TagMaps
UNINEXT
| TagMaps | UNINEXT | |
|---|---|---|
| 1 | 2 | |
| 6 | 1,447 | |
| - | - | |
| 8.6 | 5.5 | |
| 3 months ago | 10 months ago | |
| Python | Python | |
| GNU General Public License v3.0 only | MIT License |
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.
TagMaps
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Stop lying to yourself – you “fix it later”
I think this is perfectly natural - some ideas never manifest or are convincing enough to publish, or sometimes you write code and it turns out not to be used - why produce production ready code in this case. I have a python package that I slowly developed over 5 years, step by step [1]. Everytime I use it, I find many things that I could develop, some I do right then, others I leave for later. I also have a blog [2] - you can see three dates for each blog post:
- the time I first started working on it
- the first time I published it
- the last time it was updated
All of these dates are important. Think of doing things more like a process of chained events, not like a one-stop thing.
[1]: https://github.com/Sieboldianus/TagMaps
[2]: https://du.nkel.dev/
UNINEXT
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[R] Universal Instance Perception as Object Discovery and Retrieval (Video Demo)
Hi, we have uploaded the complete paper (https://github.com/MasterBin-IIAU/UNINEXT/blob/master/assets/UNINEXT_Paper.pdf) to our repo. You can find more details in the paper :) About the first question, the input videos are NOT segmented aforehand and all target masks are predicted by our UNINEXT model. For SOT and VOS, we use target annotations (box or mask) from the first frame as the prompts. This helps UNINEXT to segment corresponding targets in the following frames.
What are some alternatives?
catgrasp - [ICRA 2022] CaTGrasp: Learning Category-Level Task-Relevant Grasping in Clutter from Simulation
VolleyVision - Applying Deep Learning Approaches to Volleyball Data
Robotics-Object-Pose-Estimation - A complete end-to-end demonstration in which we collect training data in Unity and use that data to train a deep neural network to predict the pose of a cube. This model is then deployed in a simulated robotic pick-and-place task.
ailia-models - The collection of pre-trained, state-of-the-art AI models for ailia SDK
TheWatcher - Find a specific username - (Instagram, TikTok, Snapchat, Spotify...)
py-motmetrics - :bar_chart: Benchmark multiple object trackers (MOT) in Python
interbotix_ros_toolboxes - Support-level ROS Packages for Interbotix Robots
norfair - Lightweight Python library for adding real-time multi-object tracking to any detector.
ludwig - Low-code framework for building custom LLMs, neural networks, and other AI models
unimatch - [TPAMI'23] Unifying Flow, Stereo and Depth Estimation
eSCAPE - Earth Landscape Evolution Model: https://escape-model.github.io/
VNext - Next-generation Video instance recognition framework on top of Detectron2 which supports InstMove (CVPR 2023), SeqFormer(ECCV Oral), and IDOL(ECCV Oral))