* PIPNet's precision in facial landmark detection with a record 2.6% NME on the 300W benchmark. Explore the model and its capabilities by visiting our GitHub repository.
Watch the performance showcase on YouTube. Support and star our project on GitHub for future advancements.
video: https://www.youtube.com/watch?v=cxi1WQr-HKE
source code: https://github.com/Shohruh72/PIPNet/tree/main
join our community:
@MachineLearning_Programming
Watch the performance showcase on YouTube. Support and star our project on GitHub for future advancements.
video: https://www.youtube.com/watch?v=cxi1WQr-HKE
source code: https://github.com/Shohruh72/PIPNet/tree/main
join our community:
@MachineLearning_Programming
YouTube
"Facial Landmark Detection with PIPNet | Code Link Below"
Description:
Explore facial landmark detection with PIPNet in this concise video. Get hands-on with the code and see how PIPNet accurately detects facial features.
๐ Source Code: https://github.com/Shohruh72/PIPNet
Learn how to set up PIPNet, see live demonstrationsโฆ
Explore facial landmark detection with PIPNet in this concise video. Get hands-on with the code and see how PIPNet accurately detects facial features.
๐ Source Code: https://github.com/Shohruh72/PIPNet
Learn how to set up PIPNet, see live demonstrationsโฆ
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WHENet: Real-time Fine-Grained Estimation for Wide Range Head Pose
CODE: https://github.com/Ascend-Research/HeadPoseEstimation-WHENet
CODE: https://github.com/Ascend-Research/HeadPoseEstimation-WHENet
๐11๐ฅ1
This channels is for Programmers, Coders, Software Engineers.
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โก๏ธ 500 AI Machine learning Deep learning Computer vision NLP Projects with code
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join us: @MachineLearning_Programming
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InstantID : Zero-shot Identity-Preserving Generation in Seconds
Free Source Code: https://github.com/InstantID/InstantID.
Free Source Code: https://github.com/InstantID/InstantID.
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MLOps Masterclass
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Navigating the Landscape of MLOps & LLMOps - Understanding the Synergy
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๐ฅClosing registration soon!
Navigating the Landscape of MLOps & LLMOps - Understanding the Synergy
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Tracking Any Point (TAP)
Welcome to the official Google Deepmind repository for Tracking Any Point (TAP), home of the TAP-Vid Dataset, our top-performing TAPIR model, and our RoboTAP extension.
Source code: https://github.com/google-deepmind/tapnet
Google Colab: https://github.com/google-deepmind/tapnet
join us: @MachineLearning_Programming
Welcome to the official Google Deepmind repository for Tracking Any Point (TAP), home of the TAP-Vid Dataset, our top-performing TAPIR model, and our RoboTAP extension.
Source code: https://github.com/google-deepmind/tapnet
Google Colab: https://github.com/google-deepmind/tapnet
join us: @MachineLearning_Programming
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This channels is for Programmers, Coders, Software Engineers.
0- Python
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๐13
Forwarded from Artificial Intelligence && Deep Learning (Shohruh)
EfficientViT - SAM:69x Faster SAM: Multi-Scale Linear Attention for High-Resolution Dense Prediction
1. Channel: @deeplearning_ai
2.Source Code: https://github.com/mit-han-lab/efficientvit
3. Paper: https://arxiv.org/abs/2402.05008
1. Channel: @deeplearning_ai
2.Source Code: https://github.com/mit-han-lab/efficientvit
3. Paper: https://arxiv.org/abs/2402.05008
๐6
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๐ด๐ดDirect-a-Video: driving Video Generation๐ด๐ด
๐Direct-a-Video is a text-to-video generation framework that allows users to individually or jointly control the camera movement and/or object motion. Authors: City University of HK, Kuaishou Tech & Tianjin.
๐๐ข๐ ๐ก๐ฅ๐ข๐ ๐ก๐ญ๐ฌ:
โ Decoupling camera/object motion in gen-AI
โ Allowing users to independently/jointly control
โ Novel temporal cross-attention for cam motion
โ Training-free spatial cross-attention for objects
โ Driving object generation via bounding boxes
hashtag#artificialintelligence hashtag#machinelearning hashtag#ml hashtag#AI hashtag#deeplearning hashtag#computervision hashtag#AIwithPapers hashtag#metaverse
๐Channel: @MachineLearning_Programming
๐Paper https://arxiv.org/pdf/2402.03162.pdf
๐Project https://direct-a-video.github.io/
๐Direct-a-Video is a text-to-video generation framework that allows users to individually or jointly control the camera movement and/or object motion. Authors: City University of HK, Kuaishou Tech & Tianjin.
๐๐ข๐ ๐ก๐ฅ๐ข๐ ๐ก๐ญ๐ฌ:
โ Decoupling camera/object motion in gen-AI
โ Allowing users to independently/jointly control
โ Novel temporal cross-attention for cam motion
โ Training-free spatial cross-attention for objects
โ Driving object generation via bounding boxes
hashtag#artificialintelligence hashtag#machinelearning hashtag#ml hashtag#AI hashtag#deeplearning hashtag#computervision hashtag#AIwithPapers hashtag#metaverse
๐Channel: @MachineLearning_Programming
๐Paper https://arxiv.org/pdf/2402.03162.pdf
๐Project https://direct-a-video.github.io/
๐9โค1
Result.gif
23.1 MB
๐ Discover 6DRepNet: The Ultimate Head Pose Estimation Model!
Features:
* State-of-the-art accuracy
* Comprehensive tools for training, testing, and inference
* Easy setup with conda
* Supports multiple datasets
Watch the performance showcase on GitHub for future advancements.
[Source Code] [Paper]
join our community:
๐ @deeplearning_ai
Features:
* State-of-the-art accuracy
* Comprehensive tools for training, testing, and inference
* Easy setup with conda
* Supports multiple datasets
Watch the performance showcase on GitHub for future advancements.
[Source Code] [Paper]
join our community:
๐ @deeplearning_ai
๐6๐ฅ3โค1
๐ข FREE TRAINING:
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๐ฅ Join our FREE MLOps course demo and acquire essential skills for AI and data science across Multicloud ๐
๐ Reserve your seat now:
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๐ What you'll gain:
1๏ธโฃ ML model deployment techniques on AWS, Azure, GCP & open source.
2๏ธโฃ Efficient data management insights.
3๏ธโฃ Explore the latest MLOps tools.
4๏ธโฃ Real-time interaction with expert instructors.
๐ฉ Limited spots available! Don't miss out!
๐ Enroll now:
https://bit.ly/mlops-webinar
๐ฅ Share with fellow ML enthusiasts! ๐โจ
Navigating the Landscape of MLOps & LLMOps ๐
๐ฅ Join our FREE MLOps course demo and acquire essential skills for AI and data science across Multicloud ๐
๐ Reserve your seat now:
https://bit.ly/mlops-webinar
๐ What you'll gain:
1๏ธโฃ ML model deployment techniques on AWS, Azure, GCP & open source.
2๏ธโฃ Efficient data management insights.
3๏ธโฃ Explore the latest MLOps tools.
4๏ธโฃ Real-time interaction with expert instructors.
๐ฉ Limited spots available! Don't miss out!
๐ Enroll now:
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๐5โค2๐1
Forwarded from SHOHRUH
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Forwarded from Python | Machine Learning | Coding | R
This channels is for Programmers, Coders, Software Engineers.
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