https://github.com/bestsongc/yolov5-odconvnext
YOLOv5-ODConvNeXt is an improved version of YOLOv5 for ship detection on drone-captured images.
Science Score: 10.0%
This score indicates how likely this project is to be science-related based on various indicators:
-
○CITATION.cff file
-
○codemeta.json file
-
○.zenodo.json file
-
○DOI references
-
✓Academic publication links
Links to: sciencedirect.com -
○Academic email domains
-
○Institutional organization owner
-
○JOSS paper metadata
-
○Scientific vocabulary similarity
Low similarity (7.6%) to scientific vocabulary
Last synced: 10 months ago
·
JSON representation
Repository
YOLOv5-ODConvNeXt is an improved version of YOLOv5 for ship detection on drone-captured images.
Statistics
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
- Releases: 0
Fork of chengshuxiao/YOLOv5-ODConvNeXt
Created almost 3 years ago
· Last pushed almost 3 years ago
https://github.com/Bestsongc/YOLOv5-ODConvNeXt/blob/main/
# YOLOv5-ODConvNeXt This repo is the implementation for [Deep learning based efficient ship detection from drone-captured images for maritime surveillance - ScienceDirect](https://www.sciencedirect.com/science/article/abs/pii/S0029801823018243). YOLOv5-ODConvNeXt is an improved version of YOLOv5s for ship detection on drone-captured images.## Install ```bash git clone https://github.com/chengshuxiao/YOLOv5-ODConvNeXt.git # clone cd YOLOv5-ODConvNeXt pip install -r requirements.txt # install ``` ## Inference with detect.py A trained YOLOv5-ODConvNeXt model is provided in `checkpoints/yolov5-odconvnext.pt`the detection results are saved in `runs/detect` ```bash python detect.py --weights checkpoints/yolov5-odconvnext.pt --source data/images --line-thickness 2 ``` Two demo outputs are shown below.
![]()
## Train The configuration of our model is in `models/yolov5-odconvnext.yaml` ```bash python train.py --data data/MyShip3200.yaml --cfg models/yolov5-odconvnext.yaml --hyp data/hyps/hyp.scratch-low.yaml --epoch 500 --batch-size 32 --device 0 --workers 8 ``` ## Dataset Our ship dataset will be released soon. ## References Thanks to their great works. - [ultralytics/yolov5](https://github.com/ultralytics/yolov5) - [facebookresearch/ConvNeXt](https://github.com/facebookresearch/ConvNeXt) - [OSVAI/ODConv](https://github.com/OSVAI/ODConv)
Owner
- Name: Bestsongc
- Login: Bestsongc
- Kind: user
- Repositories: 1
- Profile: https://github.com/Bestsongc
## Install
```bash
git clone https://github.com/chengshuxiao/YOLOv5-ODConvNeXt.git # clone
cd YOLOv5-ODConvNeXt
pip install -r requirements.txt # install
```
## Inference with detect.py
A trained YOLOv5-ODConvNeXt model is provided in `checkpoints/yolov5-odconvnext.pt`the detection results are saved in `runs/detect`
```bash
python detect.py
--weights checkpoints/yolov5-odconvnext.pt
--source data/images
--line-thickness 2
```
Two demo outputs are shown below.