Tag: #computer-vision

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• AI for Everyone, Part 5 — Colourising Photos With a 0.47M U-Net, and Why the Colours Came Out Washed Out

  The smallest model in this series — a 472K-parameter U-Net — restored colour to greyscale CIFAR-10 images. Shapes survived intact, but the colours came out noticeably washed out. That is not a capacity problem; it is a c

  2026-08-23 · 7 min read #ai#computer-vision#unet#colorization#pytorch

• Two Paths to 3D — Reconstruction (NeRF & Gaussian Splatting) and Generation (TRELLIS & Hunyuan3D)

  The phrase "make a 3D model" hides two completely different problems. Reconstruction brings a scene that actually exists back to life from a handful of photos, while generation conjures something that does not exist from

  2026-07-09 · 9 min read #3d#ai#gaussian-splatting#nerf#computer-vision

• Analyzing SOTA Segmentation and Detection Models — The Lineage of SAM, DETR, and YOLO

  A walk through the SOTA lineage of object detection and image segmentation. We trace detection from two-stage R-CNN to one-stage YOLO/SSD and transformer-based DETR, cover semantic, instance, and panoptic segmentation, a

  2026-06-30 · 20 min read #ai-papers#computer-vision#object-detection#segmentation#sam

• Analyzing SOTA 3D Vision — Monocular Depth and 3D Gaussian Splatting

  A survey of SOTA trends in 3D vision. We cover monocular depth estimation (relative and metric depth) and the Depth Anything family concept, an overview of stereo and MVS, and the evolution of real-time rendering from Ne

  2026-06-30 · 22 min read #ai-papers#3d-vision#monocular-depth#gaussian-splatting#nerf

• SOTA Real-Time Video Analysis — Tracking, Understanding, Efficient Inference

  A survey of SOTA trends in real-time video analysis. We cover video understanding tasks like action recognition, object tracking, and temporal segmentation, the SAM 2 family concept of video segmentation and tracking, tr

  2026-06-30 · 21 min read #ai-papers#video-understanding#object-tracking#sam2#action-recognition

• The Robots Eye — 3D Perception and SLAM

  How robots see the world. We walk through RGB-D, LiDAR, and stereo sensors, the SLAM pipeline, point clouds and voxels, 6D pose estimation, deep-learning perception, and finally NeRF and 3D Gaussian Splatting, with code

  2026-06-29 · 18 min read #ai-papers#robotics#slam#3d-perception#pointcloud

• Computer Vision Frameworks 2026 - OpenCV 4, MediaPipe, Detectron2, YOLO v11, MMDetection, SAM 2, Grounding DINO Deep Dive

  The 2026 computer vision stack is no longer about "touching pixels". OpenCV 4.10 has made ONNX inference table stakes, MediaPipe Studio reduces mobile real-time pipelines to one line, YOLO v11 bundles NAS, segmentation,

  2026-05-16 · 24 min read #computer-vision#opencv#mediapipe#detectron2#yolo

• The 2026 Vision Model Development & Fine-Tuning Guide — CNN, ViT, DETR, SAM 2, VLMs and a Real Decision Tree

  Vision model development in 2026 is no longer 'grab a ResNet and call it a day.' Between CNNs, ViTs, DETR variants, SAM 2, and VLMs like LLaVA, Qwen-VL, Gemini Vision, and Claude Vision, your choice for the same photo ca

  2026-05-14 · 20 min read #computer-vision#vision-model#cnn#vit#detr

• CNN Architecture Complete Guide: From LeNet to EfficientNet and Vision Transformers

  The complete guide to understanding the history and evolution of CNN architectures. Master every major model — LeNet, AlexNet, VGG, ResNet, DenseNet, EfficientNet, and Vision Transformer — through structural analysis and

  2026-03-17 · 21 min read #cnn#computer-vision#deep-learning#resnet#vgg

• Computer Vision Complete Guide: CNN, ViT, YOLO, and Stable Diffusion

  A comprehensive guide to computer vision covering CNN architectures (ResNet, EfficientNet), YOLO object detection, SAM segmentation, Vision Transformers (ViT, DINOv2), Stable Diffusion generative models, and production d

  2026-03-17 · 14 min read #computer-vision#cnn#yolo#vit#stable-diffusion

• The Complete torchvision Guide — From Image Classification to Object Detection and Segmentation

  From torchvision transforms v2 and pretrained models (ResNet to ViT) to datasets, Object Detection (Faster R-CNN, YOLO), Segmentation, and hands-on fine-tuning — master computer vision in practice with PyTorch.

  2026-03-02 · 8 min read #ai-platform#pytorch#torchvision#computer-vision#cnn

• The Complete Autonomous Driving & Robotics Tech Stack: From C++, ROS2, CUDA, TensorRT to VLM/VLA, Simulation, and Beyond

  A comprehensive guide to the core technology stack behind autonomous driving and robotics. Covering Modern C++, ROS/ROS2, CUDA parallel programming, TensorRT optimization, model compression (quantization/pruning), sensor

  2026-03-01 · 22 min read #autonomous-driving#robotics#ros2#cuda#tensorrt

• Mastering the Segment Anything Model: Paper Analysis and Practical Guide from SAM 1 to SAM 2 to SAM 3

  A comprehensive deep dive into Meta AI's Segment Anything Model (SAM) series. Covering SAM 1 (image promptable segmentation), SAM 2 (real-time video segmentation), and SAM 3 (concept-aware segmentation) — including archi

  2026-03-01 · 21 min read #sam#segment-anything#computer-vision#image-segmentation#video-segmentation

• ResNet Paper In-Depth Analysis: How Residual Connections Broke the Depth Barrier in Deep Learning

  An in-depth analysis of Microsoft Research ResNet paper. We cover the discovery of the degradation problem, the mathematical principles behind skip connections, bottleneck architecture, and the ImageNet ILSVRC 2015 winni

  2026-03-01 · 29 min read #resnet#residual-learning#cnn#computer-vision#image-classification

• Vision Transformer (ViT) Paper In-Depth Analysis: An Image is Worth 16x16 Words

  An in-depth analysis of Google ViT paper. We cover the innovative approach of converting images into patch sequences, the principles behind Patch Embedding and Position Embedding, the performance vs. data efficiency trad

  2026-03-01 · 30 min read #vit#vision-transformer#computer-vision#transformer#image-classification