- add author loss, The results are compared as follows
Method | Precision (%) | Recall (%) | F-measure (%) | fps |
---|---|---|---|---|
PSENet-1s with resnet152 batch 8 myloss | 85.47 | 78.76 | 81.98 | 1.49 |
PSENet-1s with resnet152 batch 8 author loss | 85.04 | 79.68 | 82.27 | 1.48 |
resnet50 and resnet152: bauduyun extract code: rxjf
- config the
trainroot
,testroot
in config.py - use fellow script to run
python3 train.py
eval.py is used to test model on test dataset
- config
model_path
,data_path
,gt_path
,save_path
in eval.py - use fellow script to test
python3 eval.py
predict.py is used to inference on single image
- config
model_path
,img_path
,gt_path
,save_path
in predict.py - use fellow script to predict
python3 predict.py
The project is still under development.
only train with ICDAR2015 dataset with single 1080ti
my implementation with author loss use adam and MultiStepLR
Method | Precision (%) | Recall (%) | F-measure (%) | fps |
---|---|---|---|---|
PSENet-1s with resnet50 batch 8 | 83.49 | 79.62 | 81.51 | 1.76 |
PSENet-2s with resnet50 batch 8 | 83.37 | 79.68 | 81.48 | 3.55 |
PSENet-4s with resnet50 batch 8 | 82.44 | 78.91 | 80.63 | 4.43 |
PSENet-1s with resnet152 batch 4 | 85.04 | 79.68 | 82.27 | 1.48 |
PSENet-2s with resnet152 batch 4 | 84.88 | 79.20 | 81.94 | 2.56 |
PSENet-4s with resnet152 batch 4 | 83.81 | 78.76 | 81.21 | 2.99 |
my implementation with my loss use adam and warm_up
Method | Precision (%) | Recall (%) | F-measure (%) | fps |
---|---|---|---|---|
PSENet-1s with resnet50 batch 8 | tbd | tbd | tbd | 1.76 |
PSENet-2s with resnet50 batch 8 | tbd | tbd | tbd | 3.55 |
PSENet-4s with resnet50 batch 8 | tbd | tbd | tbd | 4.43 |
PSENet-1s with resnet152 batch 4 | 85.24 | 80.11 | 82.60 | 1.48 |
PSENet-2s with resnet152 batch 4 | 85.02 | 80.06 | 82.46 | 2.56 |
PSENet-4s with resnet152 batch 4 | 83.46 | 79.00 | 81.17 | 2.99 |
official implementation use SGD and StepLR
Method | Precision (%) | Recall (%) | F-measure (%) | fps |
---|---|---|---|---|
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 |