Transfer learning applied to train an image classifier for classifying remote sensing data into three classes:
- aircrafts
- ships
- none
The program requires the following dependencies (easy to install using pip: pip3 install -r requirements.txt):
- python 3.5
- pytorch
- numpy
- pandas
- matplotlib
- Pillow
- CUDA (for using GPU)
The dataset can be downloaded or used from here
After downloading it can be extracted by:
unzip src.zip
The structure of extracted folder is shown below:
src
├── test [121 entries]
│ ├── testing.csv
├── train
│ ├── aircrafts [500 entries]
│ ├── none [500 entries]
│ ├── ships [500 entries]
│ └── training.csv
├── main.py
├── utils_jnb.py
└── utils.pyTo train the model, simply run python3 main.py.
Once trained, you can test the results with python3 main.py --test True (make sure that you have a saved model file : model.pt before testing)
Here are some flags which could be useful. For more help and options, use python3 main.py -h:
- --directory : if the current directory is not
src. - --batch : to change the training batch size (default = 32)
- --epochs : to change the number of epochs (default = 25)
- --val : to change the fraction of validation set out of total training set (default = 0.1)
(or) Simply run the jupyter notebook file in google colab (make sure that the src.zip file is in right place)