🚀 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://yangx.top/DataScienceN

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://yangx.top/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

🎯 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://yangx.top/DataScienceN

🚀 The new HQ-SAM (High-Quality Segment Anything Model) has just been added to the Hugging Face Transformers library!

This is an enhanced version of the original SAM (Segment Anything Model) introduced by Meta in 2023. HQ-SAM significantly improves the segmentation of fine and detailed objects, while preserving all the powerful features of SAM — including prompt-based interaction, fast inference, and strong zero-shot performance. That means you can easily switch to HQ-SAM wherever you used SAM!

The improvements come from just a few additional learnable parameters. The authors collected a high-quality dataset with 44,000 fine-grained masks from various sources, and impressively trained the model in just 4 hours using 8 GPUs — all while keeping the core SAM weights frozen.

The newly introduced parameters include:

* A High-Quality Token
* A Global-Local Feature Fusion mechanism

This work was presented at NeurIPS 2023 and still holds state-of-the-art performance in zero-shot segmentation on the SGinW benchmark.

📄 Documentation: https://lnkd.in/e5iDT6Tf
🧠 Model Access: https://lnkd.in/ehS6ZUyv
💻 Source Code: https://lnkd.in/eg5qiKC2



#ArtificialIntelligence #ComputerVision #Transformers #Segmentation #DeepLearning #PretrainedModels #ResearchAndDevelopment #AdvancedModels #ImageAnalysis #HQ_SAM #SegmentAnything #SAMmodel #ZeroShotSegmentation #NeurIPS2023 #AIresearch #FoundationModels #OpenSourceAI #SOTA

🌟https://yangx.top/DataScienceN

🔥Powerful Combo: Ultralytics YOLO11 + Sony Semicon | AITRIOS (Global) Platform + Raspberry Pi
We’ve recently updated our Sony IMX model export to fully support YOLO11n detection models! This means you can now seamlessly run YOLO11n models directly on Raspberry Pi AI Cameras powered by the Sony IMX500 sensor — making it even easier to develop advanced Edge AI applications. 💡
To test this new export workflow, I trained a model on the VisDrone dataset and exported it using the following command:
👉

yolo export model=<path_to_drone_model> format=imx data=VisDrone.yaml

🎥 The video below shows the result of this process!
🔍Benchmark results for YOLO11n on IMX500:✅ Inference Time: 62.50 ms✅ mAP50-95 (B): 0.644📌 Want to learn more about YOLO11 and Sony IMX500? Check it out here ➡️
https://docs.ultralytics.com/integrations/sony-imx500/

#EdgeAI#YOLO11#SonyIMX500#AITRIOS#ObjectDetection#RaspberryPiAI#ComputerVision#DeepLearning#OnDeviceAI#ModelDeployment

🌟https://yangx.top/DataScienceN