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---
name: "🐛 Bug report"
about: Create a report to help us improve
title: ''
labels: bug
assignees: ''
---
Before submitting a bug report, please be aware that your issue **must be reproducible** with all of the following, otherwise it is non-actionable, and we can not help you:
- **Current repo**: run `git fetch && git status -uno` to check and `git pull` to update repo
- **Common dataset**: coco.yaml or coco128.yaml
- **Common environment**: Colab, Google Cloud, or Docker image. See https://github.com/ultralytics/yolov5#environments
If this is a custom dataset/training question you **must include** your `train*.jpg`, `test*.jpg` and `results.png` figures, or we can not help you. You can generate these with `utils.plot_results()`.
## 🐛 Bug
A clear and concise description of what the bug is.
## To Reproduce (REQUIRED)
Input:
```
import torch
a = torch.tensor([5])
c = a / 0
```
Output:
```
Traceback (most recent call last):
File "/Users/glennjocher/opt/anaconda3/envs/env1/lib/python3.7/site-packages/IPython/core/interactiveshell.py", line 3331, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File "<ipython-input-5-be04c762b799>", line 5, in <module>
c = a / 0
RuntimeError: ZeroDivisionError
```
## Expected behavior
A clear and concise description of what you expected to happen.
## Environment
If applicable, add screenshots to help explain your problem.
- OS: [e.g. Ubuntu]
- GPU [e.g. 2080 Ti]
## Additional context
Add any other context about the problem here.

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---
name: "🚀 Feature request"
about: Suggest an idea for this project
title: ''
labels: enhancement
assignees: ''
---
## 🚀 Feature
<!-- A clear and concise description of the feature proposal -->
## Motivation
<!-- Please outline the motivation for the proposal. Is your feature request related to a problem? e.g., I'm always frustrated when [...]. If this is related to another GitHub issue, please link here too -->
## Pitch
<!-- A clear and concise description of what you want to happen. -->
## Alternatives
<!-- A clear and concise description of any alternative solutions or features you've considered, if any. -->
## Additional context
<!-- Add any other context or screenshots about the feature request here. -->

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---
name: "❓Question"
about: Ask a general question
title: ''
labels: question
assignees: ''
---
## ❔Question
## Additional context

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version: 2
updates:
- package-ecosystem: pip
directory: "/"
schedule:
interval: weekly
time: "04:00"
open-pull-requests-limit: 10
reviewers:
- glenn-jocher
labels:
- dependencies

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name: CI CPU testing
on: # https://help.github.com/en/actions/reference/events-that-trigger-workflows
push:
branches: [ master ]
pull_request:
# The branches below must be a subset of the branches above
branches: [ master ]
schedule:
- cron: '0 0 * * *' # Runs at 00:00 UTC every day
jobs:
cpu-tests:
runs-on: ${{ matrix.os }}
strategy:
fail-fast: false
matrix:
os: [ubuntu-latest, macos-latest, windows-latest]
python-version: [3.8]
model: ['yolov5s'] # models to test
# Timeout: https://stackoverflow.com/a/59076067/4521646
timeout-minutes: 50
steps:
- uses: actions/checkout@v2
- name: Set up Python ${{ matrix.python-version }}
uses: actions/setup-python@v2
with:
python-version: ${{ matrix.python-version }}
# Note: This uses an internal pip API and may not always work
# https://github.com/actions/cache/blob/master/examples.md#multiple-oss-in-a-workflow
- name: Get pip cache
id: pip-cache
run: |
python -c "from pip._internal.locations import USER_CACHE_DIR; print('::set-output name=dir::' + USER_CACHE_DIR)"
- name: Cache pip
uses: actions/cache@v1
with:
path: ${{ steps.pip-cache.outputs.dir }}
key: ${{ runner.os }}-${{ matrix.python-version }}-pip-${{ hashFiles('requirements.txt') }}
restore-keys: |
${{ runner.os }}-${{ matrix.python-version }}-pip-
- name: Install dependencies
run: |
python -m pip install --upgrade pip
pip install -qr requirements.txt -f https://download.pytorch.org/whl/cpu/torch_stable.html
pip install -q onnx
python --version
pip --version
pip list
shell: bash
- name: Download data
run: |
# curl -L -o tmp.zip https://github.com/ultralytics/yolov5/releases/download/v1.0/coco128.zip
# unzip -q tmp.zip -d ../
# rm tmp.zip
- name: Tests workflow
run: |
# export PYTHONPATH="$PWD" # to run '$ python *.py' files in subdirectories
di=cpu # inference devices # define device
# train
python train.py --img 128 --batch 16 --weights weights/${{ matrix.model }}.pt --cfg models/${{ matrix.model }}.yaml --epochs 1 --device $di
# detect
python detect.py --weights weights/${{ matrix.model }}.pt --device $di
python detect.py --weights runs/train/exp/weights/last.pt --device $di
# test
python test.py --img 128 --batch 16 --weights weights/${{ matrix.model }}.pt --device $di
python test.py --img 128 --batch 16 --weights runs/train/exp/weights/last.pt --device $di
python hubconf.py # hub
python models/yolo.py --cfg models/${{ matrix.model }}.yaml # inspect
python models/export.py --img 128 --batch 1 --weights weights/${{ matrix.model }}.pt # export
shell: bash

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# This action runs GitHub's industry-leading static analysis engine, CodeQL, against a repository's source code to find security vulnerabilities.
# https://github.com/github/codeql-action
name: "CodeQL"
on:
schedule:
- cron: '0 0 1 * *' # Runs at 00:00 UTC on the 1st of every month
jobs:
analyze:
name: Analyze
runs-on: ubuntu-latest
strategy:
fail-fast: false
matrix:
language: [ 'python' ]
# CodeQL supports [ 'cpp', 'csharp', 'go', 'java', 'javascript', 'python' ]
# Learn more:
# https://docs.github.com/en/free-pro-team@latest/github/finding-security-vulnerabilities-and-errors-in-your-code/configuring-code-scanning#changing-the-languages-that-are-analyzed
steps:
- name: Checkout repository
uses: actions/checkout@v2
# Initializes the CodeQL tools for scanning.
- name: Initialize CodeQL
uses: github/codeql-action/init@v1
with:
languages: ${{ matrix.language }}
# If you wish to specify custom queries, you can do so here or in a config file.
# By default, queries listed here will override any specified in a config file.
# Prefix the list here with "+" to use these queries and those in the config file.
# queries: ./path/to/local/query, your-org/your-repo/queries@main
# Autobuild attempts to build any compiled languages (C/C++, C#, or Java).
# If this step fails, then you should remove it and run the build manually (see below)
- name: Autobuild
uses: github/codeql-action/autobuild@v1
# Command-line programs to run using the OS shell.
# 📚 https://git.io/JvXDl
# ✏️ If the Autobuild fails above, remove it and uncomment the following three lines
# and modify them (or add more) to build your code if your project
# uses a compiled language
#- run: |
# make bootstrap
# make release
- name: Perform CodeQL Analysis
uses: github/codeql-action/analyze@v1

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name: Greetings
on: [pull_request_target, issues]
jobs:
greeting:
runs-on: ubuntu-latest
steps:
- uses: actions/first-interaction@v1
with:
repo-token: ${{ secrets.GITHUB_TOKEN }}
pr-message: |
👋 Hello @${{ github.actor }}, thank you for submitting a 🚀 PR! To allow your work to be integrated as seamlessly as possible, we advise you to:
- ✅ Verify your PR is **up-to-date with origin/master.** If your PR is behind origin/master an automatic [GitHub actions](https://github.com/ultralytics/yolov5/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/yolov5.git
git fetch upstream
git checkout feature # <----- replace 'feature' with local branch name
git rebase upstream/master
git push -u origin -f
```
- ✅ Verify all Continuous Integration (CI) **checks are passing**.
- ✅ 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: |
👋 Hello @${{ github.actor }}, thank you for your interest in 🚀 YOLOv5! Please visit our ⭐️ [Tutorials](https://github.com/ultralytics/yolov5/wiki#tutorials) to get started, where you can find quickstart guides for simple tasks like [Custom Data Training](https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data) all the way to advanced concepts like [Hyperparameter Evolution](https://github.com/ultralytics/yolov5/issues/607).
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](https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data#visualize) if available.
For business inquiries or professional support requests please visit https://www.ultralytics.com or email Glenn Jocher at glenn.jocher@ultralytics.com.
## Requirements
Python 3.8 or later with all [requirements.txt](https://github.com/ultralytics/yolov5/blob/master/requirements.txt) dependencies installed, including `torch>=1.7`. To install run:
```bash
$ 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](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/yolov5/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/yolov5"><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/yolov5/wiki/GCP-Quickstart)
- **Amazon** Deep Learning AMI. See [AWS Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/AWS-Quickstart)
- **Docker Image**. See [Docker Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/Docker-Quickstart) <a href="https://hub.docker.com/r/ultralytics/yolov5"><img src="https://img.shields.io/docker/pulls/ultralytics/yolov5?logo=docker" alt="Docker Pulls"></a>
## Status
![CI CPU testing](https://github.com/ultralytics/yolov5/workflows/CI%20CPU%20testing/badge.svg)
If this badge is green, all [YOLOv5 GitHub Actions](https://github.com/ultralytics/yolov5/actions) Continuous Integration (CI) tests are currently passing. CI tests verify correct operation of YOLOv5 training ([train.py](https://github.com/ultralytics/yolov5/blob/master/train.py)), testing ([test.py](https://github.com/ultralytics/yolov5/blob/master/test.py)), inference ([detect.py](https://github.com/ultralytics/yolov5/blob/master/detect.py)) and export ([export.py](https://github.com/ultralytics/yolov5/blob/master/models/export.py)) on MacOS, Windows, and Ubuntu every 24 hours and on every commit.

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name: Automatic Rebase
# https://github.com/marketplace/actions/automatic-rebase
on:
issue_comment:
types: [created]
jobs:
rebase:
name: Rebase
if: github.event.issue.pull_request != '' && contains(github.event.comment.body, '/rebase')
runs-on: ubuntu-latest
steps:
- name: Checkout the latest code
uses: actions/checkout@v2
with:
fetch-depth: 0
- name: Automatic Rebase
uses: cirrus-actions/rebase@1.3.1
env:
GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}

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name: Close stale issues
on:
schedule:
- cron: "0 0 * * *"
jobs:
stale:
runs-on: ubuntu-latest
steps:
- uses: actions/stale@v3
with:
repo-token: ${{ secrets.GITHUB_TOKEN }}
stale-issue-message: 'This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.'
stale-pr-message: 'This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.'
days-before-stale: 30
days-before-close: 5
exempt-issue-labels: 'documentation,tutorial'
operations-per-run: 100 # The maximum number of operations per run, used to control rate limiting.