deeplab2
Unicorn
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deeplab2 | Unicorn | |
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5 | 7 | |
982 | 942 | |
1.0% | - | |
4.0 | 0.0 | |
about 1 year ago | over 1 year ago | |
Python | Python | |
Apache License 2.0 | 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.
deeplab2
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Meet MOAT: An Artificial Intelligence (AI) Model that Combines Convolution and Attention Operations to Achieve Powerful Vision Models
Quick Read: https://www.marktechpost.com/2022/12/30/meet-moat-an-artificial-intelligence-ai-model-that-combines-convolution-and-attention-operations-to-achieve-powerful-vision-models/ Paper: https://arxiv.org/pdf/2210.01820.pdf Github: https://github.com/google-research/deeplab2
- [D] Most Popular AI Research July 2022 pt. 2 - Ranked Based On GitHub Stars
- Most Popular AI Research July 2022 pt. 2 - Ranked Based On GitHub Stars
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[P] DeepLab2: A TensorFlow Library for Deep Labeling web demo
github: https://github.com/google-research/deeplab2
- DeepLab2 – New deep labeling library for TensorFlow
Unicorn
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need help with object detection and object tracking using yolov4
Also check out Unicorn - https://github.com/MasterBin-IIAU/Unicorn
- [D] Most Popular AI Research July 2022 pt. 2 - Ranked Based On GitHub Stars
- Most Popular AI Research July 2022 pt. 2 - Ranked Based On GitHub Stars
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Researchers from Bytedance and Dalian University Propose 🦄 ‘Unicorn’: a Unified Computer Vision Approach to Address Four Tracking Tasks Using a Single Model with the Same Model Parameters
Continue reading | Checkout the paper and github link
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[R] Unicorn: 🦄 : Towards Grand Unification of Object Tracking(Video Demo)
Brief Overview We present a unified method, termed Unicorn, that can simultaneously solve four tracking problems (SOT, MOT, VOS, MOTS) with a single network using the same model parameters. For the first time, we accomplished the great unification of the tracking network architecture and learning paradigm. Unicorn performs on-par or better than its task-specific counterparts in 8 tracking datasets, including LaSOT, TrackingNet, MOT17, BDD100K, DAVIS16-17, MOTS20, and BDD100K MOTS. Our work is accepted to ECCV 2022 as an oral presentation ! Paper: https://arxiv.org/abs/2207.07078 Code: https://github.com/MasterBin-IIAU/Unicorn
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[R] Unicorn: 🦄 : Towards Grand Unification of Object Tracking
Code for https://arxiv.org/abs/2207.07078 found: https://github.com/MasterBin-IIAU/Unicorn
What are some alternatives?
yolov7 - Implementation of paper - YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors
XMem - [ECCV 2022] XMem: Long-Term Video Object Segmentation with an Atkinson-Shiffrin Memory Model
theseus - A library for differentiable nonlinear optimization
multiface - Hosts the Multiface dataset, which is a multi-view dataset of multiple identities performing a sequence of facial expressions.
latent-diffusion - High-Resolution Image Synthesis with Latent Diffusion Models
NUWA - A unified 3D Transformer Pipeline for visual synthesis
Cream - This is a collection of our NAS and Vision Transformer work. [Moved to: https://github.com/microsoft/AutoML]
hivemind - Decentralized deep learning in PyTorch. Built to train models on thousands of volunteers across the world.