Available now! Telegram Research 2025 β the year's key insights 
Github Top Repositories
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Top GitHub repositories in one place π Explore the best projects in programming, AI, data science, and more.
Show moreπ Analytical overview of Telegram channel Github Top Repositories
Channel Github Top Repositories (@githubre) in the English language segment is an active participant. Currently, the community unites 13 332 subscribers, ranking 15 267 in the Education category and 32 065 in the India region.
π Audience metrics and dynamics
Since its creation on Π½Π΅Π²ΡΠ΄ΠΎΠΌΠΎ, the project has demonstrated rapid growth, gathering an audience of 13 332 subscribers.
According to the latest data from 16 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 415 over the last 30 days and by 4 over the last 24 hours, overall reach remains high.
- Verification status: Not verified
- Engagement rate (ER): The average audience engagement rate is 1.07%. Within the first 24 hours after publication, content typically collects 0.80% reactions from the total number of subscribers.
- Post reach: On average, each post receives 143 views. Within the first day, a publication typically gains 106 views.
- Reactions and interaction: The audience actively supports content: the average number of reactions per post is 1.
- Thematic interests: Content is focused on key topics such as repository, fork, programming, statistic, description.
π Description and content policy
The author describes the resource as a platform for expressing subjective opinions:
βTop GitHub repositories in one place π
Explore the best projects in programming, AI, data science, and more.β
Thanks to the high frequency of updates (latest data received on 17 June, 2026), the channel maintains relevance and a high level of publication reach. Analytics show that the audience actively interacts with content, making it an important point of influence in the Education category.
13 332
Subscribers
+424 hours
+837 days
+41530 days
Posts Archive
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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.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
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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.me/DataScienceN
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Really attractive.
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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.me/DataScienceN
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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.
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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.me/DataScienceN
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Repost from Machine Learning with Python
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πStrawberry counting using Ultralytics Solutionsπ₯πΈ Counting strawberries manually is slow, inconsistent, and hard to scale.But what if a computer vision system could do it for you β in real time? β±οΈ
With Ultralytics Solutions, you can effortlessly detect, track, and count strawberries with precision.π‘ Best part? It works seamlessly with various object detection models like YOLOv11, YOLOv9, YOLOv12, and more!
π Advantages:
βοΈ Get real-time insights into how much produce is available β perfect for planning & logistics π¦π
β
Track strawberry flow on conveyor belts to spot slowdowns, errors, or quality issues π
βοΈ Maintain an accurate count of packed items with no manual work, reducing human error
ππGet started today https://docs.ultralytics.com/guides/object-counting/
π By : https://t.me/DataScienceN
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13 332
Repost from Machine Learning with Python
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
β
https://t.me/addlist/8_rRW2scgfRhOTc0
β
https://t.me/Codeprogrammer
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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.me/DataScienceN
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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.me/DataScienceN
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π TTT Long Video Generation π
π 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 π
π Review: https://t.ly/mhlTN
π Paper: arxiv.org/pdf/2504.05298
π Project: test-time-training.github.io/video-dit
π» Repo: github.com/test-time-training/ttt-video-dit
#AI #VideoGeneration #MachineLearning #DeepLearning #Transformers #TTT #GenerativeAI
βοΈ BEST DATA SCIENCE CHANNELS ON TELEGRAM βοΈ
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AI-Powered Digit Recognition Project is Here!
Unleashing the power of Computer Vision + Deep Learning + Speech Processing
Hereβs what this awesome project can do:
βοΈ Draw any digit on the screen
π§ A custom CNN model (trained on MNIST with PyTorch) recognizes it instantly
π The system speaks the digit out loud using speech synthesis
π° Achieves 97%+ accuracy on handwritten digits
𧩠Built using PyTorch + OpenCV
βοΈ 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!
π Notebook:
π https://github.com/AlirezaChahardoli/MNIST-Classification-with-PyTorch
π By: https://t.me/DataScienceN5
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Repost from Machine Learning with Python
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
β
https://t.me/addlist/8_rRW2scgfRhOTc0
β
https://t.me/Codeprogrammer
