#detection
6 articles
01
·CNNs & Image Recognition·★ MEMBER·PAPER·9 min read
Object Detection from Scratch (from YOLO to DETR)
A from-zero guide to object detection: the classic toolkit of two-stage detectors, anchors, and NMS — and how DETR reframed the whole problem to make all of it unnecessary. Primary source: the DETR paper.
02
·CNNs & Image Recognition·FREE·9 min read
The Autonomous Driving Perception Stack from Scratch — What Cameras and LiDAR Each Bring to the Table
How a self-driving car figures out what is around it, starting from sensor physics. We cover the complementary strengths of cameras and LiDAR, the bird's-eye-view grid where all sensors meet, occupancy grids grown by Bayesian updates, and the tug-of-war between safety requirements and compute budgets — no prior knowledge assumed.
03
·CNNs & Image Recognition·FREE·9 min read
The Autonomous Driving Perception Stack from Scratch — What Cameras and LiDAR Each Bring to the Table
How a self-driving car figures out what is around it, starting from sensor physics. We cover the complementary strengths of cameras and LiDAR, the bird's-eye-view grid where all sensors meet, occupancy grids grown by Bayesian updates, and the tug-of-war between safety requirements and compute budgets — no prior knowledge assumed.
04
·CNNs & Image Recognition·★ MEMBER·11 min read
Anomaly Detection from Scratch — Learning From Normal Alone
Why an AI can learn from good units alone on a factory floor where defect samples barely exist. Two rulers — reconstruction error and density estimation — built up from metaphor to equations, all the way to the part that decides everything in practice: choosing the threshold.
05
·VLMs & Multimodal·★ MEMBER·PAPER·8 min read
BEV Representations From Scratch — Fusing Multiple Cameras Into One Top-Down Map
How a self-driving car turns six camera feeds into a single top-down map. Starting from perspective projection, we build up to the two big design philosophies: LSS, which pushes features into 3D via a predicted depth distribution, and Transformer-style methods like BEVFormer that pull information with BEV queries.
06
·VLMs & Multimodal·★ MEMBER·PAPER·8 min read
BEV Representations From Scratch — Fusing Multiple Cameras Into One Top-Down Map
How a self-driving car turns six camera feeds into a single top-down map. Starting from perspective projection, we build up to the two big design philosophies: LSS, which pushes features into 3D via a predicted depth distribution, and Transformer-style methods like BEVFormer that pull information with BEV queries.