SAR_yolov3

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Best Accruacy:speed ratio SAR Ship detection in the world.

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# SAR_yolov3

# Welcome to SAR SHIP DETECTION

We have applied YOLO-V3 Object detection on  **SAR Satellite** Images. We are detecting ship by these Images where SAR sensors are immune to bad weather and night time which a great way of detecting . We applied YOLO-V3 to it and it gives best accuracy:speed ratio in the world among all other models and methods applied. We are further looking to improve the accuracy where the current accuracy is **90.25 %**.

# Files

We have included the Config file which is the model architecture for the **darknet** deep learning framework where we changed the models in many ways for our results , from data augmentation to hyper-parameters.
Anchors boxes is also given in this repositories.
Some scripts which converted the **VOC Xml**  into **Darknet** Text Format

## Results

![Prediction 1](https://github.com/humblecoder612/SAR_yolov3/blob/master/images/1.jpg)

**PREDICTION 1**

![Prediction 2](https://github.com/humblecoder612/SAR_yolov3/blob/master/images/2.jpg)

**PREDICTION 1**

## Submission 
We have written a research paper on this project and submitted into a Springer conference.

SAR_yolov3

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