Comments (6)
ICDAR Dataset
데이터 이름 | training set | test set | val set | 언어 | 형태 |
---|---|---|---|---|---|
IC13 | 229 | 233 | En | horizontal | |
IC15 - Incidental Scene Text | 1000 | 500 | En | Google Glass, quadrilaterals | |
IC17 | 7,200 | 9,000 | 1,800 | multi-lingual | |
MSRA-TD500 | 300 | 200 | EN, CH | line-level | |
TotalText | 1255 | 300 | curved texts | ||
CTW-1500 | 1000 | 500 | |||
COCO-Text | 43,686 | 20,000 |
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Paper 학습 - IC15 Case
research | Pretrain | Training Data | augmentation |
---|---|---|---|
PixelLink | No | IC15-train | |
SegLink | SynthText | ,IC15-train | |
EAST | ImageNet | IC15-train ,IC13-train(229개) | |
Text-Block FCN | ImageNet | IC15-train | Y |
FOTS | ImageNet, SynthText | MLT 학습/val set, IC15-train+IC13-train | Y, i) longer sides of images are resized from 640 pixels to 2560 pixels, ii) rotated in range [−10, 10] ] randomly, iii) rescaled with ratio from 0.8 to 1.2 iv) 640×640 random samples are cropped from the transformed images. |
- FOTS - End to End라 애매
- we first train our model using 9000 images from ICDAR 2017 MLT training and validation datasets, then we use 1000 ICDAR 2015 training images and 229 ICDAR 2013 training images to fine-tune our model.
- 2017 MLT 학습셋+val set를 이용하여 첫번째 학습. 이후 ICDAR 2015+CDAR 2013 학습이미지로 finetuning
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Paper 학습 - TD500
research | Pretrained | Training Data | augmentation |
---|---|---|---|
PixelLink | IC15-train | ITD500-train + HUST-TR400 | |
EAST | ImageNet | TD500-train, HUSTTR400 | |
Text-Block FCN | ImageNet | TD500-train | Y |
[1] | ImageNet | TD500-train + HUST-TR400 | Y |
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Paper 학습 - IC13
research | Pretrain | Training Data | augmentation |
---|---|---|---|
PixelLink | IC15-train | IC13-train,TD500-train and HUST-TR400 | Y |
FOTS | SynthText, ImageNet | MLT 학습셋+val set, IC15-train+IC13-train | Y IC15와 동일 |
[1] | ImageNet | IC15-train+IC13-train | Y |
- FOTS
- We use model trained on ImageNet dataset [29] as our pre-trained model. The training process includes two steps: first we use Synth800k dataset [10] to train the network for 10 epochs, and then real data is adopted to fine-tune the model until convergence. Different training datasets are adopted for different tasks, which will be discussed in Sec. 4. Some blurred text regions in ICDAR 2015 and ICDAR 2017 MLT datasets are labeled as “DO NOT CARE”, and we ignore them in training.
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Paper 학습 - MLT
research | Pretrain | Training Data | augmentation |
---|---|---|---|
FOTS | SynthText, ImageNet | MLT 학습셋+val set | Y IC15와 동일 |
[1] | ImageNet | MLT 학습셋+val set | Y |
- FOTS
- We use model trained on ImageNet dataset [29] as our pre-trained model. The training process includes two steps: first we use Synth800k dataset [10] to train the network for 10 epochs, and then real data is adopted to fine-tune the model until convergence. Different training datasets are adopted for different tasks, which will be discussed in Sec. 4. Some blurred text regions in ICDAR 2015 and ICDAR 2017 MLT datasets are labeled as “DO NOT CARE”, and we ignore them in training.
- [1] : 구성되어 있다고만 있지, valset을 합쳐적용했다는 의미는 없는듯..예측.
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Paper 학습 - ICDAR2017-RCTW
research | Pretrain | Training Data | augmentation |
---|---|---|---|
[1] | ImageNet | RCTW | Y |
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Related Issues (1)
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