M1 Abrams Tank Classifier

Project Goal

Train a CNN model to determine whether a given image contains an M1 Abrams tank or not.

Motivation

Open source research such as that conducted by Bellingcat is a tedious process, especially so when it involves analyzing photos and videos from a conflict zone. To accelerate this process, I wanted to build a proof of concept CNN that would accurately determine if an image contains an M1 Abrams tank.

Dataset

I used the Fatkun Batch Download Image extension for Google Chrome to download results of a Google Image search. The resulting dataset required manual cleaning as the downloader is fairly brute force and Google Image searches were sometimes polluted with images of non-Abrams tanks.

Training

I used Google Colab to provide added compute power and TensorBoard to visual the model's learning in real time.

Conclusions on v1

The model was successfully able to detect the presence of an M1 Abrams tank in each of the four test images I passed it from various Iraqi militia social media accounts. More broadly, the model performs well on the testing data.

TensorBoard Graph showing loss on validation data over 100 epochs

TensorBoard Graph showing accuracy on validation data over 100 epochs

Next Steps

1. Further adjust layers in the CNN to see if I can further reduce false negatives.
2. Determine what other systems or equipment are of similar "high risk" and train models to detect them.