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.iss.one/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

📡 By: https://t.iss.one/DataScienceN

📡 By: https://t.iss.one/DataScienceN

🎯 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.iss.one/DataScienceN

📡 By: https://t.iss.one/DataScienceN

🎉🚁 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:
[email protected]
Enjoy exploring the power of Unidrone v1.0!


💬https://huggingface.co/StephanST/unidrone

📡 By: https://t.iss.one/DataScienceN

📡 By: https://t.iss.one/DataScienceN

🚀 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.iss.one/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://t.iss.one/DataScienceN

🎁 Your balance is credited $4,000 , the owner of the channel wants to contact you!

Dear subscriber, we would like to thank you very much for supporting our channel, and as a token of our gratitude we would like to provide you with free access to Lisa's investor channel, with the help of which you can earn today

t.iss.one/Lisainvestor

Be sure to take advantage of our gift, admission is free, don't miss the opportunity, change your life for the better.

You can follow the link :
https://t.iss.one/+0DQSCADFTUA3N2Qx

Follow me on LinkedIn for more projects and jobs

https://www.linkedin.com/in/hussein-sheikho-4a8187246

🔥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://t.iss.one/DataScienceN