[Snyk] Security upgrade numpy from 1.21.3 to 1.22.2

[snyk-bot]

Snyk has created this PR to fix one or more vulnerable packages in the `pip` dependencies of this project.

Changes included in this PR

• Changes to the following files to upgrade the vulnerable dependencies to a fixed version:
  • requirements.txt

⚠️ Warning

torch 1.13.1 requires nvidia-cublas-cu11, which is not installed.
torch 1.13.1 requires nvidia-cuda-nvrtc-cu11, which is not installed.
torch 1.13.1 requires nvidia-cuda-runtime-cu11, which is not installed.
torch 1.13.1 requires nvidia-cudnn-cu11, which is not installed.

Vulnerabilities that will be fixed

By pinning:

Severity	Priority Score (*)	Issue	Upgrade	Breaking Change	Exploit Maturity
	506/1000 Why? Proof of Concept exploit, Has a fix available, CVSS 3.7	NULL Pointer Dereference SNYK-PYTHON-NUMPY-2321964	numpy: 1.21.3 -> 1.22.2	No	Proof of Concept
	399/1000 Why? Has a fix available, CVSS 3.7	Buffer Overflow SNYK-PYTHON-NUMPY-2321966	numpy: 1.21.3 -> 1.22.2	No	No Known Exploit
	506/1000 Why? Proof of Concept exploit, Has a fix available, CVSS 3.7	Denial of Service (DoS) SNYK-PYTHON-NUMPY-2321970	numpy: 1.21.3 -> 1.22.2	No	Proof of Concept

(*) Note that the real score may have changed since the PR was raised.

Some vulnerabilities couldn't be fully fixed and so Snyk will still find them when the project is tested again. This may be because the vulnerability existed within more than one direct dependency, but not all of the affected dependencies could be upgraded.

Check the changes in this PR to ensure they won't cause issues with your project.

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Learn how to fix vulnerabilities with free interactive lessons:

🦉 NULL Pointer Dereference
🦉 Denial of Service (DoS)

🛠️ PR Summary

_{Made with ❤️ by Ultralytics Actions}

🌟 Summary

Upgraded minimum required version of NumPy in yolov5 dependencies.

📊 Key Changes

• Updated the numpy library from a minimum version of 1.18.5 to 1.22.2.

🎯 Purpose & Impact

• Ensure Compatibility: Newer versions of other libraries may rely on features or fixes introduced in newer NumPy versions.
• Access to New Features: Users can leverage improvements and new functionalities available in the updated NumPy.
• Potential Performance Improvements: NumPy updates often include optimizations that could benefit YOLOv5 performance.
• Developer Awareness: Ensures that developers working on the project are aware of the new minimum NumPy version requirement, which can prevent compatibility issues.
• User Impact: Users updating YOLOv5 need to ensure their NumPy version is at least 1.22.2. This may require users to update their Python environment accordingly.