🏆 Leaderboard

Date Organization Model Site Score Video
YYYY-MM-DD Example Lab Example Model 🔗 000 📽️
YYYY-MM-DD Example Lab Example Model 🔗 000 📽️
YYYY-MM-DD Example Lab Example Model 🔗 000 📽️
YYYY-MM-DD Example Lab Example Model 🔗 000 📽️

Challenge Overview

The current goal of automated proofreading is a difficult challenge involving many distinct, intermediate skills. In the interest of facilitating fully autonomous proofreading, we have prepared a series of intermediate challenges that incorporate essential skills for proofreading.

For each sub-challenge, training can be performed on the publicly available data hosted on Flywire. Submissions are currently being evaluated manually; we hope to develop an automated framework for leaderboard submission soon. Please reach out to raphael_levisse@berkeley.edu, kp0374@princeton.edu and dl2635@princeton.edu for additional information.

Submissions will be evaluated on private data not made available to the public.

Sub-Challenge 1: Navigation

Sub-Challenge 1 is a navigational task: to achieve the highest z-position for a given neuron. Your agent, when initialized at an arbitrary XYZ-position on a random neuron, should be able to navigate (via clicking, keyboard interactions, etc.) to the top z-position of the neuron.

Evaluation Criteria

  • Rotational invariance
    • Can the highest z-position be obtained regardless of initial angular orientation of the z-axis?
  • Depth understanding
    • Can your agent successfully identify the highest z-position in visually ambiguous situations?
  • Speed
    • How efficient is your agent at navigating?

Demonstrations

An example of reaching highest z-position.