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Amazing new year gifts for my subscribers
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🎁 Learn Generative AI
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Hope you like it 😀
InstantID : Zero-shot Identity-Preserving Generation in Seconds
Free Source Code: https://github.com/InstantID/InstantID.
Free Source Code: https://github.com/InstantID/InstantID.
MLOps Masterclass
🔥Closing registration soon!
Navigating the Landscape of MLOps & LLMOps - Understanding the Synergy
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🔥Closing registration soon!
Navigating the Landscape of MLOps & LLMOps - Understanding the Synergy
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Schedule:
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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
This channels is for Programmers, Coders, Software Engineers.
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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
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🌴🌴Direct-a-Video: driving Video Generation🌴🌴
👉Direct-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.
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
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👉Channel: @MachineLearning_Programming
👉Paper https://arxiv.org/pdf/2402.03162.pdf
👉Project https://direct-a-video.github.io/
👉Direct-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.
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Decoupling camera/object motion in gen-AI
✅Allowing users to independently/jointly control
✅Novel temporal cross-attention for cam motion
✅Training-free spatial cross-attention for objects
✅Driving object generation via bounding boxes
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👉Channel: @MachineLearning_Programming
👉Paper https://arxiv.org/pdf/2402.03162.pdf
👉Project https://direct-a-video.github.io/
Result.gif
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🌟 Discover 6DRepNet: The Ultimate Head Pose Estimation Model!
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Watch the performance showcase on GitHub for future advancements.
[Source Code] [Paper]
join our community:
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Features:
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* Supports multiple datasets
Watch the performance showcase on GitHub for future advancements.
[Source Code] [Paper]
join our community:
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Forwarded from SHOHRUH
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This channels is for Programmers, Coders, Software Engineers.
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AiOS: The Future of Human Shape & Pose Recovery
Discover AiOS, the cutting-edge, unified framework by SenseTime, HKU, IDEA, S-Lab, and Shanghai AI Lab. AiOS redefines state-of-the-art expressive pose and shape recovery, seamlessly integrating advanced features without the need for separate human detection steps.
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✅Comprehensive Analysis: Unified feature system for unparalleled whole-body understanding.
✅Unmatched Performance: Top-tier results sans ground truth bounding boxes.
Explore More:
Project Page
Read the Paper
@MachineLearning_Programming
Discover AiOS, the cutting-edge, unified framework by SenseTime, HKU, IDEA, S-Lab, and Shanghai AI Lab. AiOS redefines state-of-the-art expressive pose and shape recovery, seamlessly integrating advanced features without the need for separate human detection steps.
Highlights:
✅First-of-its-Kind: Single-stage EHPS with zero extra detection networks.
✅Innovative Design: Unique "Human-as-Tokens" concept for deeper insights.
✅Enhanced Dynamics: Sophisticated attention to human relationships.
✅Comprehensive Analysis: Unified feature system for unparalleled whole-body understanding.
✅Unmatched Performance: Top-tier results sans ground truth bounding boxes.
Explore More:
Project Page
Read the Paper
@MachineLearning_Programming
LeGrad: Layerwise Explainability GRADient method for large ViT transformer architectures
Explore More:
💻DEMO: you may use demo
📖Read the Paper: Access Here
💻Source Code: Explore on GitHub
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join our community:
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Explore More:
💻DEMO: you may use demo
📖Read the Paper: Access Here
💻Source Code: Explore on GitHub
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join our community:
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🚀 Explore AI News with Us! 🤖
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This Event is especially designed for people interested in the field of AI, ML, GenAI & LLMs.
Learn to deploy Gen Ai Models to production 👇
MLOps Masterclass
Productionizing Generative AI models, Navigating the Landscape of MLOps & LLMOps - Understanding the Synergy
Schedule:
May 25th (Sat) & 26th (Sun), 10AM to 3:30 PM
Register Now👇
https://bit.ly/mlops-masterclass
🔥 Limited Seats Available!
☎️ Contact:
Sarath Kumar
+918940876397 / +918778033930
MLOps Masterclass
Productionizing Generative AI models, Navigating the Landscape of MLOps & LLMOps - Understanding the Synergy
Schedule:
May 25th (Sat) & 26th (Sun), 10AM to 3:30 PM
Register Now👇
https://bit.ly/mlops-masterclass
🔥 Limited Seats Available!
☎️ Contact:
Sarath Kumar
+918940876397 / +918778033930
Result.gif
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🚀 Discover LiteHPE: Advanced Head Pose Estimation 🚀
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🛠️ Setup in Minutes:
📈 Top-Tier Performance:
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🔗 Join now: @MachineLearning_Programming
Features:
🛠️ Setup in Minutes:
📈 Top-Tier Performance:
✅ Achieve low Mean Absolute Error rates
✅ Models range from MobileOne_s0 to s4
✅ Pretrained models ready for download
🌟 🌟 Star us on GitHub for the latest updates: LiteHPE on GitHub.
Boost your project's capabilities with LiteHPE – the forefront of head pose estimation technology!
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