pse-lite.pytorch
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Published
20 hours ago •
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psenet，prune model, text detection
pse-lite-pytorch
data format
follow icdar15 dataset format, x1,y1,x2,y2,x3,y3,x4,y4,label
image
│ 1.jpg
│ 2.jpg
│ ...
label
│ gt_1.txt
│ gt_2.txt
| ...
Compression model mode one,use lite basemodel
Support switching basemodel,(mobilenet,squeezenet,shufflenet,resnet)
train
python3 train.py --backbone mobile
test
python3 inference.py
Compression model mode two,Channel clipping
Sparse training
python3 train.py --backbone resnet --sr_lr 0.00001
prune model
python3 prune.py
fintune
python3 train_prune_finetune.py
prune test
python3 inference_prune.py
performance
| Method | precision | recall | hmean | prune ratio | modelsize(M) | infer time(v100)(ms) |
|---|---|---|---|---|---|---|
| PSENet-1s (ResNet50) | 0 | 114.5 | 12 | |||
| PSENet-1s (ResNet50) | 0.8179 | 0.7958 | 0.8067 | 0.8 | 25.1 | 9 |
| PSENet-1s (ResNet50) | 0.8124 | 0.7862 | 0.7991 | 0.9 | 16.6 | 7 |
Compression model mode three, Model distillation
reference
- https://github.com/whai362/PSENet
- https://github.com/xiaolai-sqlai/mobilenetv3
- https://github.com/MhLiao/DB
- https://github.com/tanluren/yolov3-channel-and-layer-pruning
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