Object Detection and Instance Segmentation

Detection and instance segmentation on MS COCO 2017 is implemented based on MMDetection.

Models

Model	$AP^b$	$AP_{50}^b$	$AP_{75}^b$	$AP^m$	$AP_{50}^m$	$AP_{75}^m$	Latency	Ckpt	Log
RepViT-M1.1	39.8	61.9	43.5	37.2	58.8	40.1	4.9ms	M1.1	M1.1
RepViT-M1.5	41.6	63.2	45.3	38.6	60.5	41.5	6.4ms	M1.5	M1.5
RepViT-M2.3	44.6	66.1	48.8	40.8	63.6	43.9	9.9ms	M2.3	M2.3

Install mmcv-full and MMDetection v2.28.2, Later versions should work as well. The easiest way is to install via MIM

pip install -U openmim
mim install mmcv-full==1.7.1
mim install mmdet==2.28.2

Prepare COCO 2017 dataset according to the instructions in MMDetection. The dataset should be organized as

detection
├── data
│   ├── coco
│   │   ├── annotations
│   │   ├── train2017
│   │   ├── val2017
│   │   ├── test2017

We provide a multi-GPU testing script, specify config file, checkpoint, and number of GPUs to use:

./dist_test.sh config_file path/to/checkpoint #GPUs --eval bbox segm

For example, to test RepViT-M1.1 on COCO 2017 on an 8-GPU machine,

./dist_test.sh configs/mask_rcnn_repvit_m1_1_fpn_1x_coco.py path/to/repvit_m1_1_coco.pth 8 --eval bbox segm

Download ImageNet-1K pretrained weights into ./pretrain

We provide PyTorch distributed data parallel (DDP) training script dist_train.sh, for example, to train RepViT-M1.1 on an 8-GPU machine:

./dist_train.sh configs/mask_rcnn_repvit_m1_1_fpn_1x_coco.py 8

Tips: specify configs and #GPUs!