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yolov5customdataset

This repository demonstrates how to use YOLOv5 for object detection on a custom dataset. It includes scripts and instructions for preparing the dataset, training the model, and evaluating its performance.

https://github.com/tanvirnwu/yolov5customdataset

Science Score: 44.0%

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Keywords

custom customdataset dataset object objectdetection yolov5
Last synced: 6 months ago · JSON representation ·

Repository

This repository demonstrates how to use YOLOv5 for object detection on a custom dataset. It includes scripts and instructions for preparing the dataset, training the model, and evaluating its performance.

Basic Info
  • Host: GitHub
  • Owner: tanvirnwu
  • License: agpl-3.0
  • Language: Python
  • Default Branch: master
  • Homepage:
  • Size: 14.4 MB
Statistics
  • Stars: 2
  • Watchers: 1
  • Forks: 0
  • Open Issues: 2
  • Releases: 0
Topics
custom customdataset dataset object objectdetection yolov5
Created almost 2 years ago · Last pushed over 1 year ago
Metadata Files
Readme License Citation

README.md

YOLOv5 Custom Dataset Application

This repository contains my work on using the YOLOv5 object detection model on a custom dataset. The original YOLOv5 repository by Ultralytics can be found here.

Overview

YOLOv5 is a state-of-the-art object detection model that provides excellent accuracy and performance. This project demonstrates how to train YOLOv5 on a custom dataset and evaluate its performance.

Features

  • Clone of the original YOLOv5 repository.
  • Training of YOLOv5 on a custom dataset.
  • Evaluation of the trained model on a custom test set.
  • Instructions and scripts for reproducing the results.

Installation

  1. Clone this repository: ```bash git clone https://github.com/your-username/YOLOV5CustomDataset.git cd your-repo-name

  2. Install the requirements ```bash pip install -r requirements.txt

  3. Preparing the Custom Dataset ```bash data/ ├── mycustomdataset/ │ ├── images/ │ │ ├── train/ │ │ │ ├── img1.jpg │ │ │ ├── img2.jpg │ │ │ └── ... │ │ ├── val/ │ │ │ ├── img1.jpg │ │ │ ├── img2.jpg │ │ │ └── ... │ │ ├── test/ │ │ │ ├── img1.jpg │ │ │ ├── img2.jpg │ │ │ └── ... │ ├── labels/ │ │ ├── train/ │ │ │ ├── img1.txt │ │ │ ├── img2.txt │ │ │ └── ... │ │ ├── val/ │ │ │ ├── img1.txt │ │ │ ├── img2.txt │ │ │ └── ... │ │ ├── test/ │ │ │ ├── img1.txt │ │ │ ├── img2.txt │ │ │ └── ...

  4. Create a dataset configuration file mycustomdataset.yaml ```bash train: data/mycustomdataset/images/train val: data/mycustomdataset/images/val test: data/mycustomdataset/images/test

nc: 2 # number of classes names: ['class1', 'class2'] # list of class names

  1. Training Model To train YOLOv5 on your custom dataset, run: ```bash python train.py --img 640 --batch 16 --epochs 100 --data data/mycustomdataset.yaml --weights yolov5s.pt

  2. Testing Model To evaluate the trained model on the test set, run: ```bash python val.py --data data/mycustomdataset.yaml --weights runs/train/exp/weights/best.pt --img 640 --task test

  3. Results The training and evaluation results, including loss curves and other metrics, can be found in the runs/ directory.

Acknowledgements

The original YOLOv5 repository: Ultralytics YOLOv5

Owner

  • Name: Md Tanvir Islam
  • Login: tanvirnwu
  • Kind: user
  • Location: Suwon, South Korea

Masters student at Sungkyunkwan University (SKKU), South Korea

Citation (CITATION.cff) 

cff-version: 1.2.0
preferred-citation:
  type: software
  message: If you use YOLOv5, please cite it as below.
  authors:
  - family-names: Jocher
    given-names: Glenn
    orcid: "https://orcid.org/0000-0001-5950-6979"
  title: "YOLOv5 by Ultralytics"
  version: 7.0
  doi: 10.5281/zenodo.3908559
  date-released: 2020-5-29
  license: AGPL-3.0
  url: "https://github.com/ultralytics/yolov5"

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Dependencies

.github/workflows/ci-testing.yml actions
  • actions/checkout v4 composite
  • actions/setup-python v5 composite
  • slackapi/slack-github-action v1.26.0 composite
.github/workflows/cla.yml actions
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.github/workflows/codeql-analysis.yml actions
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  • github/codeql-action/autobuild v3 composite
  • github/codeql-action/init v3 composite
.github/workflows/docker.yml actions
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  • docker/build-push-action v5 composite
  • docker/login-action v3 composite
  • docker/setup-buildx-action v3 composite
  • docker/setup-qemu-action v3 composite
.github/workflows/format.yml actions
  • ultralytics/actions main composite
.github/workflows/greetings.yml actions
  • actions/first-interaction v1 composite
.github/workflows/links.yml actions
  • actions/checkout v4 composite
  • nick-invision/retry v3 composite
.github/workflows/merge-main-into-prs.yml actions
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  • actions/setup-python v5 composite
.github/workflows/stale.yml actions
  • actions/stale v9 composite
utils/docker/Dockerfile docker
  • pytorch/pytorch 2.0.0-cuda11.7-cudnn8-runtime build
utils/google_app_engine/Dockerfile docker
  • gcr.io/google-appengine/python latest build
pyproject.toml pypi
  • matplotlib >=3.3.0
  • numpy >=1.22.2
  • opencv-python >=4.6.0
  • pandas >=1.1.4
  • pillow >=7.1.2
  • psutil *
  • py-cpuinfo *
  • pyyaml >=5.3.1
  • requests >=2.23.0
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  • seaborn >=0.11.0
  • thop >=0.1.1
  • torch >=1.8.0
  • torchvision >=0.9.0
  • tqdm >=4.64.0
  • ultralytics >=8.1.47
requirements.txt pypi
  • PyYAML >=5.3.1
  • gitpython >=3.1.30
  • matplotlib >=3.3
  • numpy >=1.23.5
  • opencv-python >=4.1.1
  • pandas >=1.1.4
  • pillow >=10.3.0
  • psutil *
  • requests >=2.23.0
  • scipy >=1.4.1
  • seaborn >=0.11.0
  • setuptools >=65.5.1
  • thop >=0.1.1
  • torchvision >=0.9.0
  • tqdm >=4.64.0
  • ultralytics >=8.0.232
  • wheel >=0.38.0
utils/google_app_engine/additional_requirements.txt pypi
  • Flask ==2.3.2
  • gunicorn ==22.0.0
  • pip ==23.3
  • werkzeug >=3.0.1