π New Tutorial: Automatic Number Plate Recognition (ANPR) with YOLOv11 + GPT-4o-mini!
This hands-on tutorial shows you how to combine the real-time detection power of YOLOv11 with the language understanding of GPT-4o-mini to build a smart, high-accuracy ANPR system! From setup to smart prompt engineering, everything is covered step-by-step. ππ‘
π― Key Highlights:
β YOLOv11 + GPT-4o-mini = High-precision number plate recognition
β Real-time video processing in Google Colab
β Smart prompt engineering for enhanced OCR performance
π’ A must-watch if you're into computer vision, deep learning, or OpenAI integrations!
π Colab Notebook
βΆοΈ Watch on YouTube
#YOLOv11 #GPT4o #OpenAI #ANPR #OCR #ComputerVision #DeepLearning #AI #DataScience #Python #Ultralytics #MachineLearning #Colab #NumberPlateRecognition
π By : https://t.iss.one/DataScienceN
This hands-on tutorial shows you how to combine the real-time detection power of YOLOv11 with the language understanding of GPT-4o-mini to build a smart, high-accuracy ANPR system! From setup to smart prompt engineering, everything is covered step-by-step. ππ‘
π― Key Highlights:
β YOLOv11 + GPT-4o-mini = High-precision number plate recognition
β Real-time video processing in Google Colab
β Smart prompt engineering for enhanced OCR performance
π’ A must-watch if you're into computer vision, deep learning, or OpenAI integrations!
π Colab Notebook
βΆοΈ Watch on YouTube
#YOLOv11 #GPT4o #OpenAI #ANPR #OCR #ComputerVision #DeepLearning #AI #DataScience #Python #Ultralytics #MachineLearning #Colab #NumberPlateRecognition
π By : https://t.iss.one/DataScienceN
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π· Ultralytics YOLO11!π
Developed by Jing Qiu and Glenn Jocher, YOLO11 represents a major leap forward in object detection technology, reflecting months of dedicated research and development by the Ultralytics team.
β YOLO11 Key Features:
- Enhanced architecture for high-precision detection and complex vision tasks
- Faster inference speeds with balanced accuracy
- Higher precision while using 22% fewer parameters
- Seamlessly deployable across edge devices, cloud, and GPU systems
- Full support for:
πΉ Object Detection
πΉ Segmentation
πΉ Classification
πΉ Pose Estimation
πΉ Oriented Bounding Boxes (OBB)
---
β‘ Quick Start
Run inference instantly with:
yolo predict model="yolo11n.pt"
---
π Learn more and explore the documentation here:
π https://ow.ly/mKOC50Tyyok
π By : https://t.iss.one/DataScienceN
Developed by Jing Qiu and Glenn Jocher, YOLO11 represents a major leap forward in object detection technology, reflecting months of dedicated research and development by the Ultralytics team.
β YOLO11 Key Features:
- Enhanced architecture for high-precision detection and complex vision tasks
- Faster inference speeds with balanced accuracy
- Higher precision while using 22% fewer parameters
- Seamlessly deployable across edge devices, cloud, and GPU systems
- Full support for:
πΉ Object Detection
πΉ Segmentation
πΉ Classification
πΉ Pose Estimation
πΉ Oriented Bounding Boxes (OBB)
---
β‘ Quick Start
Run inference instantly with:
yolo predict model="yolo11n.pt"
---
π Learn more and explore the documentation here:
π https://ow.ly/mKOC50Tyyok
π By : https://t.iss.one/DataScienceN
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π Join Our IT Study Group for Daily Updates & Tips:
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π₯Whether you're preparing for #Cisco #AWS #PMP #Python #Excel #Google #Microsoft #AI or any other in-demand certification β SPOTO has got you covered!
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π Join Our IT Study Group for Daily Updates & Tips:
π https://chat.whatsapp.com/E3Vkxa19HPO9ZVkWslBO8s
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With Ultralytics Solutions, you can effortlessly detect, track, and count strawberries with precision.
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Forget Coding; start Vibing! Tell AI what you want, and watch it build your dream website while you enjoy a cup of coffee.
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π¦ Traffic Lights Detection using Ultralytics YOLO11! π§ π€
Ultralytics YOLOv11 can be used for real-time detection of π« red, β οΈ yellow, and β green traffic lights β boosting road safety, traffic management, and autonomous navigation π£οΈπ
π Unlock new possibilities in:
π Smart city planning ποΈ
π¦ Adaptive traffic control
π Computer vision-powered transportation systems
π Get started now β‘οΈ https://ow.ly/XQyG50VgcR3
π‘ By: https://t.iss.one/DataScienceN
Ultralytics YOLOv11 can be used for real-time detection of π« red, β οΈ yellow, and β green traffic lights β boosting road safety, traffic management, and autonomous navigation π£οΈπ
π Unlock new possibilities in:
π Smart city planning ποΈ
π¦ Adaptive traffic control
π Computer vision-powered transportation systems
π Get started now β‘οΈ https://ow.ly/XQyG50VgcR3
π‘ By: https://t.iss.one/DataScienceN
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Python | Machine Learning | Coding | R
Forget Coding; start Vibing! Tell AI what you want, and watch it build your dream website while you enjoy a cup of coffee. Date: Thursday, April 17th at 9 PM IST Register for FREE: https://lu.ma/4nczknky?tk=eAT3Bi Limited FREE Seat !!!!!!
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π₯ SAMWISE: Infusing Wisdom in SAM2 for Text-Driven Video Segmentation, has been accepted at hashtag#CVPR2025! π
make #SegmentAnything wiser by enabling it to understand text promptsβall with just 4.9M additional trainable parameters.
make #SegmentAnything wiser by enabling it to understand text promptsβall with just 4.9M additional trainable parameters.
π3
ππ‘ What makes SAMWISE special?
πΉ Textual & Temporal Adapter for #SAM2 β We introduce a novel adapter that enables early fusion of text and visual features, allowing SAM2 to understand textual queries while modeling temporal evolution across frames.
πΉ Tracking Bias Correction β SAM2 tends to keep tracking an object even when a better match for the text query appears. Our learnable correction mechanism dynamically adjusts its focus, ensuring it tracks the most relevant object at every moment.
β¨ State-of-the-art performance across multiple benchmarks:
β New SOTA on Referring Video Object Segmentation (RVOS)
β New SOTA on image-level Referring Segmentation (RIS)β Runs online
β Requires no fine-tuning of SAM2 weights
π SAMWISE is the first text-driven segmentation approach built on SAM2 that achieves SOTA while staying lightweight and online.
π Project page: https://lnkd.in/dtBHBVbG
π» Code and models: https://lnkd.in/d-fadFGd
π Paper: arxiv.org/abs/2411.17646
π‘ By: https://t.iss.one/DataScienceN
πΉ Textual & Temporal Adapter for #SAM2 β We introduce a novel adapter that enables early fusion of text and visual features, allowing SAM2 to understand textual queries while modeling temporal evolution across frames.
πΉ Tracking Bias Correction β SAM2 tends to keep tracking an object even when a better match for the text query appears. Our learnable correction mechanism dynamically adjusts its focus, ensuring it tracks the most relevant object at every moment.
β¨ State-of-the-art performance across multiple benchmarks:
β New SOTA on Referring Video Object Segmentation (RVOS)
β New SOTA on image-level Referring Segmentation (RIS)β Runs online
β Requires no fine-tuning of SAM2 weights
π SAMWISE is the first text-driven segmentation approach built on SAM2 that achieves SOTA while staying lightweight and online.
π Project page: https://lnkd.in/dtBHBVbG
π» Code and models: https://lnkd.in/d-fadFGd
π Paper: arxiv.org/abs/2411.17646
π‘ By: https://t.iss.one/DataScienceN
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π1
Instance segmentation vs semantic segmentation using Ultralytics π₯
β
Semantic segmentation classifies each pixel into a category (e.g., "car," "horse"), but doesn't distinguish between different objects of the same class.
β
Instance segmentation goes further by identifying and separating individual objects within the same category (e.g., horse 1 vs. horse 2).
Each type has its strengths, semantic segmentation is more common in medical imaging due to its focus on pixel-wise classification without needing to distinguish individual object instances. Its simplicity and adaptability also make it widely applicable across industries.
π https://docs.ultralytics.com/guides/instance-segmentation-and-tracking/
π By: https://t.iss.one/DataScienceN
Each type has its strengths, semantic segmentation is more common in medical imaging due to its focus on pixel-wise classification without needing to distinguish individual object instances. Its simplicity and adaptability also make it widely applicable across industries.
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Ultralytics
Instance Segmentation with Object Tracking
Master instance segmentation and tracking with Ultralytics YOLO11. Learn techniques for precise object identification and tracking.
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π―πππππππππ πππ
π²πππππππ πππ ππππππππ π¨ππππππππ β½οΈπ
π Highlighting the latest strides in football field analysis using computer vision, this post shares a single frame from our video that demonstrates how homography and keypoint detection combine to produce precise minimap overlays. π§ π―
𧩠At the heart of this project lies the refinement of field keypoint extraction. Our experiments show a clear link between both the number and accuracy of detected keypoints and the overall quality of the minimap. πΊοΈ
π Enhanced keypoint precision leads to a more reliable homography transformation, resulting in a richer, more accurate tactical view. βοΈβ‘
π For this work, we leveraged the championship-winning keypoint detection model from the SoccerNet Calibration Challenge:
π Implementing and evaluating this stateβofβtheβart solution has deepened our appreciation for keypointβdriven approaches in sports analytics. πΉπ
π https://lnkd.in/em94QDFE
π‘ By: https://t.iss.one/DataScienceN
#ObjectDetection hashtag#DeepLearning hashtag#Detectron2 hashtag#ComputerVision hashtag#AI
hashtag#Football hashtag#SportsTech hashtag#MachineLearning hashtag#ComputerVision hashtag#AIinSports
hashtag#FutureOfFootball hashtag#SportsAnalytics
hashtag#TechInnovation hashtag#SportsAI hashtag#AIinFootball hashtag#AI hashtag#AIandSports hashtag#AIandSports
hashtag#FootballAnalytics hashtag#python hashtag#ai hashtag#yolo hashtag
π Highlighting the latest strides in football field analysis using computer vision, this post shares a single frame from our video that demonstrates how homography and keypoint detection combine to produce precise minimap overlays. π§ π―
𧩠At the heart of this project lies the refinement of field keypoint extraction. Our experiments show a clear link between both the number and accuracy of detected keypoints and the overall quality of the minimap. πΊοΈ
π Enhanced keypoint precision leads to a more reliable homography transformation, resulting in a richer, more accurate tactical view. βοΈβ‘
π For this work, we leveraged the championship-winning keypoint detection model from the SoccerNet Calibration Challenge:
π Implementing and evaluating this stateβofβtheβart solution has deepened our appreciation for keypointβdriven approaches in sports analytics. πΉπ
π https://lnkd.in/em94QDFE
π‘ By: https://t.iss.one/DataScienceN
#ObjectDetection hashtag#DeepLearning hashtag#Detectron2 hashtag#ComputerVision hashtag#AI
hashtag#Football hashtag#SportsTech hashtag#MachineLearning hashtag#ComputerVision hashtag#AIinSports
hashtag#FutureOfFootball hashtag#SportsAnalytics
hashtag#TechInnovation hashtag#SportsAI hashtag#AIinFootball hashtag#AI hashtag#AIandSports hashtag#AIandSports
hashtag#FootballAnalytics hashtag#python hashtag#ai hashtag#yolo hashtag
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Forwarded from Python | Machine Learning | Coding | R
This channels is for Programmers, Coders, Software Engineers.
0οΈβ£ Python
1οΈβ£ Data Science
2οΈβ£ Machine Learning
3οΈβ£ Data Visualization
4οΈβ£ Artificial Intelligence
5οΈβ£ Data Analysis
6οΈβ£ Statistics
7οΈβ£ Deep Learning
8οΈβ£ programming Languages
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Introducing CoMotion, a project that detects and tracks detailed 3D poses of multiple people using a single monocular camera stream. This system maintains temporally coherent predictions in crowded scenes filled with difficult poses and occlusions, enabling online tracking through frames with high accuracy.
π Key Features:
- Precise detection and tracking in crowded scenes
- Temporal coherence even with occlusions
- High accuracy in tracking multiple people over time
This project advances 3D human motion tracking by offering faster and more accurate tracking of multiple individuals compared to existing systems.
#AI #DeepLearning #3DTracking #ComputerVision #PoseEstimation
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