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lgpn

https://github.com/goforit96/lgpn

Science Score: 44.0%

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    Scientific vocabulary similarity
    Low similarity (9.7%) to scientific vocabulary
Last synced: 6 months ago · JSON representation ·

Repository

Basic Info
  • Host: GitHub
  • Owner: goforit96
  • Language: Python
  • Default Branch: master
  • Size: 21.1 MB
Statistics
  • Stars: 0
  • Watchers: 0
  • Forks: 0
  • Open Issues: 0
  • Releases: 0
Created 8 months ago · Last pushed 7 months ago
Metadata Files
Readme Contributing Citation

README.md

Introduction

This is the implementation code for the paper "LGPN: A Lightweight Algorithm for Enhanced Dental Lesion Detection in Panoramic X-rays." The model is built based on YOLOv8s, using the pretrained configuration file yolov8s.pt.

Software Platform

CUDA 11.3

PyTorch 1.12.0

Python 3.8

Training and Testing

You can use PyCharm as the development environment and run the code through Python command-line execution.

Training

To perform training, create a Python file named train.py and input the following code:

from ultralytics import YOLO

if name == 'main':

model = YOLO('ultralytics/cfg/models/v8/yolov8s.yaml')

model.load('yolov8s.pt')  # loading pre-trained weights

model.train(

    data='your_data.yaml',

    imgsz=640,

    epochs=,          # specify number of epochs

    batch=,           # specify batch size

    close_mosaic=,    # optionally close mosaic augmentation

    device='',        # specify CUDA device (e.g. '0')

    optimizer='SGD',  # using SGD optimizer

    # resume='True',   # path to last.pt (if resuming training)

    # amp=False,       # disable AMP (automatic mixed precision)

    # fraction=0.2,

    # project='runs/train',

    name='exp',

)

Some of the parameters (like epochs, batch, device, etc.) should be specified according to your specific needs.

Testing

The testing procedure follows the same logic: create a test script similarly and modify the mode or use .predict() or .val() methods as needed.

Owner

  • Name: Algorithm
  • Login: goforit96
  • Kind: user

Citation (CITATION.cff) 

cff-version: 1.2.0
preferred-citation:
  type: software
  message: If you use this software, please cite it as below.
  authors:
  - family-names: Jocher
    given-names: Glenn
    orcid: "https://orcid.org/0000-0001-5950-6979"
  - family-names: Chaurasia
    given-names: Ayush
    orcid: "https://orcid.org/0000-0002-7603-6750"
  - family-names: Qiu
    given-names: Jing
    orcid: "https://orcid.org/0000-0003-3783-7069"
  title: "YOLO by Ultralytics"
  version: 8.0.0
  # doi: 10.5281/zenodo.3908559  # TODO
  date-released: 2023-1-10
  license: AGPL-3.0
  url: "https://github.com/ultralytics/ultralytics"

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Dependencies

examples/YOLOv8-ONNXRuntime-Rust/Cargo.toml cargo
docker/Dockerfile docker
  • pytorch/pytorch 2.1.0-cuda12.1-cudnn8-runtime build
requirements.txt pypi
  • matplotlib >=3.3.0
  • mmcv *
  • numpy *
  • opencv-python >=4.6.0
  • pandas >=1.1.4
  • pillow >=7.1.2
  • psutil *
  • py-cpuinfo *
  • pyyaml >=5.3.1
  • requests >=2.23.0
  • scipy >=1.4.1
  • seaborn >=0.11.0
  • setuptools *
  • thop >=0.1.1
  • torch >=1.8.0
  • torchvision >=0.9.0
  • tqdm >=4.64.0
  • ultralytics *
  • wandb *
  • yolo *
setup.py pypi