| # Rethinking "Batch" in BatchNorm | |
| We provide configs that reproduce detection experiments in the paper [Rethinking "Batch" in BatchNorm](https://arxiv.org/abs/2105.07576). | |
| All configs can be trained with: | |
| ``` | |
| ../../tools/lazyconfig_train_net.py --config-file configs/X.py --num-gpus 8 | |
| ``` | |
| ## Mask R-CNN | |
| * `mask_rcnn_BNhead.py`, `mask_rcnn_BNhead_batch_stats.py`: | |
| Mask R-CNN with BatchNorm in the head. See Table 3 in the paper. | |
| * `mask_rcnn_BNhead_shuffle.py`: Mask R-CNN with cross-GPU shuffling of head inputs. | |
| See Figure 9 and Table 6 in the paper. | |
| * `mask_rcnn_SyncBNhead.py`: Mask R-CNN with cross-GPU SyncBatchNorm in the head. | |
| It matches Table 6 in the paper. | |
| ## RetinaNet | |
| * `retinanet_SyncBNhead.py`: RetinaNet with SyncBN in head, a straightforward implementation | |
| which matches row 3 of Table 5. | |
| * `retinanet_SyncBNhead_SharedTraining.py`: RetinaNet with SyncBN in head, normalizing | |
| all 5 feature levels together. Match row 1 of Table 5. | |
| The script `retinanet-eval-domain-specific.py` evaluates a checkpoint after recomputing | |
| domain-specific statistics. Running it with | |
| ``` | |
| ./retinanet-eval-domain-specific.py checkpoint.pth | |
| ``` | |
| on a model produced by the above two configs, can produce results that match row 4 and | |
| row 2 of Table 5. | |