๐ 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 โ 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 โ 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.
๐ Documentation: https://lnkd.in/e5iDT6Tf
๐ง Model Access: https://lnkd.in/ehS6ZUyv
๐ป 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
๐https://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 โ 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 โ 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.
๐ Documentation: https://lnkd.in/e5iDT6Tf
๐ง Model Access: https://lnkd.in/ehS6ZUyv
๐ป 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
๐https://t.iss.one/DataScienceN
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Forwarded from Python | Machine Learning | Coding | R
๐ Your balance is credited $4,000 , the owner of the channel wants to contact you!
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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
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Be sure to take advantage of our gift, admission is free, don't miss the opportunity, change your life for the better.
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This channels is for Programmers, Coders, Software Engineers.
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1๏ธโฃ Data Science
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๐ฅPowerful Combo: Ultralytics YOLO11 + Sony Semicon | AITRIOS (Global) Platform + Raspberry Pi
Weโve 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 โ making it even easier to develop advanced Edge AI applications. ๐ก
To test this new export workflow, I trained a model on the VisDrone dataset and exported it using the following command:
๐
๐Benchmark results for YOLO11n on IMX500:โ Inference Time: 62.50 msโ mAP50-95 (B): 0.644๐ Want to learn more about YOLO11 and Sony IMX500? Check it out here โก๏ธ
https://docs.ultralytics.com/integrations/sony-imx500/
#EdgeAI#YOLO11#SonyIMX500#AITRIOS#ObjectDetection#RaspberryPiAI#ComputerVision#DeepLearning#OnDeviceAI#ModelDeployment
๐https://t.iss.one/DataScienceN
Weโve 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 โ making it even easier to develop advanced Edge AI applications. ๐ก
To test this new export workflow, I trained a model on the VisDrone dataset and exported it using the following command:
๐
yolo export model=<path_to_drone_model> format=imx data=VisDrone.yaml๐ฅ The video below shows the result of this process!
๐Benchmark results for YOLO11n on IMX500:โ Inference Time: 62.50 msโ mAP50-95 (B): 0.644๐ Want to learn more about YOLO11 and Sony IMX500? Check it out here โก๏ธ
https://docs.ultralytics.com/integrations/sony-imx500/
#EdgeAI#YOLO11#SonyIMX500#AITRIOS#ObjectDetection#RaspberryPiAI#ComputerVision#DeepLearning#OnDeviceAI#ModelDeployment
๐https://t.iss.one/DataScienceN
Ultralytics
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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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python_basics.pdf
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๐ 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.
๐ Topics Covered:
Python Basics
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NumPy Essentials
Pandas Overview
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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.
๐ Topics Covered:
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Jupyter Notebook Tips
Importing Libraries
NumPy Essentials
Pandas Overview
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Some projects donโt scream for attention โ they quietly change the way we learn๐งโ๐ .
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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 data๐
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๐ Documentation
๐ฅ GitHub
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Data Copilot - is like Cursor, but it is designed for working with data
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Itโs a hands-on learning environment powered by Jupyter Notebooks
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๐งฌBreast cancer detection from medical datasets.๐ณ Credit card fraud detection.๐ Boston housing price prediction.๐ณ Face recognition using OpenCV.
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Forwarded from Python | Machine Learning | Coding | R
This channels is for Programmers, Coders, Software Engineers.
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1๏ธโฃ Data Science
2๏ธโฃ Machine Learning
3๏ธโฃ Data Visualization
4๏ธโฃ Artificial Intelligence
5๏ธโฃ Data Analysis
6๏ธโฃ Statistics
7๏ธโฃ Deep Learning
8๏ธโฃ programming Languages
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