Github Top Repositories

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Repost from Machine Learning with Python

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.me/addlist/8_rRW2scgfRhOTc0 ✅ https://t.me/Codeprogrammer

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🚀 Retail Fashion Sales Data Analysis Here's a fascinating project in the field of data analysis, focused on real-world fashion retail sales. The dataset contains 3,400 records of customer purchases, including item types, purchase amounts, customer ratings, and payment methods. 🔍 Project Goals: - Understand customer purchasing behavior - Identify the most popular products - Analyze preferred payment methods 📊 The dataset was first cleaned using Pandas to handle missing values, and then insightful visualizations were created with Matplotlib to reveal hidden patterns in the data. 🔗Data source: https://lnkd.in/dbGbuhG7 📓 Check out the full notebook here: 🔗 https://lnkd.in/dhnJpk47 If you're interested in customer behavior analytics and working with real-world retail data, this project is a great source of insight! 🌟 📡 By: https://t.me/DataScienceN

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📡 By: https://t.me/DataScienceN

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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: stephan.sturges@gmail.com Enjoy exploring the power of Unidrone v1.0! 💬https://huggingface.co/StephanST/unidrone 📡 By: https://t.me/DataScienceN

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Repost from Github Top Repositories

🎉🚁 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: stephan.sturges@gmail.com Enjoy exploring the power of Unidrone v1.0! 💬https://huggingface.co/StephanST/unidrone 📡 By: https://t.me/DataScienceN

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📡 By: https://t.me/DataScienceN

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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: stephan.sturges@gmail.com Enjoy exploring the power of Unidrone v1.0! 💬https://huggingface.co/StephanST/unidrone 📡 By: https://t.me/DataScienceN

13 354

Repost from Python Courses & Resources

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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

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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.me/DataScienceN

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📡 By: https://t.me/DataScienceN

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🚀 CoMotion: Concurrent Multi-person 3D Motion 🚶‍♂️🚶‍♀️ 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 📦 Access the code and weights here: 🔗 Code & Weights 🔗 View Project 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 📡 By: https://t.me/DataScienceN

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📡 By: https://t.me/DataScienceN

13 354

Repost from Machine Learning with Python

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.me/addlist/8_rRW2scgfRhOTc0 ✅ https://t.me/Codeprogrammer

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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.me/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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❤️https://t.me/DataScienceN

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Repost from Machine Learning with Python

Follow me on linkedin (important for you) https://www.linkedin.com/in/hussein-sheikho-4a8187246

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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.me/DataScienceN

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