Github Top Repositories
13.5K subscribers
1.71K photos
59 videos
10 files
2.25K links
Top GitHub repositories in one place πŸš€
Explore the best projects in programming, AI, data science, and more.
Download Telegram
This media is not supported in your browser
VIEW IN TELEGRAM
πŸˆβ€β¬› TTT Long Video Generation πŸˆβ€β¬›

▢️ 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 πŸ’™

πŸ”— Review: https://t.ly/mhlTN
πŸ“„ Paper: arxiv.org/pdf/2504.05298
🌐 Project: test-time-training.github.io/video-dit
πŸ§‘β€πŸ’» Repo: github.com/test-time-training/ttt-video-dit

#AI #VideoGeneration #MachineLearning #DeepLearning #Transformers #TTT #GenerativeAI

πŸ” By: https://xn--r1a.website/DataScienceN5
Please open Telegram to view this post
VIEW IN TELEGRAM
πŸ‘3πŸ₯°2
πŸš€ 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. πŸš—πŸ’‘

🎯 Key Highlights:
βœ… YOLOv11 + GPT-4o-mini = High-precision number plate recognition
βœ… Real-time video processing in Google Colab
βœ… Smart prompt engineering for enhanced OCR performance

πŸ“’ A must-watch if you're into computer vision, deep learning, or OpenAI integrations!


πŸ”— Colab Notebook
▢️ Watch on YouTube


#YOLOv11 #GPT4o #OpenAI #ANPR #OCR #ComputerVision #DeepLearning #AI #DataScience #Python #Ultralytics #MachineLearning #Colab #NumberPlateRecognition

πŸ” By : https://xn--r1a.website/DataScienceN
πŸ‘2❀1πŸ”₯1
π‘―π’π’Žπ’π’ˆπ’“π’‚π’‘π’‰π’š 𝒂𝒏𝒅 π‘²π’†π’šπ’‘π’π’Šπ’π’• 𝒇𝒐𝒓 𝑭𝒐𝒐𝒕𝒃𝒂𝒍𝒍 π‘¨π’π’‚π’π’šπ’•π’Šπ’„π’” βš½οΈπŸ“

πŸš€ 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. πŸ—ΊοΈ
πŸ“Š Enhanced keypoint precision leads to a more reliable homography transformation, resulting in a richer, more accurate tactical view. βš™οΈβš‘

πŸ† For this work, we leveraged the championship-winning keypoint detection model from the SoccerNet Calibration Challenge:

πŸ“ˆ Implementing and evaluating this state‑of‑the‑art solution has deepened our appreciation for keypoint‑driven approaches in sports analytics. πŸ“ΉπŸ“Œ

πŸ”— https://lnkd.in/em94QDFE

πŸ“‘ By: https://xn--r1a.website/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
πŸ‘4❀1πŸ”₯1