CNNs, object detection, and computer vision.
I answer the questions I get most about deep learning in computer vision — with verified numbers, one table, and a clear recommendation.
Newer isn't always better. I've watched teams burn months on transformer detectors when a YOLO baseline would have shipped in a week. Here's why the c...
Most computer vision projects fail from too many options, not too few. Here's a practical path: pick one detector, one dataset, one metric, and get it...
Benchmarks are fine. But the computer vision work I care about is the stuff that runs on a clinic's laptop or in a car at 65 FPS. Here's what I've lea...
Stop tutorial-hopping. Pick one detection architecture, learn it deeply, and ignore the rest until you can explain its loss function. Here's how.
Stop chasing benchmarks. Match the model to your real constraint—latency, dataset size, or hardware—or you'll ship a demo that dies in production.
YOLO wins on speed, Faster R-CNN on accuracy—but for many real-world teams, the best move is neither. Here's how to pick a detector when your data is ...
From autonomous driving to medical imaging, we bust common computer vision myths and share practical advice for real applications.
For most computer vision projects, your backbone choice matters more than tuning. Here's a blunt comparison of ResNet vs. EfficientNet across speed, a...
Before you dive into YOLO or Faster R-CNN, you need to understand evaluation metrics like mAP and IoU. Here's how to get them right.
Training a deep CNN from scratch on custom data is rarely the right move. We explain why pretrained backbones and transfer learning win, and when it a...
Object detection has two big families: two-stage accuracy hogs like Faster R-CNN and single-stage speed demons like YOLO. Here's how to pick for your ...
YOLO vs. Faster R-CNN vs. EfficientDet: I break down the real trade-offs and give you a clear pick for production.