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Github Top Repositories
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Top GitHub repositories in one place 🚀 Explore the best projects in programming, AI, data science, and more.
Mostrar más📈 Análisis del canal de Telegram Github Top Repositories
El canal Github Top Repositories (@githubre) en el segmento lingüístico de Inglés es un actor destacado. Actualmente la comunidad reúne a 13 330 suscriptores, ocupando la posición 15 272 en la categoría Educación y el puesto 32 126 en la región India.
📊 Métricas de audiencia y dinámica
Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 13 330 suscriptores.
Según los últimos datos del 15 junio, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 413, y en las últimas 24 horas de 8, conservando un alto alcance.
- Estado de verificación: No verificado
- Tasa de interacción (ER): El promedio de interacción de la audiencia es 1.07%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 0.79% de reacciones respecto al total de suscriptores.
- Alcance de las publicaciones: Cada publicación recibe en promedio 143 visualizaciones. En el primer día suele acumular 105 visualizaciones.
- Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 1.
- Intereses temáticos: El contenido se centra en temas clave como repository, fork, programming, statistic, description.
📝 Descripción y política de contenido
El autor describe el recurso como un espacio para expresar opiniones subjetivas:
“Top GitHub repositories in one place 🚀
Explore the best projects in programming, AI, data science, and more.”
Gracias a la alta frecuencia de actualizaciones (últimos datos recibidos el 16 junio, 2026), el canal mantiene la vigencia y un amplio alcance. La analítica demuestra que la audiencia interactúa activamente con el contenido, lo que lo convierte en un punto de referencia dentro de la categoría Educación.
13 330
Suscriptores
+824 horas
+927 días
+41330 días
Archivo de publicaciones
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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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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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🚀💡 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.me/DataScienceN
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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.
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🚦 Traffic Lights Detection using Ultralytics YOLO11! 🧠🤖
Ultralytics YOLOv11 can be used for real-time detection of 🚫 red, ⚠️ yellow, and ✅ green traffic lights — boosting road safety, traffic management, and autonomous navigation 🛣️🚗
🌆 Unlock new possibilities in:
🌐 Smart city planning 🏙️
🚦 Adaptive traffic control
🔍 Computer vision-powered transportation systems
🚀 Get started now ➡️ https://ow.ly/XQyG50VgcR3
📡 By: https://t.me/DataScienceN
13 332
Repost from Machine Learning with Python
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
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Limited FREE Seat !!!!!!
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🍓Strawberry counting using Ultralytics Solutions🔥📸 Counting strawberries manually is slow, inconsistent, and hard to scale.But what if a computer vision system could do it for you — in real time? ⏱️
With Ultralytics Solutions, you can effortlessly detect, track, and count strawberries with precision.💡 Best part? It works seamlessly with various object detection models like YOLOv11, YOLOv9, YOLOv12, and more!
🌟 Advantages:
✔️ Get real-time insights into how much produce is available — perfect for planning & logistics 📦🚛
✅ Track strawberry flow on conveyor belts to spot slowdowns, errors, or quality issues 🍓
✔️ Maintain an accurate count of packed items with no manual work, reducing human error
📉🚀Get started today https://docs.ultralytics.com/guides/object-counting/
🔍 By : https://t.me/DataScienceN
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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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🔷 Ultralytics YOLO11!🚀
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.
✅ 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:
🔹 Object Detection
🔹 Segmentation
🔹 Classification
🔹 Pose Estimation
🔹 Oriented Bounding Boxes (OBB)
---
⚡ Quick Start
Run inference instantly with:
yolo predict model="yolo11n.pt"
---
📎 Learn more and explore the documentation here:
🔗 https://ow.ly/mKOC50Tyyok
🔍 By : https://t.me/DataScienceN
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🚀 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. 🚗💡
🎯 Key Highlights:
✅ YOLOv11 + GPT-4o-mini = High-precision number plate recognition
✅ Real-time video processing in Google Colab
✅ Smart prompt engineering for enhanced OCR performance
📢 A must-watch if you're into computer vision, deep learning, or OpenAI integrations!
🔗 Colab Notebook
▶️ Watch on YouTube
#YOLOv11 #GPT4o #OpenAI #ANPR #OCR #ComputerVision #DeepLearning #AI #DataScience #Python #Ultralytics #MachineLearning #Colab #NumberPlateRecognition
🔍 By : https://t.me/DataScienceN
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🐈 TTT Long Video Generation 🐈
👉 A novel architecture for video generation, adapting the #CogVideoX 5B model by incorporating #TestTimeTraining (TTT) layers.
Adding TTT layers into a pre-trained Transformer enables generating a one-minute clip from text storyboards.
Videos, code & annotations released 💙
🔗 Review: https://t.ly/mhlTN
📄 Paper: arxiv.org/pdf/2504.05298
🌐 Project: test-time-training.github.io/video-dit
💻 Repo: github.com/test-time-training/ttt-video-dit
#AI #VideoGeneration #MachineLearning #DeepLearning #Transformers #TTT #GenerativeAI
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AI-Powered Digit Recognition Project is Here!
Unleashing the power of Computer Vision + Deep Learning + Speech Processing
Here’s what this awesome project can do:
✍️ Draw any digit on the screen
🧠 A custom CNN model (trained on MNIST with PyTorch) recognizes it instantly
🔊 The system speaks the digit out loud using speech synthesis
🎰 Achieves 97%+ accuracy on handwritten digits
🧩 Built using PyTorch + OpenCV
⚙️ Ready to evolve into a full OCR engine for complex handwriting/text
This real-time, interactive AI tool is a perfect example of applied machine learning in action!
📓 Notebook:
🔗 https://github.com/AlirezaChahardoli/MNIST-Classification-with-PyTorch
🔍 By: https://t.me/DataScienceN5
13 332
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
