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Bump actions/checkout from 2 to 4 #36

Bump actions/checkout from 2 to 4

Bump actions/checkout from 2 to 4 #36

Workflow file for this run

# YOLOv3 πŸš€ by Ultralytics, GPL-3.0 license
name: Greetings
on: [pull_request_target, issues]
jobs:
greeting:
runs-on: ubuntu-latest
steps:
- uses: actions/first-interaction@v1
with:
repo-token: ${{ secrets.GITHUB_TOKEN }}
pr-message: |
πŸ‘‹ Hello @${{ github.actor }}, thank you for submitting a πŸš€ PR! To allow your work to be integrated as seamlessly as possible, we advise you to:
- βœ… Verify your PR is **up-to-date with upstream/master.** If your PR is behind upstream/master an automatic [GitHub actions](https://github.com/ultralytics/yolov3/blob/master/.github/workflows/rebase.yml) rebase may be attempted by including the /rebase command in a comment body, or by running the following code, replacing 'feature' with the name of your local branch:
```bash
git remote add upstream https://github.com/ultralytics/yolov3.git
git fetch upstream
git checkout feature # <----- replace 'feature' with local branch name
git merge upstream/master
git push -u origin -f
```
- βœ… Verify all Continuous Integration (CI) **checks are passing**.
- βœ… Reduce changes to the absolute **minimum** required for your bug fix or feature addition. _"It is not daily increase but daily decrease, hack away the unessential. The closer to the source, the less wastage there is."_ -Bruce Lee
issue-message: |
πŸ‘‹ Hello @${{ github.actor }}, thank you for your interest in YOLOv3 πŸš€! Please visit our ⭐️ [Tutorials](https://github.com/ultralytics/yolov3/wiki#tutorials) to get started, where you can find quickstart guides for simple tasks like [Custom Data Training](https://github.com/ultralytics/yolov3/wiki/Train-Custom-Data) all the way to advanced concepts like [Hyperparameter Evolution](https://github.com/ultralytics/yolov5/issues/607).
If this is a πŸ› 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](https://github.com/ultralytics/yolov3/wiki/Train-Custom-Data#visualize) 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**](https://www.python.org/) with all [requirements.txt](https://github.com/ultralytics/yolov3/blob/master/requirements.txt) installed including [**PyTorch>=1.7**](https://pytorch.org/get-started/locally/). To get started:
```bash
$ git clone https://github.com/ultralytics/yolov3
$ cd yolov3
$ pip install -r requirements.txt
```
## Environments
YOLOv3 may be run in any of the following up-to-date verified environments (with all dependencies including [CUDA](https://developer.nvidia.com/cuda)/[CUDNN](https://developer.nvidia.com/cudnn), [Python](https://www.python.org/) and [PyTorch](https://pytorch.org/) preinstalled):
- **Google Colab and Kaggle** notebooks with free GPU: <a href="https://colab.research.google.com/github/ultralytics/yolov3/blob/master/tutorial.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a> <a href="https://www.kaggle.com/ultralytics/yolov3"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open In Kaggle"></a>
- **Google Cloud** Deep Learning VM. See [GCP Quickstart Guide](https://github.com/ultralytics/yolov3/wiki/GCP-Quickstart)
- **Amazon** Deep Learning AMI. See [AWS Quickstart Guide](https://github.com/ultralytics/yolov3/wiki/AWS-Quickstart)
- **Docker Image**. See [Docker Quickstart Guide](https://github.com/ultralytics/yolov3/wiki/Docker-Quickstart) <a href="https://hub.docker.com/r/ultralytics/yolov3"><img src="https://img.shields.io/docker/pulls/ultralytics/yolov3?logo=docker" alt="Docker Pulls"></a>
## Status
<a href="https://github.com/ultralytics/yolov3/actions"><img src="https://github.com/ultralytics/yolov3/workflows/CI%20CPU%20testing/badge.svg" alt="CI CPU testing"></a>
If this badge is green, all [YOLOv3 GitHub Actions](https://github.com/ultralytics/yolov3/actions) Continuous Integration (CI) tests are currently passing. CI tests verify correct operation of YOLOv3 training ([train.py](https://github.com/ultralytics/yolov3/blob/master/train.py)), validation ([val.py](https://github.com/ultralytics/yolov3/blob/master/val.py)), inference ([detect.py](https://github.com/ultralytics/yolov3/blob/master/detect.py)) and export ([export.py](https://github.com/ultralytics/yolov3/blob/master/export.py)) on MacOS, Windows, and Ubuntu every 24 hours and on every commit.