- Pytorch version 0.4 or higher.
- Python version 3.0 or higher.
We test a trained PSSNet on a acacia example image as follows:
python main.py -image_path figures/test.png \
-model_path checkpoints/best_model_acacia_ResUnet.pth \
-model_name ResUnet
or you can run the test.sh
bash test.sh
#the content of an example is listed as bellow:
python main.py -image_path ./figures/oilpalm/test_image.jpg \
-model_path checkpoints/best_model_acacia_ResUnet.pth \
-model_name ResUnet-
Acacia dataset & Oil Palm dataset
https://1drv.ms/f/s!AsFz7oLq0ulekgDLUWqpwWBtuXnh
-
Sorghum Plant
https://engineering.purdue.edu/~sorghum/dataset-plant-centers-2016/
for Oil Palm dataset
python main.py -m train -e oilpalm
for Acacia dataset
python main.py -m train -e acaciaIf you want to train other datasets by yourself, just change the -e parameter.
python main.py -image_path figures/test.png \
-model_path checkpoints/best_model_acacia_ResUnet.pth \
-model_name ResUnet