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SHAP-Based Interpretable Object Detection Method for Satellite Imagery
This is the author implementation of SHAP-Based Interpretable Object Detection Method for Satellite Imagery. The implementation of the object detection model (YOLOv3) is based on Pytorch_YOLOv3. The framework of the proposed method can be applied to any differentiable object detection model.
<p align="left"><img src="data/whole_figure.png" height="380"\>Performance
Visualization
<p align="left"><img src="data/vis_tp.png" height="380"\>Please see the paper for details on the results of the evaluation, regularization, and data selection methods.
Installation
Requirements
- Python 3.6.3+
- Numpy
- OpenCV
- Matplotlib
- Pytorch 1.2+
- Cython
- Cuda (verified as operable: v10.2)
- Captum (verified as operable: v0.4.1)
optional:
- tensorboard
- tensorboardX
- CuDNN
Download the original YOLOv3 weights
download the pretrained file from the author's project page:
$ mkdir weights
$ cd weights/
$ bash ../requirements/download_weights.sh
Usage
Please see the test.ipynb
Paper
SHAP-based Methods for Interpretable Object Detection in Satellite Imagery
Hiroki Kawauchi, Takashi Fuse <br>