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siddhartha

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Siddhartha Laghuvarapu

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IN

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Challenges Entered

Machine Learning for detection of early onset of Alzheimers

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Airborne Object Tracking Challenge

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failed 150529
failed 150526

Robustness and teamwork in a massively multiagent environment

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failed 158663
failed 151796
failed 149853

Play in a realistic insurance market, compete for profit!

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5 Puzzles 21 Days. Can you solve it all?

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Multi-Agent Reinforcement Learning on Trains

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Predicting wine quality

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Reinforcement Learning, IIT-M, assignment 1

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Classifying Emotion from Texts

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Multi-Agent Reinforcement Learning on Trains

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graded 158440
graded 158436
failed 158297

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graded 158466
submitted 158456
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submitted 158597
graded 158560
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submitted 158597
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Trustable
May 16, 2020

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  • May 16, 2020

  • May 16, 2020
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    May 16, 2020
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Airborne Object Tracking Challenge

New notebook for training with YOLO

2 months ago

We 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

3 months ago

Here 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 -

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.

The Neural-MMO Challenge

About the The Neural-MMO Challenge

6 months ago

This 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.

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