# planes detector

Published articles for planes detector.

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## Presenting Project Ergo: How to Build an Airplane Detector for Satellite Imagery With Deep Learning

DevFeed: [Presenting Project Ergo: How to Build an Airplane Detector for Satellite Imagery With Deep Learning](<https://devfeed.tech/articles/presenting-project-ergo-how-to-build-an-airplane-detector-for-satellite-imagery-with-deep-learning-41260.md>)

Original publisher: [Read original article](<https://www.evilsocket.net/2018/11/22/Presenting-project-Ergo-how-to-build-an-airplane-detector-for-satellite-imagery-with-Deep-Learning/>)

Author: Simone Margaritelli

Published: 2018-11-22T17:15:50Z

Content type: tutorial

Language: en

Sources: [evilsocket](<https://devfeed.tech/sources/evilsocket.md>)

Topics: [Keras](<https://devfeed.tech/topics/keras.md>), [Tensorflow](<https://devfeed.tech/topics/tensorflow.md>), [Framework](<https://devfeed.tech/topics/framework.md>), [Deep learning](<https://devfeed.tech/topics/deep-learning.md>), [Deep neural networks](<https://devfeed.tech/topics/deep-neural-networks.md>), [datasets](<https://devfeed.tech/topics/datasets.md>), [Open Source](<https://devfeed.tech/topics/open-source.md>), [Code](<https://devfeed.tech/topics/code.md>), [Development](<https://devfeed.tech/topics/development.md>), [Back end](<https://devfeed.tech/topics/backend.md>), [Python](<https://devfeed.tech/topics/python.md>), [Shell](<https://devfeed.tech/topics/shell.md>)

Tags: [cnn](<https://devfeed.tech/tags/cnn.md>), [code](<https://devfeed.tech/tags/code.md>), [computer-vision](<https://devfeed.tech/tags/computer-vision.md>), [convolutional-neural-networks](<https://devfeed.tech/tags/convolutional-neural-networks.md>), [cpu](<https://devfeed.tech/tags/cpu.md>), [cuda](<https://devfeed.tech/tags/cuda.md>), [cudnn](<https://devfeed.tech/tags/cudnn.md>), [cybersecurity](<https://devfeed.tech/tags/cybersecurity.md>), [deep-learning](<https://devfeed.tech/tags/deep-learning.md>), [deep-neural-networks](<https://devfeed.tech/tags/deep-neural-networks.md>), [dnn](<https://devfeed.tech/tags/dnn.md>), [ergo](<https://devfeed.tech/tags/ergo.md>), [how-to](<https://devfeed.tech/tags/how-to.md>), [image-classification](<https://devfeed.tech/tags/image-classification.md>), [keras](<https://devfeed.tech/tags/keras.md>), [machine-learning](<https://devfeed.tech/tags/machine-learning.md>), [neural-networks](<https://devfeed.tech/tags/neural-networks.md>), [nvidia](<https://devfeed.tech/tags/nvidia.md>), [open-source](<https://devfeed.tech/tags/open-source.md>), [planes](<https://devfeed.tech/tags/planes.md>), [planes-detector](<https://devfeed.tech/tags/planes-detector.md>), [planesnet](<https://devfeed.tech/tags/planesnet.md>), [project-release](<https://devfeed.tech/tags/project-release.md>), [python](<https://devfeed.tech/tags/python.md>), [shell](<https://devfeed.tech/tags/shell.md>), [tensorflow](<https://devfeed.tech/tags/tensorflow.md>), [tf](<https://devfeed.tech/tags/tf.md>), [train](<https://devfeed.tech/tags/train.md>), [training](<https://devfeed.tech/tags/training.md>), [tutorial](<https://devfeed.tech/tags/tutorial.md>)

### AI overview

The article introduces Project Ergo, an open-source framework and manager for Keras-based machine-learning projects. It demonstrates prototyping, training, and testing a convolutional neural network with the PlanesNet dataset to build an airplane detector for satellite imagery, including CPU and GPU setup considerations.

### Source excerpt

It's been a while that i've been quite intensively playing with Deep Learning both for work related research and personal projects. More specifically, I've been using the Keras framework on top of a TensorFlow backend for all sorts of stuff. From big and complex projects for malware detection, to smaller and simpler experiments about ideas i just wanted to quickly implement and test - it didn't really matter the scope of the project, I always found myself struggling with the same issues: code reuse over tens of crap python and shell scripts, datasets and models that are spread all over my dev and prod servers, no real standard for versioning them, no order, no structure. So a few days ago I started writing what it was initially meant to be just a simple wrapper for the main commands of my training pipelines but quickly became a full-fledged framework and manager for all my Keras based projects. Today I'm pleased to open source and present project Ergo by showcasing an example use-case: we'll prototype, train and test a Convolutional Neural Network on top of the PlanesNet raw dataset in order to build an airplane detector for satellite imagery.