OrienMask
This repository implements the framework OrienMask for real-time instance segmentation.
It achieves 34.8 mask AP on COCO test-dev at the speed of 42.7 FPS evaluated with a single RTX 2080Ti. (log)
Paper: Real-time Instance Segmentation with Discriminative Orientation Maps
Installation
Please see INSTALL.md to prepare the environment and dataset.
Usage
Place the pre-trained backbone (link) and trained model (link) as follows for convenience (otherwise update the corresponding path in configurations):
├── checkpoints
│ ├── pretrained
│ │ ├──pretrained_darknet53.pth
│ ├── OrienMaskAnchor4FPNPlus
│ │ ├──orienmask_yolo.pth
train
Three items should be noticed when deploying different number of GPUs: n_gpu, batch_size, accumulate. Keep in mind that the approximate batch size equals to n_gpu * batch_size * accumulate.
# multi-gpu train (n_gpu=2, batch_size=8, accumulate=1)
# if necessary, set MASTER_PORT to avoid port conflict
# if permission error, run `chmod +x dist_train.sh`
CUDA_VISIBLE_DEVICES=0,1 ./dist_train.sh \
-c orienmask_yolo_coco_544_anchor4_fpn_plus
# single-gpu train (n_gpu=1, batch_size=8, accumulate=2)
CUDA_VISIBLE_DEVICES=0 ./dist_train.sh \
-c orienmask_yolo_coco_544_anchor4_fpn_plus
# or
CUDA_VISIBLE_DEVICES=0 python train.py \
-c orienmask_yolo_coco_544_anchor4_fpn_plus
test
Run the following command to obtain AP and AR metrics on val2017 split:
CUDA_VISIBLE_DEVICES=0 python test.py \
-c orienmask_yolo_coco_544_anchor4_fpn_plus_test \
-w checkpoints/OrienMaskAnchor4FPNPlus/orienmask_yolo.pth
infer
Please run python infer.py -h for more usages.
# infer on an image and save the visualized result
CUDA_VISIBLE_DEVICES=0 python infer.py \
-c orienmask_yolo_coco_544_anchor4_fpn_plus_infer \
-w checkpoints/OrienMaskAnchor4FPNPlus/orienmask_yolo.pth \
-i assets/000000163126.jpg -v -o outputs
# infer on a list of images and save the visualized results
CUDA_VISIBLE_DEVICES=0 python infer.py \
-c orienmask_yolo_coco_544_anchor4_fpn_plus_infer \
-w checkpoints/OrienMaskAnchor4FPNPlus/orienmask_yolo.pth \
-d coco/test2017 -l assets/test_dev_selected.txt -v -o outputs
logs
We provide two types of logs for monitoring the training process. The first is updated on the terminal which is also stored in a train.log file in the checkpoint directory. The other is the tensorboard whose statistics are kept in the checkpoint directory.
Citation
@article{du2021realtime,
title={Real-time Instance Segmentation with Discriminative Orientation Maps},
author={Du, Wentao and Xiang, Zhiyu and Chen, Shuya and Qiao, Chengyu and Chen, Yiman and Bai, Tingming},
journal={arXiv preprint arXiv:2106.12204},
year={2021}
}
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