Implementation of Quality-Aware Network for Plant Parsing
In this repository, we release the QANet code in Pytorch.
- QANet architecture:
- 1 x RTX GPU
- pytorch1.6
- python3.6.8
Install QANet following [INSTALL.md].
Please follow [DATA_PREPARE.md] to download training and evaluating data.
QANet On Plant parsing dataset
Backbone | DOWNLOAD |
---|---|
ResNet50 | GoogleDrive |
please put the pretrained weights in QANet/weights
To train a model with 1 GPUs run:
python tools/train_net_all.py --cfg cfgs/CIHP/QANet/QANet_R-50c_512x384_1x.yaml --gpu_id 0
python tools/test_net_all.py --cfg ckpts/CIHP/QANet/QANet_R-50c_512x384_1x/QANet_R-50c_512x384_1x.yaml --gpu_id 0
QANet is released under the MIT license. And the code is adapted from QANet Human parsing