π The model automatically detects individual products on a retail shelf and draws bounding boxes around each item. π The total number of detected products is displayed at the top of the image (Total Products: 115). π This system can be used for retail inventory management, shelf monitoring, stock analysis, and automation.
π Key Features:
Real-time object detection
Accurate product counting
Bounding box visualization
Useful for retail stores & supermarkets
Scalable for large inventory systems
π οΈ Technologies Used:
Python
OpenCV
Deep Learning (Object Detection Model) YOLOV12 Model
Computer Vision
This project is developed for academic and research purposes, but it can be extended for real-world retail applications.
π Fetal Echo Segmentation with Transformers π«
Transformers arenβt just for language models anymore β theyβre making a big impact in medical imaging! In fetal echocardiography, accurate segmentation of the heart is essential for early detection of congenital heart defects.
β¨ Why Transformers? πΉ Capture global context better than CNNs πΉ Handle ultrasound challenges (noise, motion, low contrast) πΉ Deliver state-of-the-art accuracy in cardiac structure segmentation
This could be a game-changer for prenatal care and diagnosis β€οΈ
π Integrating Deep Learning with Explainable AI (XAI) helps clinicians achieve accurate π« cardiac image analysis. π‘ It also reveals why decisions are made β boosting trust, transparency, and clinical adoption. β
Curious about how AI understands complex relationships in social networks, molecules, or even recommendation systems? π€ We break down Graph Neural Networks (GNNs)βthe powerful AI models designed for graph-structured dataβand dive into Explainable AI (XAI) techniques that make their decisions transparent!
πΉ What Youβll Learn:
β How GNNs Work: Message passing, graph convolutions, and attention mechanisms.
β Real-World Applications: Drug discovery, fraud detection, and more!
β Explainability in GNNs: Tools like GNNExplainer and attention visualization.
β Why It Matters: Trustworthy AI for critical decisions.
π Perfect for:
AI/ML enthusiasts
, Data scientists Researchers in graph-based AI
Anyone who wants to understand how AI "thinks"!
π Like, Subscribe, and Hit the Bell to stay updated on AI breakthroughs!
Vision Transformers (ViTs) have been evolving rapidly with several recent advancements that improve efficiency, accuracy, and robustness. Here are some of the latest trends and research in Vision Transformers....
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Digital Era 2 Agentic Era
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Image classification using Quantum Computing. A new Era ....
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MATLAB & PYTHON Deep Learning - jitectechnologies
π The model automatically detects individual products on a retail shelf and draws bounding boxes around each item.
π The total number of detected products is displayed at the top of the image (Total Products: 115).
π This system can be used for retail inventory management, shelf monitoring, stock analysis, and automation.
π Key Features:
Real-time object detection
Accurate product counting
Bounding box visualization
Useful for retail stores & supermarkets
Scalable for large inventory systems
π οΈ Technologies Used:
Python
OpenCV
Deep Learning (Object Detection Model) YOLOV12 Model
Computer Vision
This project is developed for academic and research purposes, but it can be extended for real-world retail applications.
8 months ago | [YT] | 0
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MATLAB & PYTHON Deep Learning - jitectechnologies
π― The power of Transformer models in object detection β enabling smarter, context-aware vision that sees beyond boundaries. π
10 months ago | [YT] | 0
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MATLAB & PYTHON Deep Learning - jitectechnologies
π Fetal Echo Segmentation with Transformers π«
Transformers arenβt just for language models anymore β theyβre making a big impact in medical imaging!
In fetal echocardiography, accurate segmentation of the heart is essential for early detection of congenital heart defects.
β¨ Why Transformers?
πΉ Capture global context better than CNNs
πΉ Handle ultrasound challenges (noise, motion, low contrast)
πΉ Deliver state-of-the-art accuracy in cardiac structure segmentation
This could be a game-changer for prenatal care and diagnosis β€οΈ
#AI #Transformers #MedicalImaging #FetalEcho #DeepLearning
1 year ago | [YT] | 0
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MATLAB & PYTHON Deep Learning - jitectechnologies
π Integrating Deep Learning with Explainable AI (XAI) helps clinicians achieve accurate π« cardiac image analysis.
π‘ It also reveals why decisions are made β boosting trust, transparency, and clinical adoption. β
#trending Research
Saliency Maps
Grad-CAM (Gradient-weighted Class Activation Mapping) , SHAP (SHapley Additive exPlanations)
LIME (Local Interpretable Model-agnostic Explanations), Concept Activation Vectors (CAVs)
Attention Maps , Feature Importance ,Surrogate Models
Layer-wise Relevance Propagation (LRP)
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MATLAB & PYTHON Deep Learning - jitectechnologies
Federated Learning is NOT invincible! - Are you ready to defend the health care system π¨ ?
While it keeps your data local, attackers can still:
Inject poisoned data π§ͺ
Plant backdoors πͺ€
Leak private patient info through gradients π
In this video, we break down the top federated learning attacks and how to defend against them like a pro:
β Robust Aggregation (Krum, Trimmed Mean, FLTrust)
β Differential Privacy (DP-SGD, Opacus)
β Secure Aggregation (SMPC, HE)
β Anomaly Detection & Trust Scores
β Backdoor Defense (Neurotoxin, FLAME, STRIP)
π‘ Whether you're building FL systems in healthcare, finance, or IoT, these techniques are essential to secure your models!
π Don't just federate β federate safely.
#FederatedLearning #AI #MachineLearning #Cybersecurity #DeepLearning #DataPrivacy #MedicalAI
1 year ago | [YT] | 0
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MATLAB & PYTHON Deep Learning - jitectechnologies
π Graph Neural Networks (GNNs) & Explainable AI (XAI) Explained! π§
Curious about how AI understands complex relationships in social networks, molecules, or even recommendation systems? π€ We break down Graph Neural Networks (GNNs)βthe powerful AI models designed for graph-structured dataβand dive into Explainable AI (XAI) techniques that make their decisions transparent!
πΉ What Youβll Learn:
β How GNNs Work: Message passing, graph convolutions, and attention mechanisms.
β Real-World Applications: Drug discovery, fraud detection, and more!
β Explainability in GNNs: Tools like GNNExplainer and attention visualization.
β Why It Matters: Trustworthy AI for critical decisions.
π Perfect for:
AI/ML enthusiasts , Data scientists
Researchers in graph-based AI
Anyone who wants to understand how AI "thinks"!
π Like, Subscribe, and Hit the Bell to stay updated on AI breakthroughs!
#AI #MachineLearning #GraphNeuralNetworks #ExplainableAI #GNN #XAI #DeepLearning #TechExplained
1 year ago | [YT] | 0
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MATLAB & PYTHON Deep Learning - jitectechnologies
Vision Transformers (ViTs) have been evolving rapidly with several recent advancements that improve efficiency, accuracy, and robustness. Here are some of the latest trends and research in Vision Transformers....
1 year ago | [YT] | 0
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