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yolov5_skin_disense

https://github.com/lndong2612/yolov5_skin_disense

Science Score: 36.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • ○
    CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
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    .zenodo.json file
    Found .zenodo.json file
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    DOI references
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    Academic publication links
    Links to: zenodo.org
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    Academic email domains
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    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (9.4%) to scientific vocabulary
Last synced: 10 months ago · JSON representation

Repository

Basic Info
  • Host: GitHub
  • Owner: lndong2612
  • License: agpl-3.0
  • Language: Python
  • Default Branch: main
  • Size: 3.5 MB
Statistics
  • Stars: 0
  • Watchers: 1
  • Forks: 0
  • Open Issues: 0
  • Releases: 0
Created almost 3 years ago · Last pushed over 1 year ago
Metadata Files
Readme License Citation

README.md

[English](README.md) | [简体中文](README.zh-CN.md)
YOLOv5 CI YOLOv5 Citation Docker Pulls
Run on Gradient Open In Colab Open In Kaggle

YOLOv5 🚀 is the world's most loved vision AI, representing Ultralytics open-source research into future vision AI methods, incorporating lessons learned and best practices evolved over thousands of hours of research and development. We hope that the resources here will help you get the most out of YOLOv5. Please browse the YOLOv5 Docs for details, raise an issue on GitHub for support, and join our Discord community for questions and discussions! To request an Enterprise License please complete the form at [Ultralytics Licensing](https://ultralytics.com/license).

In this repository, i will using yolov5 to detect the skin disense with the input is an image and the output is a dict contain xmax, ymax, xmin, ymin, score and label of disense.


Install

  1. Download this weight name best.pt and save with the following path: public/files/weight_init/best.pt

  2. Create and activate a virtual environment:

    sh $ python3 -m venv venv && source venv/bin/activate

  3. Install the requirements:

    sh (venv)$ pip install -r requirements.txt

  4. Run

    sh (venv)$ python main.py

Example

I wrote an example code that sends an input image, receives the output, and uses that result to draw on the input image.

Run in different console while running main.py

python (venv)$ python test/test_client.py

{ "content": [ { "label": "Hắc lào", "score": "0.90", "xmax": 436, "xmin": 339, "ymax": 238, "ymin": 136 }, { "label": "Lang ben", "score": "0.91", "xmax": 306, "xmin": 203, "ymax": 169, "ymin": 45 }, { "label": "Hắc lào", "score": "0.93", "xmax": 537, "xmin": 414, "ymax": 213, "ymin": 83 }, { "label": "Lang ben", "score": "0.93", "xmax": 223, "xmin": 0, "ymax": 281, "ymin": 101 }, { "detectedimage": "resources/images/2023/09/13/detect/17284113092023detected.jpg", "originalimage": "resources/images/2023/09/13/original/17284113092023original.jpg" } ], "status_code": 200 }

Owner

  • Name: Lê Nhân Đông
  • Login: lndong2612
  • Kind: user
  • Location: Thanhhoa, Vietnam
  • Company: ThinkLABs JSC, Thanh Hoá

1998 @Arsenal

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