SOLOV2代码和预训练模型

# ABCNetv1 & ABCNetv2 [ABCNetv1](https://openaccess.thecvf.com/content_CVPR_2020/html/Liu_ABCNet_Real-Time_Scene_Text_Spotting_With_Adaptive_Bezier-Curve_Network_CVPR_2020_paper.html) is an efficient end-to-end scene text spotting framework over 10x faster than previous state of the art. It's published in IEEE Conf. Comp Vis Pattern Recogn.'2020 as an oral paper. [ABCNetv2](https://ieeexplore.ieee.org/document/9525302) is published in TPAMI. ## Models ### Experimental resutls on CTW1500: Name | inf. time | e2e-None-hmean | e2e-Full-hmean | det-hmean | download --- |:---:|:---:|:---:|:---:|:---: [v1-CTW1500-finetune](CTW1500/attn_R_50.yaml) | 8.7 FPS | 53.2 | 74.7 | 84.4 | [pretrained-model](https://universityofadelaide.box.com/shared/static/okeo5pvul5v5rxqh4yg8pcf805tzj2no.pth) [v2-CTW1500-finetune](CTW1500/v2_attn_R_50.yaml) | 7.2 FPS | 57.7 | 75.8 | 85.0 | [finetuned-model](https://drive.google.com/file/d/12HV1dHjw1POdhOiHXPPXcGnjyp-3IuQv/view?usp=sharing) ### Experimental resutls on TotalText: Config | inf. time | e2e-None-hmean | e2e-Full-hmean | det-hmean | download --- |:---------:|:---------:|:---------:|:---------:|:---: [v1-pretrain](Pretrain/attn_R_50.yaml) | 11.3 FPS | 58.1 | 75.5 | 80.0 | [pretrained-model](https://cloudstor.aarnet.edu.au/plus/s/dEzxhTlEumICiq0/download) [v1-totaltext-finetune](TotalText/attn_R_50.yaml) | 11.3 FPS | 67.1 | 81.1 | 86.0 | [finetuned-model](https://cloudstor.aarnet.edu.au/plus/s/tYsnegjTs13MwwK/download) [v2-pretrain](Pretrain/v2_attn_R_50.yaml) | 7.8 FPS | 63.5 | 78.4 | 83.7 | [pretrained-model](https://drive.google.com/file/d/1v5C9klxBuNVBaLVxZRCy1MYnwEu0F25q/view?usp=sharing) [v2-totaltext-finetune](TotalText/v2_attn_R_50.yaml) | 7.7 FPS | 71.8 | 83.4 | 87.2 | [finetuned-model](https://drive.google.com/file/d/1jR5-A-7ITvjdSx3kWVE9bMgh_biMsqcR/view?usp=sharing) ### Experimental resutls on [ICDAR2015](https://rrc.cvc.uab.es/?ch=4): Name | e2e-None | e2e-Generic | e2e-Weak | e2e-Strong | det-hmean | download --- |:---:|:---:|:---:|:---:|:---:|:---: [v1-icdar2015-pretrain](Pretrain/v1_ic15_attn_R_50.yaml) | 38.0 | 50.8 | 59.0 | 65.8 | 83.2 | [pretrained-model](https://drive.google.com/file/d/1MZab_ftY8qGCurW1rwZBx5ftquZgcf4e/view?usp=sharing) [v1-icdar2015-finetune](ICDAR2015/v1_attn_R_50.yaml) | 57.1 | 66.8 | 74.1 | 79.2 | 86.8 | [finetuned-model](https://drive.google.com/file/d/15eEctI4CqTxtcMAMcYiHIysYw3l53BGQ/view?usp=sharing) [v2-icdar2015-pretrain](Pretrain/v2_ic15_attn_R_50.yaml) | 59.5 | 69.0 | 75.8 | 80.8 | 86.2 | [pretrained-model](https://drive.google.com/file/d/17xIB064Jlq31z875POrw9a3aDmg04C3y/view?usp=sharing) [v2-icdar2015-finetune](ICDAR2015/v2_attn_R_50.yaml) | 66.3 | 73.2 | 78.8 | 83.7 | 88.2 | [finetuned-model](https://drive.google.com/file/d/1bxVxu7kX13S1_xYvCfUfomO8hSZGNZUl/view?usp=sharing) ### Experimental resutls on [ReCTS](https://rrc.cvc.uab.es/?ch=12): Name | inf. time | det-recall | det-precision | det-hmean | 1 - NED | download --- |:---:|:---:|:---:|:---:|:---:|:---: [v2-Chinese-pretrained](Pretrain/v2_chn_attn_R_50.yaml) | -| - | - | - | - | [pretrained-model](https://drive.google.com/file/d/1AU8yAMNm2H8ryB7uIvp2HUHpCso7eyNH/view?usp=sharing) [v2-ReCTS-finetune](ReCTS/v2_chn_attn_R_50.yaml) | 8 FPS | 87.9 | 92.9 | 90.33 | 63.9 | [finetuned-model](https://drive.google.com/file/d/1YTlC5jkh6y3g1RRc_hDs4m_tcU2J20fe/view?usp=sharing) ### Experimental resutls on [MSRA-TD500](http://www.iapr-tc11.org/mediawiki/index.php/MSRA_Text_Detection_500_Database_%28MSRA-TD500%29): Name | det-recall | det-precision | det-hmean | download --- |:---:|:---:|:---:|:---: [v2-TD500-finetune](https://github.com/aim-uofa/AdelaiDet/issues/537) | 81.9 | 89.0 | 85.3 | [finetuned-model](https://github.com/aim-uofa/AdelaiDet/issues/537) * Note the pretrained model for TD500 is the Chinese pretrained used for ReCTS. As MSRA-TD is a det. only dataset, a small amount of [modification](https://github.com/aim-uofa/AdelaiDet/issues/537) is needed. ## Quick Start (ABCNetv1) ### Inference with our trained Models 1. Select the model and config file above, for example, `configs/BAText/CTW1500/attn_R_50.yaml`. 2. Run the demo with ``` wget -O ctw1500_attn_R_50.pth https://universityofadelaide.box.com/shared/static/okeo5pvul5v5rxqh4yg8pcf805tzj2no.pth python demo/demo.py \ --config-file configs/BAText/CTW1500/attn_R_50.yaml \ --input datasets/CTW1500/ctwtest_text_image/ \ --opts MODEL.WEIGHTS ctw1500_attn_R_50.pth ``` or ``` wget -O tt_attn_R_50.pth https://cloudstor.aarnet.edu.au/plus/s/tYsnegjTs13MwwK/download python demo/demo.py \ --config-file configs/BAText/TotalText/attn_R_50.yaml \ --input datasets/totaltext/test_images/ \ --opts MODEL.WEIGHTS tt_attn_R_50.pth ``` or ``` # Download v1_ic15_finetuned.pth above python demo/demo.py \ --config-file configs/BAText/ICDAR2015/v1_attn_R_50.yaml \ --input datasets/icdar2015/test_images \ --opts MODEL.WEIGHTS v1_ic15_finetuned.pth ``` ### Train Your Own Models To train a model with "train_net.py", first setup the corresponding datasets following [datasets/README.md](../../datasets/README.md) or using the following script: ``` cd datasets/ wget https://drive.google.com/file/d/1we4iwZNA80q-yRoEKqB66SuTa1tPbhZu/view?usp=sharing -O totaltext.zip unzip totaltext.zip rm totaltext.zip wget https://drive.google.com/file/d/1ntlnlnQHZisDoS_bgDvrcrYFomw9iTZ0/view?usp=sharing -O CTW1500.zip unzip CTW1500.zip rm CTW1500.zip wget https://drive.google.com/file/d/1J94245rU-s7KTecNQRD3KXG04ICZhL9z/view?usp=sharing -O icdar2015.zip unzip icdar2015.zip rm icdar2015.zip mkdir evaluation cd evaluation wget -O gt_ctw1500.zip https://cloudstor.aarnet.edu.au/plus/s/xU3yeM3GnidiSTr/download wget -O gt_totaltext.zip https://cloudstor.aarnet.edu.au/plus/s/SFHvin8BLUM4cNd/download wget -O gt_icdar2015.zip https://drive.google.com/file/d/1wrq_-qIyb_8dhYVlDzLZTTajQzbic82Z/view?usp=sharing ``` * Note (synthetic and mlt2017 datasets need to be downloaded through [datasets/README.md](../../datasets/README.md).) You can also prepare your custom dataset following the [example scripts](https://universityofadelaide.box.com/s/phqfzpvhe0obmkvn17akn9qw47u1m44i). Pretrainining with synthetic data (For Totaltext and CTW1500): ``` OMP_NUM_THREADS=1 python tools/train_net.py \ --config-file configs/BAText/Pretrain/attn_R_50.yaml \ --num-gpus 4 \ OUTPUT_DIR text_pretraining/attn_R_50 ``` Pretrainining with synthetic data (For ICDAR2015): ``` OMP_NUM_THREADS=1 python tools/train_net.py \ --config-file configs/BAText/Pretrain/v1_ic15_attn_R_50.yaml \ --num-gpus 4 \ OUTPUT_DIR text_pretraining/v1_ic15_attn_R_50 ``` Finetuning on TotalText: ``` OMP_NUM_THREADS=1 python tools/train_net.py \ --config-file configs/BAText/TotalText/attn_R_50.yaml \ --num-gpus 4 \ MODEL.WEIGHTS text_pretraining/attn_R_50/model_final.pth ``` Finetuning on CTW1500: ``` OMP_NUM_THREADS=1 python tools/train_net.py \ --config-file configs/BAText/CTW1500/attn_R_50.yaml \ --num-gpus 4 \ MODEL.WEIGHTS text_pretraining/attn_R_50/model_final.pth ``` Finetuning on ICDAR2015: ``` OMP_NUM_THREADS=1 python tools/train_net.py \ --config-file configs/BAText/ICDAR2015/v1_attn_R_50.yaml \ --num-gpus 4 \ MODEL.WEIGHTS text_pretraining/v1_ic15_attn_R_50/model_final.pth ``` ### Evaluate on Trained Model Download test GT [here](../../datasets/README.md) so that the directory has the following structure: ``` datasets |_ evaluation | |_ gt_totaltext.zip | |_ gt_ctw1500.zip | |_ gt_icdar2015.zip ``` Producing both (w/wo lexion) e2e and detection results on CTW1500: ``` wget -O ctw1500_attn_R_50.pth https://universityofadelaide.box.com/shared/static/okeo5pvul5v5rxqh4yg8pcf805tzj2no.pth python tools/train_net.py \ --config-file configs/BAText/CTW1500/attn_R_50.yaml \ --eval-only \ MODEL.WEIGHTS ctw1500_attn_R_50.pth ``` or Totaltext: ``` wget -O tt_attn_R_50.pth https://cloudstor.aarnet.edu.au/plus/s/tYsnegjTs13