📡 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

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🔥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

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