our dataset[^1] and obtain tight-fitting bounding boxes from the annotated segmentation masks. We evaluate models tested on both [MS COCO]{}and PASCAL, see Table \[tab:ap\_scores\]. We evaluate two different models. **DPMv5-P**: the latest implementation of [@felzenszwalb2010object] (release 5 [@voc-release5]) trained on PASCAL VOC 2012. **DPMv5-C**: the same implementation trained on COCO (5000 positive and 10000 negative images). We use the default parameter settings for training COCO models.

If we compare the average performance of DPMv5-P on PASCAL VOC and [MS COCO]{}, we find that average performance on [MS COCO]{}drops by nearly a [*factor of 2*]{}, suggesting that [MS COCO]{}does include more difficult (non-iconic) images of objects that are partially occluded, amid clutter, etc. We notice a similar drop in performance for the model trained on [MS COCO]{}(DPMv5-C).

The effect on detection performance of training on PASCAL VOC or [MS COCO]{}may be analyzed by comparing DPMv5-P and DPMv5-C. They use the same implementation with different sources of training data. Table \[tab:ap\_scores\] shows DPMv5-C still outperforms DPMv5-P in 6 out of 20 categories when testing on PASCAL VOC. In some categories (e.g., dog, cat, people), models trained on [MS COCO]{}perform worse, while on others (e.g., bus, tv, horse), models trained on our data 