Shape Robust Text Detection with Progressive Scale Expansion Network
Requirements
- pytorch 1.0
- pyclipper
- opencv3
Update
20190401
- add author loss, the results are compared in Performance
Download
resnet50 and resnet152 model on icdar 2015: bauduyun extract code: rxjf
Train
- config the
trainroot,testrootin config.py - use fellow script to run
python3 train.pyTest
eval.py is used to test model on test dataset
- config
model_path,data_path,gt_path,save_pathin eval.py - use fellow script to test
python3 eval.pyPredict
predict.py is used to inference on single image
- config
model_path,img_path,gt_path,save_pathin predict.py - use fellow script to predict
python3 predict.pyThe project is still under development.
Performance
ICDAR 2015
only train on ICDAR2015 dataset with single NVIDIA 1080Ti
my implementation with my loss use adam and warm_up
| Method | Precision (%) | Recall (%) | F-measure (%) | FPS(1080Ti) |
|---|---|---|---|---|
| PSENet-1s with resnet50 batch 8 | 81.13 | 77.03 | 79.03 | 1.76 |
| PSENet-2s with resnet50 batch 8 | 81.36 | 77.13 | 79.18 | 3.55 |
| PSENet-4s with resnet50 batch 8 | 81.00 | 76.55 | 78.71 | 4.43 |
| PSENet-1s with resnet152 batch 4 | 85.45 | 80.06 | 82.67 | 1.48 |
| PSENet-2s with resnet152 batch 4 | 85.42 | 80.11 | 82.68 | 2.56 |
| PSENet-4s with resnet152 batch 4 | 83.93 | 79.00 | 81.39 | 2.99 |
my implementation with my loss use adam and MultiStepLR
| Method | Precision (%) | Recall (%) | F-measure (%) | FPS(1080Ti) |
|---|---|---|---|---|
| PSENet-1s with resnet50 batch 8 | 83.39 | 79.29 | 81.29 | 1.76 |
| PSENet-2s with resnet50 batch 8 | 83.22 | 79.05 | 81.08 | 3.55 |
| PSENet-4s with resnet50 batch 8 | 82.57 | 78.23 | 80.34 | 4.43 |
| PSENet-1s with resnet152 batch 4 | 85.33 | 79.87 | 82.51 | 1.48 |
| PSENet-2s with resnet152 batch 4 | 85.36 | 79.73 | 82.45 | 2.56 |
| PSENet-4s with resnet152 batch 4 | 83.95 | 78.86 | 81.33 | 2.99 |
my implementation with author loss use adam and warm_up
| Method | Precision (%) | Recall (%) | F-measure (%) | FPS(1080Ti) |
|---|---|---|---|---|
| PSENet-1s with resnet50 batch 8 | 83.33 | 77.75 | 80.44 | 1.76 |
| PSENet-2s with resnet50 batch 8 | 83.01 | 77.66 | 80.24 | 3.55 |
| PSENet-4s with resnet50 batch 8 | 82.38 | 76.98 | 79.59 | 4.43 |
| PSENet-1s with resnet152 batch 4 | 85.16 | 79.87 | 82.43 | 1.48 |
| PSENet-2s with resnet152 batch 4 | 85.03 | 79.63 | 82.24 | 2.56 |
| PSENet-4s with resnet152 batch 4 | 84.53S | 79.20 | 81.77 | 2.99 |
my implementation with author loss use adam and MultiStepLR
| Method | Precision (%) | Recall (%) | F-measure (%) | FPS(1080Ti) |
|---|---|---|---|---|
| PSENet-1s with resnet50 batch 8 | 83.93 | 79.48 | 81.65 | 1.76 |
| PSENet-2s with resnet50 batch 8 | 84.17 | 79.63 | 81.84 | 3.55 |
| PSENet-4s with resnet50 batch 8 | 83.50 | 78.71 | 81.04 | 4.43 |
| PSENet-1s with resnet152 batch 4 | 85.16 | 79.58 | 82.28 | 1.48 |
| PSENet-2s with resnet152 batch 4 | 85.13 | 79.15 | 82.03 | 2.56 |
| PSENet-4s with resnet152 batch 4 | 84.40 | 78.71 | 81.46 | 2.99 |
official implementation use SGD and StepLR
| Method | Precision (%) | Recall (%) | F-measure (%) | FPS(1080Ti) |
|---|---|---|---|---|
| PSENet-1s with resnet50 batch 8 | 84.15 | 80.26 | 82.16 | 1.76 |
| PSENet-2s with resnet50 batch 8 | 83.61 | 79.82 | 81.67 | 3.72 |
| PSENet-4s with resnet50 batch 8 | 81.90 | 78.23 | 80.03 | 4.51 |
| PSENet-1s with resnet152 batch 4 | 82.87 | 78.76 | 80.77 | 1.53 |
| PSENet-2s with resnet152 batch 4 | 82.33 | 78.33 | 80.28 | 2.61 |
| PSENet-4s with resnet152 batch 4 | 81.19 | 77.13 | 79.11 | 3.00 |
examples
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