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tugas-besar-visi-komputer-kelompok-4

Visi Komputer

https://github.com/californiahasfallen69/tugas-besar-visi-komputer-kelompok-4

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 (4.3%) to scientific vocabulary
Last synced: 6 months ago · JSON representation ·

Repository

Visi Komputer

Basic Info
  • Host: GitHub
  • Owner: CaliforniaHasFallen69
  • License: agpl-3.0
  • Language: Python
  • Default Branch: main
  • Homepage:
  • Size: 1.05 MB
Statistics
  • Stars: 0
  • Watchers: 1
  • Forks: 0
  • Open Issues: 1
  • Releases: 0
Created over 1 year ago · Last pushed over 1 year ago
Metadata Files
Readme Contributing License Citation

README.md

Tugas Besar Visi Komputer

Object Detection dengan Model Deep Learning "YOLOv5" Topik: Perabot

Kelompok 4 - Kelas A

24060121140159 ZAERI HAIKAL RABBANI 24060121140163 RAFIF ABBRAR MAHESWARA 24060121140142 NAUFAL ARIQ DWIKURNIA 24060121120013 IKSAN NUR ROCHIM 24060121140170 YODA RACHMAN NUR SAHID

Link Video Demo Program

https://www.youtube.com/watch?v=m6dFAzOT2ww

Deskripsi

  • Program ini menggunakan YOLOv5 untuk mendeteksi objek dari foto dan video

Dataset

Sumber : https://universe.roboflow.com/roboflow-100/furniture-ngpea/dataset/2 Dataset terdiri dari 3 kelas perabot, yaitu :

  • Chair
  • Sofa
  • Table

Sebelum menjalankan program, pastikan Anda telah menginstal:

  • Python 3.8.0 atau yang lebih baru

Penggunaan

  1. Clone YOLOv5 Framework dari github Sumber Framework : https://github.com/ultralytics/yolov5
  2. Install dependensi yang tertera pada file 'requirements.txt dengan command : pip install requirements.txt
  3. Download dataset dari roboflow dengan format YOLOv5pytorch.txt
  4. Masukkan dataset tersebut dengan nama folder adalah dataset2 dalam satu folder project
  5. Jalankan program deteksi objek video dengan command : python detect.py --weights runs/train/exp13/weights/best.pt --source AsiaFurniture2.mp4 --img 640 --conf 0.25 (untuk conf, img, dan source bisa disesuaikan)
  6. file video akan tersimpan di folder runs

TERIMA KASIH

Owner

  • Login: CaliforniaHasFallen69
  • Kind: user

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"

GitHub Events

Total
  • Issue comment event: 2
  • Push event: 1
  • Pull request event: 1
  • Create event: 3
Last Year
  • Issue comment event: 2
  • Push event: 1
  • Pull request event: 1
  • Create event: 3

Dependencies

.github/workflows/ci-testing.yml actions
  • actions/checkout v4 composite
  • actions/setup-python v5 composite
  • slackapi/slack-github-action v1.27.0 composite
.github/workflows/cla.yml actions
  • contributor-assistant/github-action v2.6.1 composite
.github/workflows/codeql-analysis.yml actions
  • actions/checkout v4 composite
  • github/codeql-action/analyze v3 composite
  • github/codeql-action/init v3 composite
.github/workflows/docker.yml actions
  • actions/checkout v4 composite
  • docker/build-push-action v6 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/links.yml actions
  • actions/checkout v4 composite
  • nick-invision/retry v3 composite
.github/workflows/merge-main-into-prs.yml actions
  • actions/checkout v4 composite
  • 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
  • scipy >=1.4.1
  • 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 - Copy.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 >=2.32.2
  • scipy >=1.4.1
  • seaborn >=0.11.0
  • setuptools >=70.0.0
  • thop >=0.1.1
  • torchvision >=0.9.0
  • tqdm >=4.66.3
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 >=2.32.2
  • scipy >=1.4.1
  • seaborn >=0.11.0
  • setuptools >=70.0.0
  • thop >=0.1.1
  • torchvision >=0.9.0
  • tqdm >=4.66.3
utils/google_app_engine/additional_requirements.txt pypi
  • Flask ==2.3.2
  • gunicorn ==22.0.0
  • pip ==23.3
  • werkzeug >=3.0.1
  • zipp >=3.19.1