planes-detector is an ergo based project that relies on a convolutional neural network to detect airplanes from satellite imagery.
Download the PlanesNet dataset (the zip file with pictures) and extract it somewhere, then:
ergo train /path/to/planes-detector --dataset /path/to/planesnet/pictures
This will make ergo preprocess the pictures, import them as a csv dataset and start the training algorithm (see how to train on GPU) that can be monitored with
After training is completed, you can view the model structure and how the accuracy and loss metrics changed during training with:
ergo view /path/to/planes-detector
Once the training is completed, you can clean the project from temporary datasets and start a REST API server to test the model:
cd /path/to/planes-detector ergo clean . ergo serve .
You can test the predictions with
Alternatively you can use the file
model.h5 (created inside the project folder after training) by loadeding it as you would normally do with Keras API for evaluation.
planes-detector was made with