# Object Detection with PE ## Getting started Please refer to [INSTALL.md](INSTALL.md) for installation and dataset preparation instructions. ## Results and Fine-tuned Models ### LVIS
detector vision encoder box
AP		 mask
AP		 download
Mask R-CNN PE core G 51.9 47.9 model
Mask R-CNN PE spatial G 54.2 49.3 model
### COCO
detector vision encoder box
AP		 mask
AP		 download
Mask R-CNN PE core G 57.0 49.8 model
Mask R-CNN PE spatial G 57.8 50.3 model
### Training By default, we use 64 GPUs in slurm training, for example ``` sbatch scripts/coco/train_mask_rcnn_PEspatial_G_coco36ep.sh ``` ### Evaluation Evaluation is running locally ``` bash scripts/evaluate_local.sh --config-file projects/ViTDet/configs/COCO/mask_rcnn_PEspatial_G_coco36ep.py train.output_dir="/path/to/output_dir" train.init_checkpoint="/path/to/mask_rcnn_PEspatial_G_coco36ep.pth" ``` ## SOTA COCO Object Detection
detector vision encoder box
AP		 box(TTA)
AP		 download
DETA PE spatial G 65.2 66.0 model
More details are in [DETA_pe](DETA_pe) ## Acknowledgment This code is built using [detectron2](https://github.com/facebookresearch/detectron2) and [DETA](https://github.com/jozhang97/DETA).