng and Xu, Bo},
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booktitle={2019 International Conference on Document Analysis and Recognition (ICDAR)},
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pages={781--786},
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year={2019},
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organization={IEEE}
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}
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```
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## Dataset
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### Train Dataset
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| trainset | instance_num | repeat_num | source |
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| :--------: | :----------: | :--------: | :----------------------: |
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| SynthText | 7266686 | 1 | synth |
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| Syn90k | 8919273 | 1 | synth |
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### Test Dataset
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| testset | instance_num | type |
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| :-----: | :----------: | :-------------------------: |
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| IIIT5K | 3000 | regular |
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| SVT | 647 | regular |
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| IC13 | 1015 | regular |
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| IC15 | 2077 | irregular |
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| SVTP | 645 | irregular |
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| CT80 | 288 | irregular |
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## Results and Models
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| Methods | Backbone || Regular Text |||| Irregular Text ||download|
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| :-------: | :---------: | :----: | :----: | :--: | :-: | :--: | :------: | :--: | :-----: |
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| | | IIIT5K | SVT | IC13 | | IC15 | SVTP | CT80 |
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| [NRTR](/configs/textrecog/nrtr/nrtr_r31_academic.py) | R31-1/16-1/8 | 93.9 | 90.0| 93.5 | | 74.5 | 78.5 | 86.5 | [model](https://download.openmmlab.com/mmocr/textrecog/nrtr/nrtr_r31_academic_20210406-954db95e.pth) \| [log](https://download.openmmlab.com/mmocr/textrecog/nrtr/20210406_010150.log.json) |
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**Notes:**
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- `R31-1/16-1/8` means the height of feature from backbone is 1/16 of input image, where 1/8 for width.
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@ -17,7 +17,7 @@ train_pipeline = [
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dict(
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type='ResizeOCR',
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height=32,
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min_width=100,
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min_width=32,
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max_width=100,
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keep_aspect_ratio=False),
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dict(type='ToTensorOCR'),
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@ -40,7 +40,7 @@ test_pipeline = [
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height=32,
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min_width=32,
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max_width=100,
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keep_aspect_ratio=True),
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keep_aspect_ratio=False),
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dict(type='ToTensorOCR'),
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dict(type='NormalizeOCR', **img_norm_cfg),
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dict(
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163
configs/textrecog/nrtr/nrtr_r31_academic.py
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163
configs/textrecog/nrtr/nrtr_r31_academic.py
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_base_ = [
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'../../_base_/default_runtime.py', '../../_base_/recog_models/nrtr.py'
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]
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label_convertor = dict(
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type='AttnConvertor', dict_type='DICT90', with_unknown=True)
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model = dict(
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type='NRTR',
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backbone=dict(
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type='ResNet31OCR',
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layers=[1, 2, 5, 3],
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channels=[32, 64, 128, 256, 512, 512],
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stage4_pool_cfg=dict(kernel_size=(2, 1), stride=(2, 1)),
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last_stage_pool=True),
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encoder=dict(type='TFEncoder'),
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decoder=dict(type='TFDecoder'),
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loss=dict(type='TFLoss'),
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label_convertor=label_convertor,
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max_seq_len=40)
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# optimizer
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optimizer = dict(type='Adam', lr=1e-3)
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optimizer_config = dict(grad_clip=None)
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# learning policy
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lr_config = dict(policy='step', step=[3, 4])
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total_epochs = 6
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img_norm_cfg = dict(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
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train_pipeline = [
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dict(type='LoadImageFromFile'),
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dict(
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type='ResizeOCR',
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height=32,
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min_width=32,
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max_width=160,
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keep_aspect_ratio=True,
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width_downsample_ratio=0.25),
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dict(type='ToTensorOCR'),
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dict(type='NormalizeOCR', **img_norm_cfg),
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dict(
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type='Collect',
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keys=['img'],
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meta_keys=[
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'filename', 'ori_shape', 'img_shape', 'text', 'valid_ratio'
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]),
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]
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test_pipeline = [
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dict(type='LoadImageFromFile'),
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dict(
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type='MultiRotateAugOCR',
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rotate_degrees=[0, 90, 270],
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transforms=[
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dict(
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type='ResizeOCR',
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height=32,
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min_width=32,
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max_width=160,
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keep_aspect_ratio=True,
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width_downsample_ratio=0.25),
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dict(type='ToTensorOCR'),
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dict(type='NormalizeOCR', **img_norm_cfg),
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dict(
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type='Collect',
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keys=['img'],
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meta_keys=[
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'filename', 'ori_shape', 'img_shape', 'valid_ratio'
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]),
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])
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]
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dataset_type = 'OCRDataset'
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train_prefix = 'data/mixture/'
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train_img_prefix1 = train_prefix + \
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'SynthText/synthtext/SynthText_patch_horizontal'
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train_img_prefix2 = train_prefix + 'Syn90k/mnt/ramdisk/max/90kDICT32px'
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train_ann_file1 = train_prefix + 'SynthText/label.lmdb',
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train_ann_file2 = train_prefix + 'Syn90k/label.lmdb'
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train1 = dict(
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type=dataset_type,
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img_prefix=train_img_prefix1,
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ann_file=train_ann_file1,
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loader=dict(
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type='LmdbLoader',
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repeat=1,
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parser=dict(
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type='LineStrParser',
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keys=['filename', 'text'],
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keys_idx=[0, 1],
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separator=' ')),
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pipeline=train_pipeline,
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test_mode=False)
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train2 = {key: value for key, value in train1.items()}
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train2['img_prefix'] = train_img_prefix2
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train2['ann_file'] = train_ann_file2
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test_prefix = 'data/mixture/'
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test_img_prefix1 = test_prefix + 'IIIT5K/'
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test_img_prefix2 = test_prefix + 'svt/'
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test_img_prefix3 = test_prefix + 'icdar_2013/'
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test_img_prefix4 = test_prefix + 'icdar_2015/'
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test_img_prefix5 = test_prefix + 'svtp/'
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test_img_prefix6 = test_prefix + 'ct80/'
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test_ann_file1 = test_prefix + 'IIIT5K/test_label.txt'
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test_ann_file2 = test_prefix + 'svt/test_label.txt'
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test_ann_file3 = test_prefix + 'icdar_2013/test_label_1015.txt'
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test_ann_file4 = test_prefix + 'icdar_2015/test_label.txt'
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test_ann_file5 = test_prefix + 'svtp/test_label.txt'
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test_ann_file6 = test_prefix + 'ct80/test_label.txt'
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test1 = dict(
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type=dataset_type,
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img_prefix=test_img_prefix1,
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ann_file=test_ann_file1,
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loader=dict(
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type='HardDiskLoader',
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repeat=1,
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parser=dict(
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type='LineStrParser',
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keys=['filename', 'text'],
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keys_idx=[0, 1],
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separator=' ')),
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pipeline=test_pipeline,
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test_mode=True)
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test2 = {key: value for key, value in test1.items()}
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test2['img_prefix'] = test_img_prefix2
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test2['ann_file'] = test_ann_file2
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test3 = {key: value for key, value in test1.items()}
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test3['img_prefix'] = test_img_prefix3
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test3['ann_file'] = test_ann_file3
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test4 = {key: value for key, value in test1.items()}
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test4['img_prefix'] = test_img_prefix4
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test4['ann_file'] = test_ann_file4
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test5 = {key: value for key, value in test1.items()}
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test5['img_prefix'] = test_img_prefix5
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test5['ann_file'] = test_ann_file5
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test6 = {key: value for key, value in test1.items()}
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test6['img_prefix'] = test_img_prefix6
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test6['ann_file'] = test_ann_file6
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data = dict(
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samples_per_gpu=128,
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workers_per_gpu=4,
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train=dict(type='ConcatDataset', datasets=[train1, train2]),
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val=dict(
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type='ConcatDataset',
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datasets=[test1, test2, test3, test4, test5, test6]),
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test=dict(
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type='ConcatDataset',
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datasets=[test1, test2, test3, test4, test5, test6]))
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evaluation = dict(interval=1, metric='acc')
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@ -15,7 +15,7 @@
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- Linux (Windows is not officially supported)
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- Python 3.7
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- PyTorch 1.5
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- PyTorch 1.5 or higher
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- torchvision 0.6.0
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- CUDA 10.1
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- NCCL 2
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