online hard example mining

**OHEM** (Online Hard Example Mining) is a **training method that selects the hardest examples within each mini-batch for backpropagation** — performing a forward pass on all examples, ranking by loss, and backpropagating only through the top-K hardest examples. **How OHEM Works** - **Forward Pass**: Compute loss for all examples in the mini-batch. - **Rank**: Sort examples by loss (descending) — highest-loss examples are hardest. - **Select**: Keep only the top-K (or top ratio) of examples for backpropagation. - **Backward**: Compute gradients only for the selected hard examples. **Why It Matters** - **Object Detection**: OHEM was proposed for Fast R-CNN to handle the extreme foreground/background imbalance in region proposals. - **No Heuristics**: Unlike fixed sampling ratios, OHEM automatically selects the batch composition. - **Background Reduction**: In detection, 99%+ of proposals are background — OHEM ensures the model learns from the few hard examples. **OHEM** is **training only on the hardest cases per batch** — automatically focusing each gradient update on the most informative examples.