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Machine Learning for detection of early onset of Alzheimers
Latest submissions
Robustness and teamwork in a massively multiagent environment
Latest submissions
See Allfailed | 166813 | ||
graded | 166810 | ||
graded | 166809 |
Play in a realistic insurance market, compete for profit!
Latest submissions
5 Puzzles 21 Days. Can you solve it all?
Latest submissions
Multi-Agent Reinforcement Learning on Trains
Latest submissions
Predicting wine quality
Latest submissions
Reinforcement Learning, IIT-M, assignment 1
Latest submissions
Latest submissions
Classifying Emotion from Texts
Latest submissions
Multi-Agent Reinforcement Learning on Trains
Latest submissions
Latest submissions
See Allgraded | 158440 | ||
graded | 158436 | ||
failed | 158297 |
Latest submissions
See Allgraded | 158466 | ||
submitted | 158456 | ||
submitted | 158451 |
Latest submissions
See Allsubmitted | 158597 | ||
graded | 158560 | ||
graded | 158558 |
Latest submissions
See Allsubmitted | 158597 | ||
graded | 158560 | ||
graded | 158558 |
Participant | Rating |
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Participant | Rating |
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RL Project 2021-56807e
Normalisation and Final Score computation for the RL Project
Almost 3 years agoEach of the kbc assignments will be normalised to 1.
Normalisation and Final Score computation for the RL Project
Almost 3 years agoHi,
The final scores will be computed by adding the scores of each of the 5 environments after normalising these between 0-1.
For the Acrobot and the Taxi environment, min-max normalisation will be used.
For the KBC problems, min-max normalisation after applying a log operator will be used.
The scores will be computed after the submission period has ended.
Thanks,
Siddhartha.
Running Evaluations locally
Almost 3 years agoHi,
In case you are running out of submission limit, please note that the evaluations can be run locally end to end and get an estimate of the scores before making a submission on the leaderboard.
Thanks
Siddhartha.
IITM RL Final Project-b5d2e6
Running local evaluations
Almost 3 years agoHi,
In case you are running out of submission limit, please note that the evaluations can be run locally end to end and get an estimate of the scores before making a submission on the leaderboard.
Thanks
Siddhartha.
Airborne Object Tracking Challenge
New notebook for training with YOLO
About 3 years agoWe have added a new notebook that walks you through preparing the dataset and configs for training with YOLO models. The notebook also provides an easy interface to apply filters and downloading specific data points for training.
You can find the notebook here:
Ideas for getting started
About 3 years agoHere is a list of ideas for getting started and improving baselines on the Airborne Object Detection challenge -
Using YOLO models for object detection and tracking
- Out of the box YOLOv3 model with DeepSort algorithm for tracking
- YOLO model fine tuned on the Airprime dataset
Detectron2 provides numerous models for object detection and segmentation and a flexible library for adding new ones. This can be a good starting point for building detection models.
Using JDE based algorithms for object tracking
Most algorithms explored are in SDE (Separate detection and embedding) paradigm. Recent algorithms in the Joint Detection and Embedding (JDE) paradigm have achieved superior performance in MOT leaderboards. Few notable examples -
- FAIR MOT
- JDE
- GSDT - JDE+GNN based detection, using graphs to connect objects across temporal and spatial dimensions.
PaddlePaddle provides a framework with a few MultiObject tracking methods (deepsort, fairmot, jde), and a flexible interface for adding new ones.
A few suggestions for improving object detection
- Downscaling the input resolution to the model might make it hard to detect smaller airborne objects. Scaling up your input resolution to the model could help.
- Using a model with high resolution would make your model large and increase inference time. Alternatively, you could tile your images as a preprocessing step and continue using smaller models. (The Power of Tiling for Small Object Detection)
- Removing birds from predictions can help avoid extra false positives, since we are not interested in alerting birds.
NeurIPS 2022 - The Neural MMO Challenge
About the The Neural-MMO Challenge
Over 3 years agoThis forum is for long-form discussions on Neural MMO that are not suited to a single-thread format. Please direct all other discussion and support requests to the Discord server.
Notebooks
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Starter Notebook Use this notebook to get started and make a submissionsiddharthaΒ· About 3 years ago
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Starter Notebook Use this notebook to get started and make a submissionsiddharthaΒ· About 3 years ago
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Starter notebook Use this notebook to run your experiments and make a submissionsiddharthaΒ· About 3 years ago
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Sample interface for training with DarkNet YOLO This notebook helps you navigate dataset easier and sets up training with YOLO modelssiddharthaΒ· About 3 years ago
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Getting Started This notebook walks you through installing dependencies and helps you make your first submissionsiddharthaΒ· About 3 years ago
Normalisation and Final Score computation for the RL Project
Almost 3 years agoHi, base of the log operator will be 2.