Computer Vision Algorithms

Just read a groundbreaking paper on image retrieval model training that every computer vision practitioner should know about! "All You Need to Know About Training Image Retrieval Models" provides comprehensive insights into optimizing image retrieval systems - the backbone of visual search engines and content-based recommendations we use daily. The researchers conducted tens of thousands of training runs to analyze how various factors impact retrieval accuracy across multiple datasets (Cars196, CUB-200-2011, iNaturalist 2018, and Stanford Online Products). Key technical findings: - Model architecture: DINO-v2's CLS features outperform other architectures - Optimization: Adam optimizer with 1e-6 learning rate yields best results when fine-tuning all layers - Loss functions: Two distinct categories perform differently based on resources:  -- High-resource settings: Contrastive losses (ThresholdConsistentMargin, Multi-Similarity) with online miners excel with larger batch sizes (256+)  -- Resource-constrained: Classification losses (CosFace, ArcFace) perform better with smaller batches - Batch composition: For contrastive losses, 2-4 images per class works best; for classification losses, 1 image per class is optimal - Learning rate tuning: Critical to set separate learning rates for model (1e-6) and classifier (around 1.0) - using the same rate for both can cause 10%+ accuracy drops - Feature dimensionality: Direct use of CLS token (768-dimensional for DINO-v2-base) achieves optimal results - Dataset strategy: All metric learning losses are robust to annotation errors, suggesting resources are better spent collecting more data than ensuring perfect labeling The paper provides practical guidance for balancing accuracy, computational resources, and data annotation strategies in image retrieval systems. Kudos to the researchers from Polytechnic of Turin and Setta.dev for this valuable contribution to the field!

Real-time human pose estimation just got significantly more deployable. The new YOLO26-Pose family from Ultralytics predicts the full human skeleton — 17 COCO keypoints across shoulders, elbows, wrists, hips, knees, and ankles — in a single forward pass. The smallest variant, YOLO26n-pose, runs in approximately 1.8 ms on a T4 GPU. In our latest LearnOpenCV tutorial, Dr. Satya Mallick (CEO, OpenCV.org) and the team break down what's actually new under the hood: → RLE (Residual Log-Likelihood Estimation) for sharper keypoint localization → NMS-free inference for predictable, deterministic latency → MuSGD optimizer (SGD + Muon hybrid) for more stable training dynamics We walk through the architecture, the code, raw keypoint outputs, and demos across yoga, karate, dance, gym workouts, parkour, and multi-person tracking — covering the realistic deployment surface for fitness platforms, sports analytics, gesture control, rehabilitation tech, and workplace safety monitoring. If you're building anything that depends on understanding human movement, this is a meaningful step forward in the speed/accuracy tradeoff. 📖 Full tutorial: https://vist.ly/428rn 🧠 Deep-dive on the NMS-free architecture: https://vist.ly/428rs #ComputerVision #DeepLearning #YOLO26 #PoseEstimation #ArtificialIntelligence #MachineLearning #OpenCV #AIEngineering #EdgeAI #MLOps #AIResearch #PyTorch #Ultralytics #HumanPoseEstimation #TechLeadership #AppliedAI

Vision language models (VLMs) have become very impressive in processing scientific imagery. It is opening a new avenue for how R&D teams can work with microscopy data. Back in graduate school, a lot of my TEM and SEM analysis was done manually. Counting particles, measuring fiber diameters, and eyeballing whether a crystalline pattern was real or an imaging artifact. Tools like ImageJ helped with the mechanics, but the workflow was still far from autonomous. Fast forward to today, machine learning and computer vision have become ubiquitous in scientific image analysis. The newer addition is VLMs, which have emerged as a strong and remarkably versatile contender (https://lnkd.in/ea7NRN-x). A new paper in npj Computational Materials presents a systematic evaluation of popular VLMs on microscopy images, spanning standard classification, segmentation, and counting tasks, plus the VLM-specific visual question answering. Here is how the models fared across the four tasks: 🔹Classification: Gemini-2.5 Pro and ChatGPT-5 classified 10 types of SEM images (fibers, particles, nanowires, MEMS) at 77% and 61% zero-shot. Their written rationales were often reasonable even when the classification was wrong. 🔹Segmentation: SAM-2, Meta's general-purpose segmentation model, still produced the most reliable masks across the microscopy datasets. Light parameter tuning closed much of the gap to specialized, domain-trained pipelines. 🔹Counting: SAM-2 with lightweight post-processing was the most dependable across datasets, handling both isolated and densely packed objects. Interestingly, Llama-3.2 Vision emerged as a competitive alternative. 🔹Visual question answering: Arguably the most interesting and highest-potential task. Models handle scale bars, morphology, and process stages. Even starting with an incorrect answer, conversational steering by a domain expert is often effective. A couple of things worth noting. These models operate in both pure-vision and code-based modes (writing Python to run image analysis tools), each with distinct strengths and failure modes. And the models studied here are from mid-2025 or earlier; today's frontier VLMs are meaningfully more capable. For R&D teams working with microscopy, this is a good moment to take VLMs seriously, either directly for first-pass analysis or for generating labeled data to train domain-specialized models. 📄 Vision Language Models for Scientific Image Analysis: An Evaluation Highlighting Opportunities and Challenges, npj Computational Materials, April 21, 2026 🔗 https://lnkd.in/ejZ3V4He

YOLO (You Only Look Once) revolutionized object detection by solving a fundamental problem: how to detect objects in real-time with just one forward pass through a neural network. Here is how it works in simple terms: Instead of scanning an image multiple times like traditional methods, YOLO divides the entire image into a grid (typically 4x4 or larger). Each grid cell becomes responsible for predicting whether it contains an object and what that object is. For every grid cell, the algorithm predicts three key things: 1. Objectness confidence - how likely is there an object here? 2. Class probability - what type of object is it? 3. Bounding box parameters - where exactly is the object located? The genius is in the "only look once" approach. Traditional object detection methods would run multiple scans across different regions of an image. YOLO does everything in a single pass, making it incredibly fast for real-time applications. The backbone is typically a CNN that processes the entire image simultaneously. The final confidence score combines the objectness probability with the intersection-over-union (IoU) ratio, giving you both detection accuracy and precise localization. Of course, vanilla YOLO has limitations - it struggles with small objects, crowded scenes, and unusual aspect ratios. But its speed and simplicity made it a game-changer for computer vision applications. If you are just getting started with object detection, I recently created an introductory lecture breaking down YOLO for total beginners on Vizuara's YouTube channel: https://lnkd.in/gwEEzqiT What is your experience with real-time object detection? Have you implemented YOLO in any projects?

Deci's YOLO-NAS architecture provides today's state of the art in Machine Vision, specifically the key task of Object Detection. Harpreet Sahota joins us from Deci today to detail YOLO-NAS as well as where Computer Vision is going next. Harpreet: • Leads the deep learning developer community at Deci AI, an Israeli startup that has raised over $55m in venture capital and that recently open-sourced the YOLO-NAS deep learning model architecture. • Through prolific data science content creation, including The Artists of Data Science podcast and his LinkedIn live streams, Harpreet has amassed a social-media following in excess of 70,000 followers. • Previously worked as a lead data scientist and as a biostatistician. • Holds a master’s in mathematics and statistics from Illinois State University. Today’s episode will likely appeal most to technical practitioners like data scientists, but we did our best to break down technical concepts so that anyone who’d like to understand the latest in machine vision can follow along. In the episode, Harpreet details: • What exactly object detection is. • How object detection models are evaluated. • How machine vision models have evolved to excel at object detection, with an emphasis on the modern deep learning approaches. • How a “neural architecture search” algorithm enabled Deci to develop YOLO-NAS, an optimal object detection model architecture. • The technical approaches that will enable large architectures like YOLO-NAS to be compute-efficient enough to run on edge devices. • His “top-down” approach to learning deep learning, including his recommended learning path. Many thanks to Amazon Web Services (AWS), WithFeeling.AI and Modelbit for supporting this episode of SuperDataScience, enabling the show to be freely available on all major podcasting platforms and on YouTube (see comments for details). #superdatascience #deeplearning #machinevision #machinelearning #ai

If you are starting in Computer Vision, don't begin with the latest model. Start with why each model was introduced. Here's how image understanding evolved over the years: ➡️ LeNet (1998) The first successful CNN, designed for handwritten digit recognition. It showed that neural networks could learn visual features automatically. Paper: https://lnkd.in/dY37V8XA ➡️ AlexNet (2012) A breakthrough on ImageNet that proved deep learning could outperform handcrafted features. It also popularized GPU training. Paper: https://lnkd.in/dRf-_u_K ➡️ VGG (2014) Showed that deeper networks could learn richer visual representations using a simple architecture. Paper: https://lnkd.in/d5cjvVAt ➡️ GoogLeNet / Inception (2014) Introduced multi-scale feature extraction while keeping computation efficient. Paper: https://lnkd.in/d5mME3JK ➡️ ResNet (2015) Solved the vanishing gradient problem using residual connections, making very deep networks practical. Paper: https://lnkd.in/d_mFv5uh ➡️ DenseNet & EfficientNet Focused on better feature reuse and smarter model scaling. DenseNet: https://lnkd.in/dSKNpfhu EfficientNet: https://lnkd.in/dcbXMrXe ➡️ Vision Transformers (ViT) & Swin Transformer Brought self-attention into vision, enabling better global context and becoming the foundation of many modern vision models. ViT: https://lnkd.in/d2X82aR5 Swin: https://lnkd.in/dqg6TPYA Every architecture wasn't just "better"; it solved a limitation of the previous one. Next post: How Object Detection evolved from R-CNN to DETR. Follow me for more AI & Robotics content Shivani Khandelwal. #AI #ComputerVision #DeepLearning #MachineLearning #BitsoftheDayWithShivani

🚨 AI for Home Security: Package Theft Detection with YOLOv11 Pose Estimation & Object Detection 🚨 Porch piracy is becoming a significant issue as online shopping continues to grow. Instead of just relying on motion sensors, I’m working on a multi-model AI pipeline that leverages computer vision to detect both packages and suspicious human behavior in real-time. 🧠 Core Architecture: YOLOv11 Object Detection → Trained to detect parcels/packages left at the doorstep. YOLOv11 Pose Estimation → Fine-tuned to analyze body keypoints and classify suspicious vs. thiefting behavior. Multi-Stream Fusion → Outputs from both models are combined to infer higher-level events. ⚙️ Pipeline Implementation: Framework: Ultralytics YOLOv11 Backend: Python + OpenCV for real-time video processing Input: Doorbell or CCTV camera footage (.mp4 or live RTSP streams) Output: Annotated video with bounding boxes, keypoints, and event labels Next Step: Adding alerting mechanisms (SMS, email, or smart-home integration) when theft is suspected 📊 Technical Challenges & Learnings: Designing a robust annotation strategy for “suspicious” & "thiefting" actions, since pose-based datasets are scarce. Balancing false positives (normal neighbor passing by) vs. false negatives (actual theft). Optimizing inference for edge devices (Raspberry Pi / NVIDIA Jetson Nano) to run real-time detection on doorbell cameras. 💡 This project highlights how hybrid vision systems (detection + pose estimation) can provide context-aware AI for real-world security applications. I believe these multi-model approaches are a big step toward smarter and safer homes. #AI #ComputerVision #DeepLearning #YOLOv11 #PoseEstimation #ObjectDetection #EdgeAI #SmartHome #SecurityTech #MLOps #AI, #ArtificialIntelligence, #ComputerVision, #DeepLearning, #YOLOv11, #ObjectDetection, #PoseEstimation, #VideoAnalytics, #SmartHome, #HomeSecurity, #EdgeAI, #MachineLearning, #MLOps, #AIForGood, #SecurityTech, #SurveillanceAI, #RealTimeDetection, #VideoProcessing, #Automation, #TechInnovation, #PythonAI, #Ultralytics, #AIApplications, #AIResearch, #SmartDevices

Computer vision isn't just for photo filters anymore. It's preventing accidents in real-time. I'm fascinated by this demonstration of a predictive AI safety system. It's a masterclass in how multiple computer vision tasks can work together to create something incredibly powerful. Here's the breakdown of the tech in action: ► Detection & Classification: It accurately identifies cars, buses, and even pedestrians. ► Tracking & Speed Analysis: It follows objects frame-by-frame, continuously calculating their speed. ► Collision Prediction: The system uses speed and trajectory data to calculate Time-to-Collision (TTC) and proximity warnings. The "DANGER ALERT" isn't just a guess; it's a data-driven prediction. The fact that this works seamlessly from day to night is a huge testament to the sophistication of the algorithms. This is the kind of technology that will redefine what's possible for intelligent transportation systems and vehicle safety. Where else could this predictive capability be a game-changer? #deeplearning #python #opencv #ai #saftey #tracking #trafficanalysis

The ORB algorithm is an impressive feat in computer vision that combines two powerful techniques: Oriented FAST (Features from Accelerated Segment Test) and Rotated BRIEF (Binary Robust Independent Elementary Features). It excels at efficiently detecting keypoints within images while generating highly descriptive feature vectors. By utilizing a variant of the popular FAST corner detection method, ORB can swiftly identify points of interest with high repeatability. These keypoints are then described using binary descriptors generated by the BRIEF algorithm, which captures distinctive local image information. One notable advantage of ORB lies in its ability to compute orientations for these detected features, making it robust against changes in viewpoint or rotation. This property enables accurate matching across different perspectives or even when dealing with partially occluded objects. Moreover, due to its efficient implementation leveraging integral images and Hamming distance calculations on binary strings, ORB exhibits remarkable speed compared to other keypoint-based algorithms without compromising accuracy significantly. In summary, thanks to its blend of rapidity and reliability through oriented detection coupled with rotated descriptor generation technique such as BRIEF encoding scheme -ORBIT stands out as an excellent choice for various applications requiring real-time performance. #computervision #machinelearning

Vision transformers have enabled a new level of computer vision capabilities using larger models. These models can even provide some interpretability through their attention maps. While this has worked well for models like DINO, the attention maps are less clear for newer transformers like DINOv2, DeiT-III, and OpenCLIP. Timothee Darce et al. performed some experiments to understand where these noisy artifacts are coming from and how to resolve them. They showed that this problem is more prevalent for large models, and they appear during training where patch information is redundant, i.e., the patch is similar to surrounding patches. These artifact tokens end up holding global information about the image. They resolved this problem by creating register tokens and adding them to the patch embedding layer. Adding even a single register token greatly decreased the artifacts in the attention map while having little effect on model accuracy. This is particularly beneficial for object discovery methods that use the attention map. https://lnkd.in/epXRURZ4 For more info on how you can bring the latest research models into action on your data, sign up for my Computer Vision Insights newsletter: https://lnkd.in/g9bSuQDP #MachineLearning #DeepLearning #ComputerVision