RAMP on Insects and Deep Learning at La Paillasse (May 29) and Futur en Seine

The Paris Saclay Center for Data Science is organizing a RAMP on image classification and deep learning. We will classify images of pollinating insects from the SPIPOLL crowdsourcing project of the Paris Museum of Natural History (MNHN).

The kick-off event will be on May 29 at La Paillasse.

Please register here:  https://indico.lal.in2p3.fr/event/3508/

The RAMP will run in competitive mode (scores available on the leaderboard but not the submission codes) until 20h June 7. We will switch to the open collaborative mode starting June 8 at the event organized by Proto204 in the Protobus at Futur en Seine. The prizes of the top three contestants of the the closed phase (three pots of honey, brought to you by pollinating insects) will be awarded at the June 8 event.

The kick-off event will start with a half-day tutorial on deep learning and convolutional neural nets, followed by an introduction to the particular problem to solve, the RAMP platform, and the starting kit. The tutorial is intended to an audience with basic notions of the pydata ecosystem and machine learning. The tutorial will be based on the courses of Andrey Karpathy and Olivier Grisel/Charles Ollion.

La Paillasse has a limit of 40 participantsTo enter to the kick-off/tutorial of May 29, we will ask you to sign up at the RAMP platformsign up to the Titanic test RAMPand make a submission. The selection among those who clear this step will be based on first-come-first-serve, but we maintain the right to change the criteria. If there is enough interest, we can add a second day later in the week.

Note that participation in the pollinating insects RAMP is open to everyone: it is not conditioned on participating in the kickoff event. The starting kit is entirely self-explanatory.

We have not yet set up the pollinating insects event at the RAMP site, but those who would like to start preparing can download the starting kit for our previous insect classification RAMP (which had less images and classes). The starting kit contains a Jupyter notebook which you can open on your laptop. On the other hand, to execute the starting kit, you will need to have access to a GPU-equipped machine. The simplest way is to set up an account at AWS following this tutorial. We have prepared an AMI called “pollinating_insects_users” on the Frankfurt and Oregon nodes which contains the data and the starting kit preinstalled.

You can read more about RAMPs here.

 

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