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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
RepNeXt-M3 40.8 62.4 44.7 37.8 59.5 40.6 5.1ms M3 M3
RepNeXt-M4 42.9 64.4 47.2 39.1 61.7 41.7 6.6ms M4 M4
RepNeXt-M5 44.7 66.0 49.2 40.7 63.5 43.6 10.4ms M5 M5

Installation

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

Data preparation

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

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

Testing

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 RepNeXt-M3 on COCO 2017 on an 8-GPU machine,

./dist_test.sh configs/mask_rcnn_repnext_m3_fpn_1x_coco.py path/to/repnext_m3_coco.pth 8 --eval bbox segm

Training

Download ImageNet-1K pretrained weights into ./pretrain

We provide PyTorch distributed data parallel (DDP) training script dist_train.sh, for example, to train RepNeXt-M3 on an 8-GPU machine:

./dist_train.sh configs/mask_rcnn_renext_m3_fpn_1x_coco.py 8

Tips: specify configs and #GPUs!

Hacking issues incompatible with torch>=2.0

  1. AttributeError: 'MMDistributedDataParallel' object has no attribute '_use_replicated_tensor_module'

Solution: edit /home/someone/micromamba/envs/detection/lib/python3.8/site-packages/mmcv/parallel/distributed.py line 160 in _run_ddp_forward function.

# comment below two lines
# module_to_run = self._replicated_tensor_module if \
#     self._use_replicated_tensor_module else self.module
# replace with below line
module_to_run = self.module
  1. AttributeError: 'int' object has no attribute 'type'

Solution: edit /home/someone/micromamba/envs/detection/lib/python3.8/site-packages/mmcv/parallel/_functions.py line 75 in forward function.

# comment below line
# streams = [_get_stream(device) for device in target_gpus]
# replace with below line
streams = [_get_stream(torch.device("cuda", device)) for device in target_gpus]