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Using Model Failure Modes as Fine-Tuning Data in CV

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@learnopencv

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05 May 2026 6h56

Part 2 🧊
In Part 1: accuracy is a trap.
In Part 2: failure modes ARE your fine-tuning dataset.
Probe the public model → collect data on exactly what it breaks on → fine-tune → repeat.
That's the loop most CV teams skip.
Dr. Satya Mallick 👇#ComputerVision #AI #YOLO… pic.twitter.com/6QheiFOfKh
— Satya Mallick (@LearnOpenCV) 5 mai 2026

Part 2 In Part 1: accuracy is a trap.
In Part 2: failure modes ARE your fine-tuning dataset.
Probe the public model → collect data on exactly what it breaks on → fine-tune → repeat.
That's the loop most CV teams skip.
Dr. Satya Mallick #ComputerVision #AI #YOLO

→ View original post on X — @learnopencv,

5 May 2026
Auditing CV Model Failure Modes Before Production Deployment

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@learnopencv

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04 May 2026 21h39

Accuracy is table stakes. Failure modes decide whether your CV model survives production.
Same benchmark scores. Opposite real-world performance.
Dr. Satya Mallick on what to audit before you ship 👇#ComputerVision #MachineLearning pic.twitter.com/USnlxQsp9k
— Satya Mallick (@LearnOpenCV) 4 mai 2026

Accuracy is table stakes. Failure modes decide whether your CV model survives production.
Same benchmark scores. Opposite real-world performance.
Dr. Satya Mallick on what to audit before you ship #ComputerVision #MachineLearning

→ View original post on X — @learnopencv,

4 May 2026
AI Progress Is Uneven Across Consumer and Frontier Coding Agents

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@learnopencv

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01 May 2026 21h23

Karpathy's clearest take of the year:
The AI debate isn't a debate. It's two groups looking at different parts of the curve.
→ Consumer AI still fumbles simple tasks
→ Frontier coding agents collapse days of work into hours
Both are real. The progress is just uneven.
🧵 Part 2… pic.twitter.com/0FW3VTRQW5
— Satya Mallick (@LearnOpenCV) 1 mai 2026

Karpathy's clearest take of the year:
The AI debate isn't a debate. It's two groups looking at different parts of the curve.
→ Consumer AI still fumbles simple tasks
→ Frontier coding agents collapse days of work into hours
Both are real. The progress is just uneven. Part 2

→ View original post on X — @learnopencv,

1 May 2026
YOLOE Enables Zero-Shot Open-Vocabulary Real-Time Object Detection

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@learnopencv

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01 May 2026 12h59

YOLOE = real-time object detection with NO retraining.
Type "delivery driver in a red jacket" → it finds them. Zero-shot. Open vocabulary. YOLO speed.
The closed-world era of computer vision is over. 🧵👇
🔗 https://t.co/MQ6NbUde7Y #YOLOE #ComputerVision #AI #DeepLearning #YOLO… pic.twitter.com/ROqCzxZXck
— Satya Mallick (@LearnOpenCV) 1 mai 2026

YOLOE = real-time object detection with NO retraining.
Type "delivery driver in a red jacket" → it finds them. Zero-shot. Open vocabulary. YOLO speed.
The closed-world era of computer vision is over. https://
vist.ly/42jd3
#YOLOE #ComputerVision #AI #DeepLearning #YOLO

→ View original post on X — @learnopencv,

1 May 2026
Vision Banana Unifies Object Detection Segmentation and Depth Estimation

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@learnopencv

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30 April 2026 9h14

What if image generators actually understand what they create?

Vision Banana proves it. One model handles:
→ Object detection
→ Instance segmentation
→ Metric depth from a single photo
→ Surface normal estimation

No specialist models. Beats SAM 3 on multiple segmentation… pic.twitter.com/mFY6lFTDC7
— Satya Mallick (@LearnOpenCV) 30 avril 2026

What if image generators actually understand what they create? Vision Banana proves it. One model handles:
→ Object detection → Instance segmentation → Metric depth from a single photo → Surface normal estimation No specialist models. Beats SAM 3 on multiple segmentation

→ View original post on X — @learnopencv,

30 April 2026
YOLO26 Pose Estimation: 17 Keypoints Real-Time Detection

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@learnopencv

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29 April 2026 10h18

YOLO26-Pose tracks 17 human keypoints in a single forward pass.
Smallest variant: 1.8 ms on a T4 GPU. ⚡
→ RLE for sharper localization
→ NMS-free inference (predictable latency)
→ MuSGD for stable training
Full breakdown 👇https://t.co/iU68Q7BvdF #ComputerVision #YOLO26… pic.twitter.com/G1i1AnqxXk
— Satya Mallick (@LearnOpenCV) 29 avril 2026

YOLO26-Pose tracks 17 human keypoints in a single forward pass.
Smallest variant: 1.8 ms on a T4 GPU. → RLE for sharper localization
→ NMS-free inference (predictable latency)
→ MuSGD for stable training
Full breakdown http://
learnopencv.com/yolo26-pose-es
timation-tutorial
…
#ComputerVision #YOLO26

→ View original post on X — @learnopencv,

29 April 2026
Two AI perspectives: Legacy models versus frontier model applications

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@learnopencv

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29 April 2026 0h36

Karpathy's framing of the AI debate is the cleanest I've seen:
Two groups. Same industry. Opposite conclusions.
→ Group 1: judged AI on free/old models. Saw the failures. Wrote it off.
→ Group 2: uses frontier models for hard technical work. Progress feels shocking.
But here's… pic.twitter.com/7cEY3CKvSx
— Satya Mallick (@LearnOpenCV) 28 avril 2026

Karpathy's framing of the AI debate is the cleanest I've seen:
Two groups. Same industry. Opposite conclusions.
→ Group 1: judged AI on free/old models. Saw the failures. Wrote it off.
→ Group 2: uses frontier models for hard technical work. Progress feels shocking.
But here's

→ View original post on X — @learnopencv,

29 April 2026
Vision Banana: Rethinking AI Model Generalization in Vision

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@learnopencv

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23 April 2026 20h14

Vision Banana: Rethinking How AI Models See and Generalize

In this episode of Artificial Intelligence: Papers and Concepts, we explore Vision Banana, a concept that challenges how vision models learn and generalize from visual data. Instead of focusing purely on performance… pic.twitter.com/vgp0LQbMFi
— Satya Mallick (@LearnOpenCV) 23 avril 2026

Vision Banana: Rethinking How AI Models See and Generalize In this episode of Artificial Intelligence: Papers and Concepts, we explore Vision Banana, a concept that challenges how vision models learn and generalize from visual data. Instead of focusing purely on performance

→ View original post on X — @learnopencv,

23 April 2026
Small Purpose-Built AI Models Better Than Largest Models

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@learnopencv

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23 April 2026 15h32

The biggest AI model is not always the best solution, especially for real world problems that are narrow and specific. Small, purpose-built models can run faster, cost less, and be deployed directly on devices, making them far more practical. The future of AI is about using the… pic.twitter.com/rO0niW1gB2
— Satya Mallick (@LearnOpenCV) 23 avril 2026

The biggest AI model is not always the best solution, especially for real world problems that are narrow and specific. Small, purpose-built models can run faster, cost less, and be deployed directly on devices, making them far more practical. The future of AI is about using the

→ View original post on X — @learnopencv,

23 April 2026
Position Encoding: How Transformers Understand Data Order

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@learnopencv

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22 April 2026 18h06

Position Encoding: How Transformers Understand Order in Data

In this episode of Artificial Intelligence: Papers and Concepts, we explore Position Encoding, a fundamental concept that enables transformer models to understand the order of information. Since transformers process… pic.twitter.com/FS8RcTE7Dt
— Satya Mallick (@LearnOpenCV) 22 avril 2026

Position Encoding: How Transformers Understand Order in Data In this episode of Artificial Intelligence: Papers and Concepts, we explore Position Encoding, a fundamental concept that enables transformer models to understand the order of information. Since transformers process

→ View original post on X — @learnopencv,

22 April 2026

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