detection

DPT for Object Detection

Here is our code for ImageNet classification. Please check our paper for detailed information.

Instructions

Preparations

First, install pytorch as for classification.

conda install pytorch==1.7.1 torchvision==0.8.2 cudatoolkit=10.1 -c pytorch
pip install timm==0.3.2

We develop our method under environment mmcv==1.2.7 and mmdet==2.8.0. We recommand you this document for detailed instructions.

Evaluation

To evaluate RetinaNet on COCO val2017 with 8 gpus run:

./dist_test.sh /path/to/config/file /path/to/checkpoint_file 8 --eval bbox

For example, to evaluate RetinaNet with DPT-Tiny:

./dist_test.sh configs/retinanet_dpt_t_fpn_1x_coco.py pretrained/detection/retinanet_dpt_t_1x.pth 8 --eval bbox

To evaluate Mask R-CNN on COCO val2017 with 8 gpus run:

./dist_test.sh /path/to/config/file /path/to/checkpoint_file 8 --eval bbox segm

For example, to evaluate Mask R-CNN with DPT-Tiny:

./dist_test.sh configs/mask_rcnn_dpt_t_fpn_1x_coco.py pretrained/detection/mrcnn_dpt_t_1x.pth 8 --eval bbox segm

Training

Train with certain config file:

dist_train.sh /path/to/config/file $NUM_GPUS

For example, to train DPT-Small + Mask R-CNN on COCO train2017 for 12 epochs with 8 gpus:

dist_train.sh configs/mask_rcnn_dpt_s_fpn_1x_coco.py 8

Results and Models

RetinaNet Results

Method	#Params (M)	Schedule	mAP	AP50	AP75	APs	APm	APl	Download
DPT-Tiny	24.9	1x	39.5	60.4	41.8	23.7	43.2	52.2	Google Drive
DPT-Tiny	24.9	MS+3x	41.2	62.0	44.0	25.7	44.6	53.9	Google Drive
DPT-Small	36.1	1x	42.5	63.6	45.3	26.2	45.7	56.9	Google Drive
DPT-Small	36.1	MS+3x	43.3	64.0	46.5	27.8	46.3	58.5	Google Drive
DPT-Medium	55.9	1x	43.3	64.6	45.9	27.2	46.7	58.6	Google Drive
DPT-Medium	55.9	MS+3x	43.7	64.6	46.4	27.2	47.0	58.4	Google Drive

Mask R-CNN Results

Method	#Params (M)	Schedule	box mAP	box AP50	box AP75	mask mAP	mask AP50	mask AP75	Download
DPT-Tiny	34.8	1x	40.2	62.8	43.8	37.7	59.8	40.4	Google Drive
DPT-Tiny	34.8	MS+3x	42.2	64.4	46.1	39.4	61.5	42.3	Google Drive
DPT-Small	46.1	1x	43.1	65.7	47.2	39.9	62.9	43.0	Google Drive
DPT-Small	46.1	MS+3x	44.4	66.5	48.9	41.0	63.6	44.2	Google Drive
DPT-Medium	65.8	1x	43.8	66.2	48.3	40.3	63.1	43.4	Google Drive
DPT-Medium	65.8	MS+3x	44.3	65.6	48.8	40.7	63.1	44.1	Google Drive

Other links

These models can also be obtained from BaiduNetdisk. Password for extraction is DPTs. Our result is pretrained on the ImageNet1k dataset. ImageNet1k-pretrained models can be found here.