For the ImageNet Localization (LOC) task, a per-class regression (PCR) approach is adopted, wherein bounding box regressors are learned individually for each of the 1,000 object categories. The proposal network is modified from category-agnostic proposals into a per-class RPN that ends with two sibling $1\times 1$ convolutional layers: a 1,000-dimensional binary logistic classification layer predicting class presence, and a $1000 \times 4$-dimensional regression layer providing coordinate offsets relative to translation-invariant anchor boxes.

While Fast R-CNN utilizes image-centric training, ImageNet images typically contain a single dominant object. Consequently, region proposals exhibit extensive spatial overlap and nearly identical RoI-pooled features, resulting in low sample variance that hinders stochastic training. To remedy this, the framework switches to an RoI-centric R-CNN pipeline. The top 200 proposals predicted by the per-class RPN for ground-truth classes are cropped, warped to $224\times 224$ pixels, and trained in mini-batches of 256 RoIs.

During inference, the per-class RPN extracts the top 200 proposals for each predicted class label, which the R-CNN then scores and regresses. A single ResNet-101 model achieves a 10.6% top-5 localization error on ImageNet validation, and an ensemble achieves 9.0% top-5 localization error on the test set, claiming first place in the ILSVRC 2015 localization challenge.