Paper: ShipYOLO: An Enhanced Model for Ship Detection
DOI: https://doi.org/10.1155/2021/1060182
Xu Han, "ShipYOLO: An Enhanced Model for Ship Detection," Journal of Advanced Transportation, vol. 2021, Article ID 1060182, 11 pages, 2021. https://doi.org/10.1155/2021/1060182.
@article{
author = {Xu Han and Lining Zhao and Yue Ning and Jingfeng Hu},
title = {{ShipYOLO}: An Enhanced Model for Ship Detection},
journal = {Journal of Advanced Transportation},
month = {jun},
year = {2021},
pages = {1--11}
}completed
ongoing
Inference in NVIDIA GeForce RTX 3060 Laptop GPU.
WSODD
DownLoad Link:https://github.com/sunjiaen/WSODD
Train list and Test list for this paper:train.txt,test.txt
The number of images in "datasets/WSODD" is only to give an example.
| Model | Size | $mAP_{coco}$ | $mAP_{coco}@50$ | $mAP_{coco}@75$ | FPS | Config | Datasets | Download |
|---|---|---|---|---|---|---|---|---|
| YOLOv4 | 512 | 0.251 | 0.582 | 0.173 | 34.50 | mmdet | WSODD | ------ |
| ShipYOLO | 512 | 0.296 | 0.615 | 0.237 | 44.96 | mmdet | WSODD | ------ |
| ShipYOLOV2 | 512 | 0.296 | 0.617 | 0.241 | 44.20 | mmdet | WSODD | ------ |
| - | 512 | 0.423 | 0.780 | 0.391 | 44.20 | mosaic+aug | WSODD | ------ |
pip install -r requirements.txt
bash run_train.sh
python re_pth.pyimport torch
from models.shipyolo.shipyolo import ShipYOLOv2
from utils.general import non_max_suppression
'''
Train deploy=False
Test deploy=True
'''
x = torch.rand(1, 3, 288, 512).cuda()
model = ShipYOLOv2(num_classes=14, deploy=True).cuda()
output = model(x) # without nms
output = non_max_suppression(output[0], conf_thres=0.001, iou_thres=0.5)The old version of the code(ShipYOLO) is in "project/ShipYOLO".
We also built the code(ShipYOLO-mmdet) based on mmdetection which is in "project/ShipYOLO-mmdet".