These four marathon matches are all part of our Rodeo II Hind Cast Challenge: training weather predictions models on historical data. Each challenge will have its own leaderboard, but they are all connected by a single chat forum. You can create a single solution for each challenge, or create apply the same solution to multiple - it’s all up to you!
In this competition, you’ll develop a model to classify (and if present, segment) pneumothorax from a set of chest radiographic images. If successful, you could aid in the early recognition of pneumothoraces and save lives.
In this competition, you will develop an algorithm that can predict the magnetic interaction between two atoms in a molecule (i.e., the scalar coupling constant).
Once the competition finishes, CHAMPS would like to invite the top teams to present their work, discuss the details of their models, and work with them to write a joint research publication which discusses an open-source implementation of the solution.
WNS Analytics Wizard, a one-of-its-kind online analytics hackathon, is back with a second edition. Here’s an exciting opportunity for young and aspiring analytics professionals to experience challenging, real-life business scenarios and showcase your analytical acumen and problem-solving skills.
The objective of this competition is to create a machine learning model to classify fields by crop type using Sentinel-2 satellite imagery. The fields in this training set are along the Orange River, a major agricultural region in South Africa that has been stricken by drought in recent years.
In this synchronous Kernels-only competition, you'll build a machine learning model to speed up disease detection. You’ll work with thousands of images collected in rural areas to help identify diabetic retinopathy automatically. If successful, you will not only help to prevent lifelong blindness, but these models may be used to detect other sorts of diseases in the future, like glaucoma and macular degeneration.
The NL2SQL Challenge, using financial and general domain tabular data as the data source, provides pairs of natural language question and SQL statement annotated based on the tabular data. Contestants can use the annotated data to develop methods that can accurately convert natural language question to corresponding SQL.
The goal of the Master Challenge is to derive specification of the HF sky-wave environment (the bottom-side ionosphere) across a longer circuit. This will be accomplished through passive reception of active sounders from an Oblique Incidence (OI). This process will require modification of the solver algorithms developed in the Explorer Challenge to determine ionospheric characteristics derived from more difficult OI datasets.
The objective of this competition is to create a machine learning model to detect fraudulent transactions.
Fraud detection is an important application of machine learning in the financial services sector. This solution will help Xente provide improved and safer service to its customers.
In previous years, participants worked on advancements in video-level annotations, building both unconstrained and constrained models. In this third challenge based on the YouTube 8M dataset, Kagglers will localize video-level labels to the precise time in the video where the label actually appears, and do this at an unprecedented scale. To put it another way: at what point in the video does the cat sneeze?
This competition will have you disentangling experimental noise from real biological signals. Your entry will classify images of cells under one of 1,108 different genetic perturbations. You can help eliminate the noise introduced by technical execution and environmental variation between experiments.
The objective of the new ChaLearn AutoDL challenge series, organized with Google and 4Paradigm, is to address some of the limitations of the previous challenges and provide an ambitious benchmark multi-class classification problems without any human intervention, in limited time, on any large-scale dataset composed of samples either in tabular format, 2d matrices, time series, or spatio-temporal series. Data are formatted in a uniform way as 4d tensors (t, x, y, channel). This lends itself in particular to the use of convolutional neural networks (CNNs). Although the use of TensorFlow is facilitated by providing participants with a starting kit including sample code demonstrating how to solve the problems at hand with TensorFlow, the participants are free to provide solutions not using TensorFlow.
In this track of the Challenge, you are asked to detect pairs of objects and the relationships that connect them.
The training set contains 329 relationship triplets with 375k training samples. These include both human-object relationships (e.g. "woman playing guitar", "man holding microphone"), object-object relationships (e.g. "beer on table", "dog inside car"), and also considers object-attribute relationships (e.g."handbag is made of leather" and "bench is wooden").
In this competition, you’ll benchmark machine learning models on a challenging large-scale dataset. The data comes from Vesta's real-world e-commerce transactions and contains a wide range of features from device type to product features. You also have the opportunity to create new features to improve your results.
In this track of the Challenge, you are asked to provide segmentation masks of objects.
This track’s training set represents 2.1M segmentation masks for object instances in 300 categories; with a validation set containing an additional 23k masks. The train set masks were produced by our state-of-the-art interactive segmentation process, where professional human annotators iteratively correct the output of a segmentation neural network. The validation and test set masks have been annotated manually with a strong focus on quality.
In this challenge, the task is to detect truly suspicious events and false alarms within the set of so-called network traffic alerts, that the Security Operations Center (SOC) Team members @ SOD have to analyze on an everyday basis. An efficient classification model could help the SOC Team to optimize their operations significantly. It is worth adding that although the competition sponsor is entirely commercial, the knowledge and experience that can be gathered by the competition participants may be highly beneficial to improve the intelligent cybersecurity modules in many organizations.
The hosts need help from machine learning experts to transcribe Kuzushiji into contemporary Japanese characters. With your help, Center for Open Data in the Humanities (CODH) will be able to develop better algorithms for Kuzushiji recognition. The model is not only a great contribution to the machine learning community, but also a great help for making millions of documents more accessible and leading to new discoveries in Japanese history and culture.
In this track of the Challenge, you are asked to predict a tight bounding box around object instances.
The training set contains 12.2M bounding-boxes across 500 categories on 1.7M images. The boxes have been largely manually drawn by professional annotators to ensure accuracy and consistency. The images are very diverse and often contain complex scenes with several objects (7 per image on average).