文章实验数据真实性有待考究

[zhaojinbiao]

❔Question

Additional context

讨论的数据根本就对不上！！！

[github-actions]

👋 Hello @zhaojinbiao, thank you for your interest in YOLOv5 🚀! Please visit our ⭐️ Tutorials to get started, where you can find quickstart guides for simple tasks like Custom Data Training all the way to advanced concepts like Hyperparameter Evolution.

If this is a 🐛 Bug Report, please provide screenshots and minimum viable code to reproduce your issue, otherwise we can not help you.

If this is a custom training ❓ Question, please provide as much information as possible, including dataset images, training logs, screenshots, and a public link to online W&B logging if available.

For business inquiries or professional support requests please visit https://ultralytics.com or email Glenn Jocher at [email protected].

Requirements

Python>=3.6.0 with all requirements.txt installed including PyTorch>=1.7. To get started:

$ git clone https://github.com/ultralytics/yolov5
$ cd yolov5
$ pip install -r requirements.txt

Environments

YOLOv5 may be run in any of the following up-to-date verified environments (with all dependencies including CUDA/CUDNN, Python and PyTorch preinstalled):

• Google Colab and Kaggle notebooks with free GPU:
• Google Cloud Deep Learning VM. See GCP Quickstart Guide
• Amazon Deep Learning AMI. See AWS Quickstart Guide
• Docker Image. See Docker Quickstart Guide

Status

If this badge is green, all YOLOv5 GitHub Actions Continuous Integration (CI) tests are currently passing. CI tests verify correct operation of YOLOv5 training (train.py), validation (val.py), inference (detect.py) and export (export.py) on MacOS, Windows, and Ubuntu every 24 hours and on every commit.

[qinhongda8]

❔Question

Additional context

讨论的数据根本就对不上！！！

(39.6-34.1)/34.1=0.161=16.1%, (51.3-34.1)/34.1=0.504=50.4%, (55.8-34.1)/34.1=0.636=63.6%, (43.6-36.4)/36.4=0.197=19.7%, (51.3-41.2)/41.2=0.245=24.5%, (67.0-49.3)/49.3=0.359=35.9%, (55.8-45.6)/45.6=0.223=22.3%