SMENet

This is a pytorch implementation of SMENet

Requirements

1. pytorch == 1.1.0

2. cuda 8.0

3. python == 3.7

4. opencv(CV2)

Data Prepare

1. Please download NWPU VHR-10
2. Convert to PASCAL VOC data format
3. Create dataset folder

   ./SMENet/VOCNWPU/

   4.Data format

   ├── VOCNWPU
   │   ├── VOC2012
   │       ├── Annotations
   │       ├── JPEGImages
   │       ├── ImageSets
   │         ├── train.txt
   │         ├── test.txt
   |         ├── val.txt
   |         ├── trainval.txt

Demo

1.Please download weights file SMENet.pth, and put it to:

./SMENet/weights/

2. Run visual_SMENet.py:

   cd ./SMENet/demo/visual_SMENet.py
   modify parser.add_argument('--trained_model', default='../weights/SMENet.pth', type=str, help='Trained state_dict file path to open')
   python visual_SMENet.py

   Train

   if you want to train your own dataset:

3. Convert your dataset to PASCAL VOC and put it to ./SMENet/dataset-file-name/
4. Modify parameters HOME and num_classes in ./SMENet/data/config.py : HOME= absolute path of the SMENet folder
5. Modify parameters VOC_CLASSES in ./SMENet/data/voc0712.py
6. python train_SMENet.py
7. save *.pth to weights, like ./SMENet/weights/*.pth

   ## Eval
   if you want to eval trained model:

8. cd ./SMENet/eval.py
9. Modify parser.add_argument('--trained_model', default='weights/*.pth', type=str, help='Trained state_dict file path to open')
10. python eval.py