6 articles in this category
Stop tutorial-hopping. Pick one detection architecture, learn it deeply, and ignore the rest until you can explain its loss function. Here's how.
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.
Forget training from scratch. I explain why pretrained models are the only sane choice for most computer vision tutorials, and how to adapt them witho...
Before you touch that optimizer, train a SimCLR model. Self-supervised pretraining beats hand-crafted features and even supervised baselines. Here's h...
Stop benchmarking on COCO alone. Here's why your detector's real-world behavior depends on the benchmark you choose, and how to pick one that matches ...