Snakebite causes over 100,000 human deaths and 400,000 victims of
disability and disfigurement globally every year. It affects poor and
rural communities in developing countries, which host the highest
venomous snake diversity and the highest burden of snakebite due to limited medical expertise and access to antivenoms
Antivenoms can be life‐saving when correctly administered but this
depends first on the correct taxonomic identification (i.e. family,
genus, species) of the biting snake. Snake identification is challenging
due to:
their high diversity
the incomplete or misleading information provided by snakebite victims
the lack of knowledge or resources in herpetology that healthcare professionals have
In this challenge we want to explore how Machine Learning can help
with snake identification, in order to potentially reduce erroneous and
delayed healthcare actions.
Higher School of Economics and Yandex are proud to announce the 3rd international data analysts olympiad.
The event is open to all teams and individuals, be they
undergraduate, postgraduate or PhD students, company employees,
researchers or new data scientists, .
The event aims to bridge the gap between the all-increasing
complexity of Machine Learning models and performance bottlenecks of the
industry. The participants will strive not only to maximize the quality
of their predictions, but also to devise resource-efficient algorithms.
This will be a team machine learning competition, divided into two
stages. The first stage will be online, open to all participants. The
second stage will be the offline on-site finals, in which the top 30 performing teams from the online round will compete at the Yandex office in Moscow.
Leverage millions of images of animals on the Serengeti to
build a classifier that distinguishes between gazelles, lions, and more!
In this competition, participants will predict the presence and species of wildlife in new camera trap data from the Snapshot Serengeti project, which boasts over 6 million images.
Camera traps
are motion-triggered systems for passively collecting animal behavior
data with minimal disturbance to their natural tendencies. Camera traps
are an invaluable tool in conservation research, but the sheer amount of
data they generate presents a huge barrier to using them effectively.
This is where AI can help!
There are two immediate challenges where efforts like this competition are needed:
Camera traps can’t automatically label the animals they observe, creating an immense (and sometimes prohibitive) burden on humans to determine where and what wildlife are present.
Even when automated animal tagging models are available, the models that do exist don’t generalize well across time and locations, severely limiting their usefulness with new data.
To address these opportunities, we’re challenging data scientists,
researchers, and developers from around the world to build the best
algorithms for wildlife detection.
The competition is designed with a few objectives in mind:
Innovation: Participants use state-of-the-art approaches in
computer vision and AI and get live feedback on how well their
solutions perform
Generalization: This competition is designed to reward the
best generalizable solutions. The private test data used to determine
the winners will come entirely from the latest, unreleased season of
data from the Snapshot Serengeti project (season 11). For more
information on the competition timeline and evaluation, see the problem description.
Execution: Models are trained locally and submitted to execute inference in the cloud – read on!
Openness: All prize-winning models are released under an open source license for anyone to use and learn from
This is a brand new kind of DrivenData challenge! Previous
models trained for camera trap images have often failed to generalize
well. In this competition, we want to reward the models that generalize
best to new images, so you won’t interact directly with the test set.
Rather than submitting your predicted labels for a test set you have,
you’ll package up everything needed to do inference and send it to us.
We’ll execute that code on Azure in a Docker container that has access
to the test set images. By leveraging Microsoft Azure’s cloud computing
platform and Docker containers, we’re moving our competition
infrastructure one step closer to translating participants’ innovation
into impact.
The objectives of the National Data Science Challenge (NDSC) are to:
Bring the tech community closer through working together and knowledge sharing
Provide an environment for the development of creative new ideas in Data Science
Equip students and professionals with essential technical skills and expertise to prepare them for Industry 4.0
Through this competition, we hope to showcase the ubiquity and usefulness of data in getting insights, thus reinforcing Indonesia’s emphasis on driving the digital economy and the use of big data.
The dataset contains about 100,000 satellite images of Northern Kenya in 10 frequency bands, collected by the International Livestock Research Institute. Local experts (pastoralists, or nomadic herders) manually labeled the forage quality at the corresponding geolocations—specifically, the number of cows from {0, 1, 2, 3+} that the location at the center of the satellite image can feed. Each satellite image is 1.95km across, and each pixel in it represents a 30 meter square. Pastoralists estimate the forage quality within 20 meters when they stand on location, an area slightly larger than a pixel in the full 65×65-pixel satellite image. The satellite images thus provide a lot of extra context, which may prove useful since forage quality is correlated across space. The challenge is to learn a mapping from a satellite image to forage quality so we can more accurately predict drought conditions. Furthermore, the current labeling is very sparse, and we want dense predictions of forage quality at any pixel in a satellite image, not just at the center.
# there may be questions from installer. Refer to Howto Install Redmine on Ubuntu www.redmine.org/projects/redmine/wiki/howto_install_redmine_on_ubuntu_step_by_step for explanation
# Next step is to upgrade all gems. It will take sometime to update all gems.
gem update
# possible error: connection problem.
# example erro message: Updating roadie
ERROR: Error installing roadie:
Unable to resolve dependency: user requested ‘roadie (= 3.5.0)’
