You don’t need years of experience in AI. You need real, world-class skills. pic.twitter.com/kgnite49Ag
— Satya Mallick (@LearnOpenCV) 16 janvier 2026
You don’t need years of experience in AI. You need real, world-class skills.
@learnopencv
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Master AI Skills Without Years of Experience Required
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YOLOv11 vs RF-DETR: Performance and Licensing Comparison
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Performance matters, but licensing decides. YOLOv11 vs. RF-DETR. pic.twitter.com/MDaiDhgpGQ
— Satya Mallick (@LearnOpenCV) 15 janvier 2026Performance matters, but licensing decides. YOLOv11 vs. RF-DETR.
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One Hour Daily Practice Transforms Your Career into AI
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One hour a day is enough to change your career into AI. pic.twitter.com/pYxP9LiCg5
— Satya Mallick (@LearnOpenCV) 13 janvier 2026One hour a day is enough to change your career into AI.
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AI Skills Alone Insufficient for Hiring Success
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AI skills aren’t enough. Proof and real projects are what get you hired. pic.twitter.com/XzVcCjQagG
— Satya Mallick (@LearnOpenCV) 12 janvier 2026AI skills aren’t enough. Proof and real projects are what get you hired.
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Upgrade Face Detection from Haar Cascades to OpenCV U-Net
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Still using Haar cascades for face detection? It’s time to move on ⚠️
— Satya Mallick (@LearnOpenCV) 9 janvier 2026
OpenCV’s U-Net–based face detector delivers better accuracy, robustness, and real-time performance—even on CPUs.
OpenCV 5 moving cascades to contrib says it all.#OpenCV #ComputerVision #FaceDetection… pic.twitter.com/Z6FLrCHEUpStill using Haar cascades for face detection? It’s time to move on OpenCV’s U-Net–based face detector delivers better accuracy, robustness, and real-time performance—even on CPUs.
OpenCV 5 moving cascades to contrib says it all. #OpenCV #ComputerVision #FaceDetection -
AI Career Opportunity: Talent Gap Shows You’re Early
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Think you’re too late for AI? The talent gap proves you’re early. pic.twitter.com/CdJpQOcZ9C
— Satya Mallick (@LearnOpenCV) 9 janvier 2026Think you’re too late for AI? The talent gap proves you’re early.
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Image Segmentation: Semantic, Instance, and Panoptic Types
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Image segmentation breaks an image into meaningful regions.
— Satya Mallick (@LearnOpenCV) 8 janvier 2026
This video explains the three main types:
• Semantic segmentation
• Instance segmentation
• Panoptic segmentation
Resources 📘
Semantic Segmentation (DeepLab v3+) → https://t.co/ytZJpGp7xT
Instance Segmentation… pic.twitter.com/j2YAoA4WmNImage segmentation breaks an image into meaningful regions. This video explains the three main types:
• Semantic segmentation
• Instance segmentation
• Panoptic segmentation Resources Semantic Segmentation (DeepLab v3+) → https://
vist.ly/4m4c5 Instance Segmentation -
Why AI Projects Fail and How to Succeed
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The real reasons AI projects stall, and how to make them unstoppable. pic.twitter.com/3A0kgDPfMy
— Satya Mallick (@LearnOpenCV) 8 janvier 2026The real reasons AI projects stall, and how to make them unstoppable.
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Modern Deep Learning Detectors: Why HOG Is Outdated
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Still using HOG for object or pedestrian detection? You probably shouldn’t.
— Satya Mallick (@LearnOpenCV) 7 janvier 2026
HOG was great years ago, but today it’s slow, fragile to viewpoint changes, and struggles with small or occluded objects.
Modern deep learning detectors in OpenCV — like U-Net (faces), YOLO, and… pic.twitter.com/YM3oDyF0XXStill using HOG for object or pedestrian detection? You probably shouldn’t. HOG was great years ago, but today it’s slow, fragile to viewpoint changes, and struggles with small or occluded objects. Modern deep learning detectors in OpenCV — like U-Net (faces), YOLO, and
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Why Chasing New Models and More Data Often Fails
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Why chasing new models and more data often fails in AI training. pic.twitter.com/5hqmEYXSWi
— Satya Mallick (@LearnOpenCV) 7 janvier 2026Why chasing new models and more data often fails in AI training.