Awesome
Shape Robust Text Detection with Progressive Scale Expansion Network
Requirements
- pytorch 1.1
- torchvision 0.3
- pyclipper
- opencv3
- gcc 4.9+
Update
20190401
- add author loss, the results are compared in Performance
Download
resnet50 and resnet152 model on icdar 2015:
-
bauduyun extract code: rxjf
Data Preparation
follow icdar15 dataset format
img
│ 1.jpg
│ 2.jpg
│ ...
gt
│ gt_1.txt
│ gt_2.txt
| ...
Train
- config the
trainroot,testrootin config.py - use following script to run
python3 train.py
Test
eval.py is used to test model on test dataset
- config
model_path,data_path,gt_path,save_pathin eval.py - use following script to test
python3 eval.py
Predict
predict.py is used to inference on single image
- config
model_path,img_path,gt_path,save_pathin predict.py - use following script to predict
python3 predict.py
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
