Forwarded from Python | Machine Learning | Coding | R
This channels is for Programmers, Coders, Software Engineers.
0️⃣ Python
1️⃣ Data Science
2️⃣ Machine Learning
3️⃣ Data Visualization
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5️⃣ Data Analysis
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🚀 The new HQ-SAM (High-Quality Segment Anything Model) has just been added to the Hugging Face Transformers library!
This is an enhanced version of the original SAM (Segment Anything Model) introduced by Meta in 2023. HQ-SAM significantly improves the segmentation of fine and detailed objects, while preserving all the powerful features of SAM — including prompt-based interaction, fast inference, and strong zero-shot performance. That means you can easily switch to HQ-SAM wherever you used SAM!
The improvements come from just a few additional learnable parameters. The authors collected a high-quality dataset with 44,000 fine-grained masks from various sources, and impressively trained the model in just 4 hours using 8 GPUs — all while keeping the core SAM weights frozen.
The newly introduced parameters include:
* A High-Quality Token
* A Global-Local Feature Fusion mechanism
This work was presented at NeurIPS 2023 and still holds state-of-the-art performance in zero-shot segmentation on the SGinW benchmark.
📄 Documentation: https://lnkd.in/e5iDT6Tf
🧠 Model Access: https://lnkd.in/ehS6ZUyv
💻 Source Code: https://lnkd.in/eg5qiKC2
#ArtificialIntelligence #ComputerVision #Transformers #Segmentation #DeepLearning #PretrainedModels #ResearchAndDevelopment #AdvancedModels #ImageAnalysis #HQ_SAM #SegmentAnything #SAMmodel #ZeroShotSegmentation #NeurIPS2023 #AIresearch #FoundationModels #OpenSourceAI #SOTA
🌟https://t.iss.one/DataScienceN
This is an enhanced version of the original SAM (Segment Anything Model) introduced by Meta in 2023. HQ-SAM significantly improves the segmentation of fine and detailed objects, while preserving all the powerful features of SAM — including prompt-based interaction, fast inference, and strong zero-shot performance. That means you can easily switch to HQ-SAM wherever you used SAM!
The improvements come from just a few additional learnable parameters. The authors collected a high-quality dataset with 44,000 fine-grained masks from various sources, and impressively trained the model in just 4 hours using 8 GPUs — all while keeping the core SAM weights frozen.
The newly introduced parameters include:
* A High-Quality Token
* A Global-Local Feature Fusion mechanism
This work was presented at NeurIPS 2023 and still holds state-of-the-art performance in zero-shot segmentation on the SGinW benchmark.
📄 Documentation: https://lnkd.in/e5iDT6Tf
🧠 Model Access: https://lnkd.in/ehS6ZUyv
💻 Source Code: https://lnkd.in/eg5qiKC2
#ArtificialIntelligence #ComputerVision #Transformers #Segmentation #DeepLearning #PretrainedModels #ResearchAndDevelopment #AdvancedModels #ImageAnalysis #HQ_SAM #SegmentAnything #SAMmodel #ZeroShotSegmentation #NeurIPS2023 #AIresearch #FoundationModels #OpenSourceAI #SOTA
🌟https://t.iss.one/DataScienceN
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Forwarded from Python | Machine Learning | Coding | R
🎁 Your balance is credited $4,000 , the owner of the channel wants to contact you!
Dear subscriber, we would like to thank you very much for supporting our channel, and as a token of our gratitude we would like to provide you with free access to Lisa's investor channel, with the help of which you can earn today
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Dear subscriber, we would like to thank you very much for supporting our channel, and as a token of our gratitude we would like to provide you with free access to Lisa's investor channel, with the help of which you can earn today
t.iss.one/Lisainvestor
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Forwarded from Python | Machine Learning | Coding | R
This channels is for Programmers, Coders, Software Engineers.
0️⃣ Python
1️⃣ Data Science
2️⃣ Machine Learning
3️⃣ Data Visualization
4️⃣ Artificial Intelligence
5️⃣ Data Analysis
6️⃣ Statistics
7️⃣ Deep Learning
8️⃣ programming Languages
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🔥Powerful Combo: Ultralytics YOLO11 + Sony Semicon | AITRIOS (Global) Platform + Raspberry Pi
We’ve recently updated our Sony IMX model export to fully support YOLO11n detection models! This means you can now seamlessly run YOLO11n models directly on Raspberry Pi AI Cameras powered by the Sony IMX500 sensor — making it even easier to develop advanced Edge AI applications. 💡
To test this new export workflow, I trained a model on the VisDrone dataset and exported it using the following command:
👉
🔍Benchmark results for YOLO11n on IMX500:✅ Inference Time: 62.50 ms✅ mAP50-95 (B): 0.644📌 Want to learn more about YOLO11 and Sony IMX500? Check it out here ➡️
https://docs.ultralytics.com/integrations/sony-imx500/
#EdgeAI#YOLO11#SonyIMX500#AITRIOS#ObjectDetection#RaspberryPiAI#ComputerVision#DeepLearning#OnDeviceAI#ModelDeployment
🌟https://t.iss.one/DataScienceN
We’ve recently updated our Sony IMX model export to fully support YOLO11n detection models! This means you can now seamlessly run YOLO11n models directly on Raspberry Pi AI Cameras powered by the Sony IMX500 sensor — making it even easier to develop advanced Edge AI applications. 💡
To test this new export workflow, I trained a model on the VisDrone dataset and exported it using the following command:
👉
yolo export model=<path_to_drone_model> format=imx data=VisDrone.yaml🎥 The video below shows the result of this process!
🔍Benchmark results for YOLO11n on IMX500:✅ Inference Time: 62.50 ms✅ mAP50-95 (B): 0.644📌 Want to learn more about YOLO11 and Sony IMX500? Check it out here ➡️
https://docs.ultralytics.com/integrations/sony-imx500/
#EdgeAI#YOLO11#SonyIMX500#AITRIOS#ObjectDetection#RaspberryPiAI#ComputerVision#DeepLearning#OnDeviceAI#ModelDeployment
🌟https://t.iss.one/DataScienceN
Ultralytics
SONY IMX500
Learn to export Ultralytics YOLO11 models to Sony's IMX500 format for efficient edge AI deployment on Raspberry Pi AI Camera with on-chip processing.
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NVIDIA introduces GENMO, a unified generalist model for human motion that seamlessly combines motion estimation and generation within a single framework. GENMO supports conditioning on videos, 2D keypoints, text, music, and 3D keyframes, enabling highly versatile motion understanding and synthesis.
Currently, no official code release is available.
Review:
https://t.ly/Q5T_Y
Paper:
https://lnkd.in/ds36BY49
Project Page:
https://lnkd.in/dAYHhuFU
#NVIDIA #GENMO #HumanMotion #DeepLearning #AI #ComputerVision #MotionGeneration #MachineLearning #MultimodalAI #3DReconstruction
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It was a challenge - a marathon 300$ to 30.000$ on trading, together with Lisa!
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python_basics.pdf
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🚀 Master Python with Ease!
I've just compiled a set of clean and powerful Python Cheat Sheets to help beginners and intermediates speed up their coding workflow.
Whether you're brushing up on the basics or diving into data science, these sheets will save you time and boost your productivity.
📌 Topics Covered:
Python Basics
Jupyter Notebook Tips
Importing Libraries
NumPy Essentials
Pandas Overview
Perfect for students, developers, and anyone looking to keep essential Python knowledge at their fingertips.
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I've just compiled a set of clean and powerful Python Cheat Sheets to help beginners and intermediates speed up their coding workflow.
Whether you're brushing up on the basics or diving into data science, these sheets will save you time and boost your productivity.
📌 Topics Covered:
Python Basics
Jupyter Notebook Tips
Importing Libraries
NumPy Essentials
Pandas Overview
Perfect for students, developers, and anyone looking to keep essential Python knowledge at their fingertips.
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Some projects don’t scream for attention — they quietly change the way we learn🧑🎓 .
Python Data Science Handbook is one of them📖 .
🧑🎓 What is it?
🎚️ Run everything instantly via Binder or Google Colab — no setup needed⚙ .
🖥 GitHub
.
Python Data Science Handbook is one of them
A powerful open-source GitHub repository by Jake VanderPlas.
It turns his bestselling book into interactive Jupyter notebooks that teach:🧮 NumPy → fast array operations📊 Pandas → data cleaning & analysis📈 Matplotlib → clear visualizations🤖 Scikit-learn → machine learning made simple🛸 IPython → efficient coding environment
Don’t chase complexity... Start with clarity.
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Imagine an AI assistant for data scientists that builds an entire Jupyter notebook on request
Data Copilot - is like Cursor, but it is designed for working with data📊
Imports Dataset, cleans it, teaches models, tests it - all in one pass✅ .
And yes, he is completely open-source🤝 .
📝 Documentation
🖥 GitHub
.
Data Copilot - is like Cursor, but it is designed for working with data
Imports Dataset, cleans it, teaches models, tests it - all in one pass
And yes, he is completely open-source
.
✈️ Our Telegram channels⬅️ 📱 Our WhatsApp channel⬅️
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It’s a hands-on learning environment powered by Jupyter Notebooks
A curated set of practical projects that turn theory into action:🖼 Predictive modeling for Black Friday and BigMart sales🕯 Time series forecasting for airline passenger trends
🧬Breast cancer detection from medical datasets.💳 Credit card fraud detection.🏠 Boston housing price prediction.😳 Face recognition using OpenCV.
Each project is built with clarity and purpose👨💻 .
You can run everything directly in Google Colab — no setup required⚙ .
Start with a notebook… and walk away with real skills🏹 .
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