M2I VS UniAD

Compare M2I vs UniAD and see what are their differences.

M2I

M2I is a simple but effective joint motion prediction framework through marginal and conditional predictions by exploiting the factorized relations between interacting agents. (by Tsinghua-MARS-Lab)
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M2I UniAD
1 1
175 2,879
0.0% 5.8%
0.0 7.1
over 1 year ago about 2 months ago
Python Python
MIT License Apache License 2.0
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

M2I

Posts with mentions or reviews of M2I. We have used some of these posts to build our list of alternatives and similar projects.

UniAD

Posts with mentions or reviews of UniAD. We have used some of these posts to build our list of alternatives and similar projects.

What are some alternatives?

When comparing M2I and UniAD you can also consider the following projects:

waymo-motion-prediction-challenge-2022-multipath-plus-plus - Solution for Waymo Motion Prediction Challenge 2022. Our implementation of MultiPath++

PaddleDetection - Object Detection toolkit based on PaddlePaddle. It supports object detection, instance segmentation, multiple object tracking and real-time multi-person keypoint detection.

ByteTrack - [ECCV 2022] ByteTrack: Multi-Object Tracking by Associating Every Detection Box