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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🔥 SAMWISE: Infusing Wisdom in SAM2 for Text-Driven Video Segmentation, has been accepted at hashtag#CVPR2025! 🎉
make #SegmentAnything wiser by enabling it to understand text prompts—all with just 4.9M additional trainable parameters.
make #SegmentAnything wiser by enabling it to understand text prompts—all with just 4.9M additional trainable parameters.
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🚀💡 What makes SAMWISE special?
🔹 Textual & Temporal Adapter for #SAM2 – 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.
🔹 Tracking Bias Correction – 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.
✨ State-of-the-art performance across multiple benchmarks:
✅ New SOTA on Referring Video Object Segmentation (RVOS)
✅ New SOTA on image-level Referring Segmentation (RIS)✅ Runs online
✅ Requires no fine-tuning of SAM2 weights
🚀 SAMWISE is the first text-driven segmentation approach built on SAM2 that achieves SOTA while staying lightweight and online.
🏠 Project page: https://lnkd.in/dtBHBVbG
💻 Code and models: https://lnkd.in/d-fadFGd
🔗 Paper: arxiv.org/abs/2411.17646
📡 By: https://t.iss.one/DataScienceN
🔹 Textual & Temporal Adapter for #SAM2 – 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.
🔹 Tracking Bias Correction – 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.
✨ State-of-the-art performance across multiple benchmarks:
✅ New SOTA on Referring Video Object Segmentation (RVOS)
✅ New SOTA on image-level Referring Segmentation (RIS)✅ Runs online
✅ Requires no fine-tuning of SAM2 weights
🚀 SAMWISE is the first text-driven segmentation approach built on SAM2 that achieves SOTA while staying lightweight and online.
🏠 Project page: https://lnkd.in/dtBHBVbG
💻 Code and models: https://lnkd.in/d-fadFGd
🔗 Paper: arxiv.org/abs/2411.17646
📡 By: https://t.iss.one/DataScienceN
LinkedIn
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Login to LinkedIn to keep in touch with people you know, share ideas, and build your career.
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Instance segmentation vs semantic segmentation using Ultralytics 🔥
✅ Semantic segmentation classifies each pixel into a category (e.g., "car," "horse"), but doesn't distinguish between different objects of the same class.
✅ 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.
🔗 https://docs.ultralytics.com/guides/instance-segmentation-and-tracking/
🌐 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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Forwarded from Python | Machine Learning | Coding | R
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𝑯𝒐𝒎𝒐𝒈𝒓𝒂𝒑𝒉𝒚 𝒂𝒏𝒅 𝑲𝒆𝒚𝒑𝒐𝒊𝒏𝒕 𝒇𝒐𝒓 𝑭𝒐𝒐𝒕𝒃𝒂𝒍𝒍 𝑨𝒏𝒂𝒍𝒚𝒕𝒊𝒄𝒔 ⚽️📐
🚀 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. 🧠🎯
🧩 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. 🗺️
📊 Enhanced keypoint precision leads to a more reliable homography transformation, resulting in a richer, more accurate tactical view. ⚙️⚡
🏆 For this work, we leveraged the championship-winning keypoint detection model from the SoccerNet Calibration Challenge:
📈 Implementing and evaluating this state‑of‑the‑art solution has deepened our appreciation for keypoint‑driven approaches in sports analytics. 📹📌
🔗 https://lnkd.in/em94QDFE
📡 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
🚀 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. 🧠🎯
🧩 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. 🗺️
📊 Enhanced keypoint precision leads to a more reliable homography transformation, resulting in a richer, more accurate tactical view. ⚙️⚡
🏆 For this work, we leveraged the championship-winning keypoint detection model from the SoccerNet Calibration Challenge:
📈 Implementing and evaluating this state‑of‑the‑art solution has deepened our appreciation for keypoint‑driven approaches in sports analytics. 📹📌
🔗 https://lnkd.in/em94QDFE
📡 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
lnkd.in
LinkedIn
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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
✅ https://t.iss.one/addlist/8_rRW2scgfRhOTc0
✅ https://t.iss.one/Codeprogrammer
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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.
🔍 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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🎯 Trackers Library is Officially Released! 🚀
If you're working in computer vision and object tracking, this one's for you!
💡 Trackers is a powerful open-source library with support for a wide range of detection models and tracking algorithms:
✅ Plug-and-play compatibility with detection models from:
Roboflow Inference, Hugging Face Transformers, Ultralytics, MMDetection, and more!
✅ Tracking algorithms supported:
SORT, DeepSORT, and advanced trackers like StrongSORT, BoT‑SORT, ByteTrack, OC‑SORT – with even more coming soon!
🧩 Released under the permissive Apache 2.0 license – free for everyone to use and contribute.
👏 Huge thanks to Piotr Skalski for co-developing this library, and to Raif Olson and Onuralp SEZER for their outstanding contributions!
📌 Links:
🔗 GitHub
🔗 Docs
📚 Quick-start notebooks for SORT and DeepSORT are linked 👇🏻
https://www.linkedin.com/posts/skalskip92_trackers-library-is-out-plugandplay-activity-7321128111503253504-3U6-?utm_source=share&utm_medium=member_desktop&rcm=ACoAAEXwhVcBcv2n3wq8JzEai3TfWmKLRLTefYo
#ComputerVision #ObjectTracking #OpenSource #DeepLearning #AI
📡 By: https://t.iss.one/DataScienceN
If you're working in computer vision and object tracking, this one's for you!
💡 Trackers is a powerful open-source library with support for a wide range of detection models and tracking algorithms:
✅ Plug-and-play compatibility with detection models from:
Roboflow Inference, Hugging Face Transformers, Ultralytics, MMDetection, and more!
✅ Tracking algorithms supported:
SORT, DeepSORT, and advanced trackers like StrongSORT, BoT‑SORT, ByteTrack, OC‑SORT – with even more coming soon!
🧩 Released under the permissive Apache 2.0 license – free for everyone to use and contribute.
👏 Huge thanks to Piotr Skalski for co-developing this library, and to Raif Olson and Onuralp SEZER for their outstanding contributions!
📌 Links:
🔗 GitHub
🔗 Docs
📚 Quick-start notebooks for SORT and DeepSORT are linked 👇🏻
https://www.linkedin.com/posts/skalskip92_trackers-library-is-out-plugandplay-activity-7321128111503253504-3U6-?utm_source=share&utm_medium=member_desktop&rcm=ACoAAEXwhVcBcv2n3wq8JzEai3TfWmKLRLTefYo
#ComputerVision #ObjectTracking #OpenSource #DeepLearning #AI
📡 By: https://t.iss.one/DataScienceN
Linkedin
Trackers Library is Out! | Piotr Skalski
Trackers Library is Out! 🔥 🔥 🔥
- Plug‑and‑play integration with detectors from Transformers, Inference, Ultralytics, PaddlePaddle, MMDetection, and more.
- Built‑in support for SORT and DeepSORT today, with StrongSORT, BoT‑SORT, ByteTrack, OC‑SORT, and…
- Plug‑and‑play integration with detectors from Transformers, Inference, Ultralytics, PaddlePaddle, MMDetection, and more.
- Built‑in support for SORT and DeepSORT today, with StrongSORT, BoT‑SORT, ByteTrack, OC‑SORT, and…
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Forwarded from ENG. Hussein Sheikho
فرصة عمل عن بعد 🧑💻
لا يتطلب اي مؤهل او خبره الشركه تقدم تدريب كامل✨
ساعات العمل مرنه⏰
يتم التسجيل ثم التواصل معك لحضور لقاء تعريفي بالعمل والشركه
https://forms.gle/hqUZXu7u4uLjEDPv8
لا يتطلب اي مؤهل او خبره الشركه تقدم تدريب كامل
ساعات العمل مرنه
يتم التسجيل ثم التواصل معك لحضور لقاء تعريفي بالعمل والشركه
https://forms.gle/hqUZXu7u4uLjEDPv8
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Google Docs
فرصة عمل
العمل من المنزل هو ببساطة حل لمشكلة البطالة للشباب العربي ولكل البشر حول العالم،👌 انه طريقك للوصول الى الحرية المالية وبعيداً عن شغل الوظيفة الحكومية المملة والمرتبات الضعيفة..
أصبح الربح من الانترنت أمر حقيقي وليس وهم..🤜
نقدم لك فرصة الآن من غير أي شهادات…
أصبح الربح من الانترنت أمر حقيقي وليس وهم..🤜
نقدم لك فرصة الآن من غير أي شهادات…
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Forwarded from Python Courses
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🎉🚁 Introducing Unidrone v1.0 – The Next Generation of Aerial Object Detection Models 🚁🎉
We are excited to present Unidrone v1.0, a powerful collection of AI detection models based on YOLOv8, specially designed for object recognition in drone imagery.
🔍 What is Unidrone?
Unidrone is a smart fusion of two previous models: WALDO (optimized for nadir/overhead views) and NANO (designed for forward-looking angles). Now you no longer need to choose between them—Unidrone handles both angles with high accuracy!
📦 These models accurately detect objects in drone images taken from altitudes of approximately 50 to 1000 feet, regardless of camera angle.
🔍 Supported Object Classes:0️⃣ Person (walking, biking, swimming, skiing, etc.)
1️⃣ Bike & motorcycle
2️⃣ Light vehicles (cars, vans, ambulances, etc.)
3️⃣ Trucks
4️⃣ Bus
5️⃣ Boat & floating objects
6️⃣ Construction vehicles (e.g., tractors, loaders)
🚫 Note: This version of Unidrone does not include military-related classes or smoke detection. It's built solely for civilian and safety-focused applications.
📌 Use Cases:✅ Disaster recovery operations
✅ Wildlife and protected area monitoring
✅ Occupancy analysis (e.g., parking lots)
✅ Infrastructure surveillance
✅ Search and rescue (SAR)
✅ Crowd counting
✅ Ground-risk mitigation for drones
🛠️ The models are available in .pt format and can easily be exported to ONNX or TFLite. They also support visualization with Roboflow’s Supervision library for clean, annotated outputs.
🧠 If you're a machine learning practitioner, you can:
Fine-tune the models on your own dataset
Optimize for fast inference on edge devices
Quantize and deploy on low-cost hardware
Use the models to auto-label your own data
📨 If you're facing detection issues or want to contribute to future improvements, feel free to contact the developer:
[email protected]
Enjoy exploring the power of Unidrone v1.0!
💬https://huggingface.co/StephanST/unidrone
📡 By: https://t.iss.one/DataScienceN
We are excited to present Unidrone v1.0, a powerful collection of AI detection models based on YOLOv8, specially designed for object recognition in drone imagery.
🔍 What is Unidrone?
Unidrone is a smart fusion of two previous models: WALDO (optimized for nadir/overhead views) and NANO (designed for forward-looking angles). Now you no longer need to choose between them—Unidrone handles both angles with high accuracy!
📦 These models accurately detect objects in drone images taken from altitudes of approximately 50 to 1000 feet, regardless of camera angle.
🔍 Supported Object Classes:0️⃣ Person (walking, biking, swimming, skiing, etc.)
1️⃣ Bike & motorcycle
2️⃣ Light vehicles (cars, vans, ambulances, etc.)
3️⃣ Trucks
4️⃣ Bus
5️⃣ Boat & floating objects
6️⃣ Construction vehicles (e.g., tractors, loaders)
🚫 Note: This version of Unidrone does not include military-related classes or smoke detection. It's built solely for civilian and safety-focused applications.
📌 Use Cases:✅ Disaster recovery operations
✅ Wildlife and protected area monitoring
✅ Occupancy analysis (e.g., parking lots)
✅ Infrastructure surveillance
✅ Search and rescue (SAR)
✅ Crowd counting
✅ Ground-risk mitigation for drones
🛠️ The models are available in .pt format and can easily be exported to ONNX or TFLite. They also support visualization with Roboflow’s Supervision library for clean, annotated outputs.
🧠 If you're a machine learning practitioner, you can:
Fine-tune the models on your own dataset
Optimize for fast inference on edge devices
Quantize and deploy on low-cost hardware
Use the models to auto-label your own data
📨 If you're facing detection issues or want to contribute to future improvements, feel free to contact the developer:
[email protected]
Enjoy exploring the power of Unidrone v1.0!
💬https://huggingface.co/StephanST/unidrone
📡 By: https://t.iss.one/DataScienceN
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