Tools for Improving Computer Vision Solutions

I built a set of command-line tools that let you generate, edit, and analyze images through Unix pipes - beautifully simple on Mac and Linux, and probably works on Windows too. These tools work perfectly with Google's brand new Gemini 2.5 Flash Image (nicely codenamed nano-banana). And at ~$0.039 per image through OpenRouter, you can actually afford to experiment and benchmark these models. Here's the simple case - generate a new image: 𝗴𝗿𝗮𝗳𝘁 -𝗽 "𝗔𝗻 𝗛𝗗 𝗽𝗵𝗼𝘁𝗼 𝗼𝗳 𝗮 𝗰𝘆𝗯𝗲𝗿𝗽𝘂𝗻𝗸 𝘀𝘁𝗿𝗲𝗲𝘁 𝗺𝗮𝗿𝗸𝗲𝘁 𝗮𝘁 𝗻𝗶𝗴𝗵𝘁" To make it more interesting, we can grab an image from the web and modify it: 𝗰𝘂𝗿𝗹 𝗵𝘁𝘁𝗽𝘀://𝗰𝗱𝗻.𝗻𝗮𝗶𝗱𝗮.𝗮𝗶/𝗺𝗶𝘀𝗰/𝘀𝗴𝟮𝟬𝟰𝟵.𝗽𝗻𝗴 | 𝗴𝗿𝗮𝗳𝘁 -𝗽 "𝗮𝗱𝗱 𝘀𝗼𝗺𝗲 𝗳𝗹𝘆𝗶𝗻𝗴 𝗱𝗿𝗼𝗻𝗲𝘀" That's a futuristic Singapore skyline, and now it has drones. Pipe it through glimpse to verify what changed, chain multiple edits, build entire workflows. Want to test if an AI model really understands photography styles? Run this: 𝗳𝗼𝗿 𝗱𝗲𝗰𝗮𝗱𝗲 𝗶𝗻 𝟭𝟵𝟱𝟬 𝟭𝟵𝟲𝟬 𝟭𝟵𝟳𝟬 𝟭𝟵𝟴𝟬 𝟭𝟵𝟵𝟬 𝟮𝟬𝟬𝟬; 𝗱𝗼  𝗴𝗿𝗮𝗳𝘁 -𝗽 "𝘀𝘁𝗿𝗲𝗲𝘁 𝘀𝗰𝗲𝗻𝗲, 𝗮𝘂𝘁𝗵𝗲𝗻𝘁𝗶𝗰 ${𝗱𝗲𝗰𝗮𝗱𝗲}𝘀 𝗽𝗵𝗼𝘁𝗼𝗴𝗿𝗮𝗽𝗵" -𝗼 - |   𝗴𝗹𝗶𝗺𝗽𝘀𝗲 -𝗺 <𝘀𝗼𝗺𝗲_𝗼𝘁𝗵𝗲𝗿_𝗺𝗼𝗱𝗲𝗹> -𝗽 "𝘄𝗵𝗮𝘁 𝗱𝗲𝗰𝗮𝗱𝗲 𝘄𝗮𝘀 𝘁𝗵𝗶𝘀 𝗽𝗵𝗼𝘁𝗼 𝘁𝗮𝗸𝗲𝗻?" 𝗱𝗼𝗻𝗲 You now have data on whether the model actually knows what makes a 1970s photo look like the 1970s. Run it 100 times with different temperatures, build a confusion matrix, find the edge cases where models hallucinate or ignore instructions. Configure glimpse to use high-end vision models like Gemini 2.5 Pro, GPT-5 or Claude 4 Sonnet to evaluate the outputs from smaller, cheaper, faster generation models - proper benchmarking without breaking the bank. For researchers evaluating image models, this beats clicking through web interfaces or writing complex evaluation scripts. Everything is scriptable, reproducible, and measurable. Export to CSV, track model performance over time, integrate into your CI/CD pipeline to catch regressions. The Unix philosophy wins big here: small tools that do one thing well, composed into powerful pipelines = rapid research & benchmarking. Code is on GitHub at u1i/graft if you want to try it yourself.

Computer Vision (CV) algorithms are the "eyes" of AI. They allow machines to not just capture pixels, but to understand 𝐨𝐛𝐣𝐞𝐜𝐭𝐬, 𝐩𝐚𝐭𝐭𝐞𝐫𝐧𝐬, 𝐚𝐧𝐝 𝐟𝐞𝐚𝐭𝐮𝐫𝐞𝐬. From autonomous driving to medical imaging, choosing the right algorithm is a balance of 𝐬𝐩𝐞𝐞𝐝, 𝐚𝐜𝐜𝐮𝐫𝐚𝐜𝐲, 𝐚𝐧𝐝 𝐡𝐚𝐫𝐝𝐰𝐚𝐫𝐞 constraints. 𝟏. 𝐎𝐁𝐉𝐄𝐂𝐓 𝐃𝐄𝐓𝐄𝐂𝐓𝐈𝐎𝐍 (𝐑𝐞𝐚𝐥-𝐓𝐢𝐦𝐞 𝐯𝐬. 𝐇𝐢𝐠𝐡 𝐏𝐫𝐞𝐜𝐢𝐬𝐢𝐨𝐧) 𝐘𝐎𝐋𝐎 (𝐘𝐨𝐮 𝐎𝐧𝐥𝐲 𝐋𝐨𝐨𝐤 𝐎𝐧𝐜𝐞): The industry standard for 𝐬𝐩𝐞𝐞𝐝. It processes the entire image in a single pass, making it ideal for real-time video feeds (e.g., security cameras, self-driving cars). 𝐑-𝐂𝐍𝐍 / 𝐅𝐚𝐬𝐭𝐞𝐫 𝐑-𝐂𝐍𝐍: Focuses on 𝐚𝐜𝐜𝐮𝐫𝐚𝐜𝐲. It uses region proposals to find objects, which is slower but much more precise for complex scenes. 𝟐. 𝐅𝐄𝐀𝐓𝐔𝐑𝐄 𝐌𝐀𝐓𝐂𝐇𝐈𝐍𝐆 & 𝐄𝐃𝐆𝐄 𝐃𝐄𝐓𝐄𝐂𝐓𝐈𝐎𝐍 Before deep learning, we relied on mathematical feature extractors. These are still vital for low-power devices: 𝐎𝐑𝐁 (𝐎𝐫𝐢𝐞𝐧𝐭𝐞𝐝 𝐅𝐀𝐒𝐓 𝐚𝐧𝐝 𝐑𝐨𝐭𝐚𝐭𝐞𝐝 𝐁𝐑𝐈𝐄𝐅): A fast, open-source alternative to SIFT/SURF. It identifies key points in an image to match them across different frames. 𝐂𝐚𝐧𝐧𝐲 𝐄𝐝𝐠𝐞 𝐃𝐞𝐭𝐞𝐜𝐭𝐨𝐫: A multi-stage algorithm used to detect a wide range of edges in images, providing the structural skeleton of an object. 𝟑. 𝐒𝐄𝐆𝐌𝐄𝐍𝐓𝐀𝐓𝐈𝐎𝐍 (𝐏𝐢𝐱𝐞𝐥-𝐋𝐞𝐯𝐞𝐥 𝐔𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝𝐢𝐧𝐠) 𝐒𝐞𝐦𝐚𝐧𝐭𝐢𝐜 𝐒𝐞𝐠𝐦𝐞𝐧𝐭𝐚𝐭𝐢𝐨𝐧: Labels every pixel in an image with a category (e.g., "Road," "Sky," "Pedestrian"). 𝐈𝐧𝐬𝐭𝐚𝐧𝐜𝐞 𝐒𝐞𝐠𝐦𝐞𝐧𝐭𝐚𝐭𝐢𝐨𝐧 (𝐞.𝐠., 𝐌𝐚𝐬𝐤 𝐑-𝐂𝐍𝐍): Goes a step further by distinguishing between individual objects of the same class (e.g., identifying Person 1 vs. Person 2). 𝟒. 𝐓𝐇𝐄 𝐍𝐄𝐖 𝐅𝐑𝐎𝐍𝐓𝐈𝐄𝐑: 𝐕𝐈𝐒𝐈𝐎𝐍 𝐓𝐑𝐀𝐍𝐒𝐅𝐎𝐑𝐌𝐄𝐑𝐒 (𝐕𝐢𝐓) 𝐕𝐢𝐬𝐢𝐨𝐧 𝐓𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦𝐞𝐫𝐬: Unlike traditional CNNs that look at local pixel neighborhoods, ViTs split images into patches and use 𝐒𝐞𝐥𝐟-𝐀𝐭𝐭𝐞𝐧𝐭𝐢𝐨𝐧 to capture global context. 𝐔𝐬𝐞 𝐂𝐚𝐬𝐞: Handling highly complex patterns where the relationship between distant parts of an image is crucial. 💡 𝐒𝐓𝐑𝐀𝐓𝐄𝐆𝐈𝐂 𝐓𝐑𝐀𝐃𝐄-𝐎𝐅𝐅𝐒 𝐋𝐢𝐦𝐢𝐭𝐞𝐝 𝐇𝐚𝐫𝐝𝐰𝐚𝐫𝐞? → Use 𝐎𝐑𝐁 or 𝐌𝐨𝐛𝐢𝐥𝐞𝐍𝐞𝐭 (Lightweight CNN). 𝐍𝐞𝐞𝐝 𝐌𝐢𝐥𝐢-𝐬𝐞𝐜𝐨𝐧𝐝 𝐋𝐚𝐭𝐞𝐧𝐜𝐲? → Use 𝐘𝐎𝐋𝐎. 𝐃𝐞𝐞𝐩 𝐂𝐨𝐧𝐭𝐞𝐱𝐭𝐮𝐚𝐥 𝐔𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝𝐢𝐧𝐠? → Use 𝐕𝐢𝐬𝐢𝐨𝐧 𝐓𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦𝐞𝐫𝐬. 🔥 𝐓𝐇𝐄 𝐁𝐎𝐓𝐓𝐎𝐌 𝐋𝐈𝐍𝐄: A great model is nothing without great data. In 2026, the focus has shifted from just "tuning algorithms" to 𝐝𝐚𝐭𝐚-𝐜𝐞𝐧𝐭𝐫𝐢𝐜 𝐀𝐈. Experimenting with data augmentation, annotation quality, and batch composition is often more effective than simply switching architectures. #𝐂𝐨𝐦𝐩𝐮𝐭𝐞𝐫𝐕𝐢𝐬𝐢𝐨𝐧 #𝐀𝐈 #𝐌𝐚𝐜𝐡𝐢𝐧𝐞𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 #𝐘𝐎𝐋𝐎 #𝐕𝐢𝐬𝐢𝐨𝐧𝐓𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦𝐞𝐫

I've started a series of short experiments using advanced Vision-Language Models (#VLM) to improve #robot #perception. In the first article, I showed how simple prompt engineering can steer Grounded SAM 2 to produce impressive detection and segmentation results. However, the major challenge remains: most #robotic systems, including mine, lack GPUs powerful enough to run these large models in real time. In my latest experiment, I tackled this issue by using Grounded SAM 2 to auto-label a dataset and then fine-tuning a compact #YOLO v8 model. The result? A small, efficient model that detects and segments my SHL-1 robot in real time on its onboard #NVIDIA #Jetson computer! If you're working in #robotics or #computervision and want to skip the tedious process of manually labeling datasets, check out my article (code included). I explain how I fine-tuned a YOLO model in just a couple of hours instead of days. Thanks to Roboflow and its amazing #opensource tools for making all of this more straightforward. #AI #MachineLearning #DeepLearning

🚀 Meta just dropped DINOv3, and it's a a big deal for computer vision AI For the first time ever, we have a self-supervised vision model that outperforms specialized solutions across multiple tasks - WITHOUT needing labeled data or fine-tuning. The numbers are staggering: ▪️ 7 billion parameters (7x larger than DINOv2) ▪️Trained on 1.7 billion images ▪️Zero human annotations required ▪️Single model beats task-specific solutions ▪️Real impact is already happening:  🌳 The World Resources Institute is using it for deforestation monitoring - reducing tree height measurement errors from 4.1m to 1.2m 🚀 NASA JPL is deploying it for Mars exploration robots 🔬 All with minimal compute requirements What makes this special? DINOv3 learns like humans do - by observing patterns, not by being told what to look for. One frozen backbone can handle object detection, segmentation, depth estimation, and classification simultaneously. No more training separate models for each task. This democratizes advanced computer vision. Startups, researchers, and enterprises can now deploy state-of-the-art vision AI without massive labeled datasets or computational resources. We're witnessing computer vision finally catching up to the versatility of large language models. The implications for robotics, autonomous systems, medical imaging, and environmental monitoring are profound. Key technical achievements: ▪️ First SSL vision model to outperform weakly-supervised methods (CLIP derivatives) on dense prediction tasks with frozen backbones ▪️Scaled to 7B parameters on 1.7B images without requiring any text captions or metadata ▪️Achieves SOTA on object detection and semantic segmentation without fine-tuning the backbone ▪️Single forward pass serves multiple downstream tasks simultaneously Architecture details: ▪️Vision Transformer variants (ViT-S/B/L/g) ▪️ConvNeXt models for edge deployment ▪️Produces dense, high-resolution features at pixel level ▪️Knowledge distillation into smaller models preserves performance Benchmark results: ▪️Outperforms SigLIP 2 and Perception Encoder on image classification ▪️Significantly widens performance gap on dense prediction vs DINOv2 ▪️Linear probing sufficient for robust dense predictions ▪️Generalizes across domains without task-specific training Why this matters?  Unlike CLIP-based models that require image-text pairs, DINOv3 learns purely from visual data through self-distillation. This eliminates dependency on noisy web captions and enables training on domains where text annotations don't exist. The frozen backbone approach means a single model checkpoint can be deployed for multiple applications without maintaining task-specific weights. 🤩 Can't wait to see this in ComfyUI! #ComputerVision #SSL #DeepLearning #Meta #DinoV3

𝐓𝐡𝐢𝐧𝐤 Ultralytics 𝐘𝐎𝐋𝐎𝐯11 𝐢𝐬 𝐠𝐫𝐞𝐚𝐭 𝐨𝐮𝐭 𝐨𝐟 𝐭𝐡𝐞 𝐛𝐨𝐱? 𝐖𝐚𝐢𝐭 𝐮𝐧𝐭𝐢𝐥 𝐲𝐨𝐮 𝐡𝐞𝐚𝐫 𝐡𝐨𝐰 NVIDIA 𝐓𝐀𝐎 𝐓𝐨𝐨𝐥𝐤𝐢𝐭 𝐢𝐧𝐬𝐩𝐢𝐫𝐞𝐝 𝐦𝐞 𝐭𝐨 𝐩𝐮𝐬𝐡 𝐢𝐭𝐬 𝐥𝐢𝐦𝐢𝐭𝐬 𝐰𝐢𝐭𝐡 𝐚𝐮𝐠𝐦𝐞𝐧𝐭𝐚𝐭𝐢𝐨𝐧 𝐚𝐧𝐝 𝐭𝐮𝐧𝐢𝐧𝐠! Excited to share my journey working with YOLOv11 for object detection! Here’s what I’ve been up to: 1) 𝐄𝐧𝐡𝐚𝐧𝐜𝐞𝐝 𝐀𝐮𝐠𝐦𝐞𝐧𝐭𝐚𝐭𝐢𝐨𝐧: Inspired by my experience with the NVIDIA TAO Toolkit, I explored how to layer additional custom augmentations after leveraging Roboflow. This approach helped diversify the training data, making the model more robust and adaptable. 3) 𝐓𝐮𝐧𝐢𝐧𝐠 𝐇𝐲𝐩𝐞𝐫𝐩𝐚𝐫𝐚𝐦𝐞𝐭𝐞𝐫𝐬: Drawing from best practices in the TAO Toolkit, I focused on hyperparameter optimization to fine-tune YOLOv11. Adjusting learning rates, experimenting with momentum, and exploring weight decay provided key insights and noticeable performance improvements. 3) 𝐌𝐨𝐝𝐞𝐥 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠 𝐢𝐧 Google 𝐂𝐨𝐥𝐥𝐚𝐛: Using the YOLOv11 framework, I set up a training pipeline directly in Google Collab. With custom hyperparameters such as learning rate, momentum, weight decay, etc. I fine-tuned the model for optimal performance. 𝐓𝐚𝐤𝐞𝐚𝐰𝐚𝐲𝐬: The TAO Toolkit’s approach to model training and augmentation inspired these strategies and reinforced the importance of a well-prepared pipeline. Combining tools and methodologies accelerates innovation and enhances results. 𝐍𝐞𝐱𝐭 𝐬𝐭𝐞𝐩𝐬: Continue refining the model and testing its real-world applications. Have you used the NVIDIA TAO Toolkit or experimented with advanced augmentation techniques and hyperparameter tuning? ♻️ Repost to your LinkedIn followers and follow Timothy Goebel for more actionable insights on AI and innovation. #YOLOv11 #ComputerVision #ObjectDetection #MachineLearning #AI #DeepLearning #NVIDIA #TAOToolkit #DataAugmentation #HyperparameterTuning

One missed detail has the power to disrupt manufacturing, compromise businesses, and even cost lives. AI is evolving at breakneck speed, but one thing has been a significant challenge for Computer Vision - detecting tiny objects in large-scale images.  Until now, the technology could miss essential small elements that could lead to: • a missed tumor during a medical scan • serious defects on a production line • security breaches due to lost details in surveillance footage. But, luckily, things have changed. My AI team and I explored the impact of YOLOv8 and SAHI (Slicing Aided Hyper Inference) and uncovered how these combined tools are revolutionizing object detection. Key insights from our research: ✅ 𝗘𝗻𝗵𝗮𝗻𝗰𝗲𝗱 𝗣𝗿𝗲𝗰𝗶𝘀𝗶𝗼𝗻 SAHI’s slicing technique ensures no detail is lost, boosting accuracy by up to 14.5% on complex datasets. ✅ 𝗥𝗲𝗮𝗹-𝗪𝗼𝗿𝗹𝗱 𝗜𝗺𝗽𝗮𝗰𝘁: From identifying tumors to improving security, this combination delivers actionable insights. ✅ 𝗘𝗹𝗲𝘃𝗮𝘁𝗲𝗱 𝗘𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝗰𝘆: YOLOv8 minimizes errors with features like Oriented Bounding Boxes (OBB), making it ideal for industries like security, agriculture, and more. Whether you're in healthcare, security, manufacturing, or agriculture, this innovative Computer Vision technology can improve your operations, reduce manual workload, and empower smarter decisions. Download our 𝗙𝗥𝗘𝗘 whitepaper and discover how these technologies can drive innovation in your business: https://bit.ly/48Tk9Qg #ComputerVision #YOLOv8 #SAHI #AI

Enhancing Transformer-Based Vision Models: Addressing Feature Map Anomalies Through Novel Optimization Strategies https://lnkd.in/ekB-i3Fn Vision Transformers (ViTs) have demonstrated superior performance across a wide range of computer vision tasks. However, structured noise artifacts in their feature maps hinder downstream applications such as segmentation and depth estimation. We propose two novel and lightweight optimisation techniques- Structured Token Augmentation (STA) and Adaptive Noise Filtering (ANF)- to improve interpretability and mitigate these artefacts. STA enhances token diversity through spatial perturbations during tokenisation, while ANF applies learnable inline denoising between transformer layers. These methods are architecture-agnostic and evaluated across standard benchmarks, including ImageNet, Ade20k, and NYUv2. Experimental results show consistent improvements in visual quality and task performance, highlighting the practical effectiveness of our approach. --- Newsletter https://lnkd.in/emCkRuA More story https://lnkd.in/enY7VpM LinkedIn https://lnkd.in/ehrfPYQ6 #AINewsClips #AI #ML #ArtificialIntelligence #MachineLearning #ComputerVision

Microsoft just rolled out Florence-2, and it's a bit of a game-changer in computer vision tasks. Imagine having one LLM model that handles what we usually tackle with multiple, resource-heavy deep learning models. Sounds cool, right? Here's what Florence-2 brings to the table: 1. Image-level Understanding: Think image classification, captioning, and even answering questions about visuals. 2. Pixel-level Precision: It's ace at segmentation and object detection, getting down to the nitty-gritty details. 3. Visual-Text Sync: Aligns text and images with a finesse we haven't seen before. But here's the kicker: You can fine-tune Florence-2 for your specific needs Computer Vision was always one of the trickier corners in AI – requiring lots of resources and expertise. With Florence-2 stepping onto the scene, I'm thinking we're about to see some big shifts. What do you think? Could this be the start of a new era in AI and computer vision? #AI #ComputerVision Link to paper: https://lnkd.in/e9FBQTAn