sie-yolo11
Science Score: 44.0%
This score indicates how likely this project is to be science-related based on various indicators:
-
✓CITATION.cff file
Found CITATION.cff file -
✓codemeta.json file
Found codemeta.json file -
✓.zenodo.json file
Found .zenodo.json file -
○DOI references
-
○Academic publication links
-
○Academic email domains
-
○Institutional organization owner
-
○JOSS paper metadata
-
○Scientific vocabulary similarity
Low similarity (15.1%) to scientific vocabulary
Repository
Basic Info
- Host: GitHub
- Owner: wangjihang0239
- License: agpl-3.0
- Language: Python
- Default Branch: master
- Size: 1.68 MB
Statistics
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 3
- Releases: 0
Metadata Files
README.md
In this paper, open source datasets HRSID and SSDD are used in the research process.In this thesis, HRSID (High Resolution SAR Images Dataset) is a dataset for the tasks of ship detection, semantic segmentation, and instance segmentation in high-resolution synthetic aperture radar (SAR) images. This project is mainly used for research and development of deep learning based ship detection and segmentation techniques.The main programming language of the HRSID project is Python, which is suitable for data processing and deep learning model development using Python.SSDD (SAR Ship Detection Dataset) is a dataset dedicated to the task of ship detection and segmentation in Synthetic Aperture Radar (SAR) images. dataset for ship target detection. It was produced by the Department of Electrical and Information Engineering at the Naval Aerospace University to provide a standardized platform so that researchers can compare the performance of different algorithms under identical conditions. They are available for download at the address below:
The HRSIFD dataset is available for download at:https://github.com/chaozhong2010/HRSID
The SSDD dataset is available for download at:https://gitcode.com/ghmirrors/of/Official-SSDD/blob/main/README.md?utmsource=csdngithubaccelerator&isLogin=1
Documentation
See below for a quickstart install and usage examples, and see our Docs for full documentation on training, validation, prediction and deployment.
Install
Pip install the Ultralytics package including all [requirements](https://github.com/ultralytics/ultralytics/blob/main/pyproject.toml) in a [**Python>=3.8**](https://www.python.org/) environment with [**PyTorch>=1.8**](https://pytorch.org/get-started/locally/). [](https://pypi.org/project/ultralytics/) [](https://www.pepy.tech/projects/ultralytics) [](https://pypi.org/project/ultralytics/) ```bash pip install ultralytics ``` For alternative installation methods including [Conda](https://anaconda.org/conda-forge/ultralytics), [Docker](https://hub.docker.com/r/ultralytics/ultralytics), and Git, please refer to the [Quickstart Guide](https://docs.ultralytics.com/quickstart/). [](https://anaconda.org/conda-forge/ultralytics) [](https://hub.docker.com/r/ultralytics/ultralytics) [](https://hub.docker.com/r/ultralytics/ultralytics)Usage
### CLI YOLO may be used directly in the Command Line Interface (CLI) with a `yolo` command: ```bash yolo predict model=yolo11n.pt source='https://ultralytics.com/images/bus.jpg' ``` `yolo` can be used for a variety of tasks and modes and accepts additional arguments, e.g. `imgsz=640`. See the YOLO [CLI Docs](https://docs.ultralytics.com/usage/cli/) for examples. ### Python YOLO may also be used directly in a Python environment, and accepts the same [arguments](https://docs.ultralytics.com/usage/cfg/) as in the CLI example above: ```python from ultralytics import YOLO # Load a model model = YOLO("yolo11n.pt") # Train the model train_results = model.train( data="coco8.yaml", # path to dataset YAML epochs=100, # number of training epochs imgsz=640, # training image size device="cpu", # device to run on, i.e. device=0 or device=0,1,2,3 or device=cpu ) # Evaluate model performance on the validation set metrics = model.val() # Perform object detection on an image results = model("path/to/image.jpg") results[0].show() # Export the model to ONNX format path = model.export(format="onnx") # return path to exported model ``` See YOLO [Python Docs](https://docs.ultralytics.com/usage/python/) for more examples.Owner
- Login: wangjihang0239
- Kind: user
- Repositories: 1
- Profile: https://github.com/wangjihang0239
Citation (CITATION.cff)
# This CITATION.cff file was generated with https://bit.ly/cffinit
cff-version: 1.2.0
title: Ultralytics YOLO
message: >-
If you use this software, please cite it using the
metadata from this file.
type: software
authors:
- given-names: Glenn
family-names: Jocher
affiliation: Ultralytics
orcid: 'https://orcid.org/0000-0001-5950-6979'
- family-names: Qiu
given-names: Jing
affiliation: Ultralytics
orcid: 'https://orcid.org/0000-0003-3783-7069'
- given-names: Ayush
family-names: Chaurasia
affiliation: Ultralytics
orcid: 'https://orcid.org/0000-0002-7603-6750'
repository-code: 'https://github.com/ultralytics/ultralytics'
url: 'https://ultralytics.com'
license: AGPL-3.0
version: 8.0.0
date-released: '2023-01-10'
GitHub Events
Total
- Delete event: 2
- Issue comment event: 8
- Push event: 9
- Pull request event: 7
- Create event: 9
Last Year
- Delete event: 2
- Issue comment event: 8
- Push event: 9
- Pull request event: 7
- Create event: 9