⚠️ HIGH: The --no-deps path lists a manual dependency set that is likely incomplete/incorrect for many Kaggle environments (e.g., missing ultralytics optional/runtime deps, and torch/torchvision versions must match CUDA + Kaggle image). This guidance risks breaking working environments. Prefer directing users to reinstall only ultralytics (and maybe opencv-python) or to pin versions based on the Kaggle image, rather than prescribing a broad manual dependency list.

It is required to install with --no-deps as numpy, opencv and torch are already installed in Kaggle notebook which raises dependency conflict while installing Ultralytics

⚠️ HIGH: Pinning opencv-python==4.8.1.78 numpy==1.24.3 is likely to conflict with Kaggle’s preinstalled stack (and with different Python versions), and can downgrade/upgrade in ways that break other libs. Consider removing hard pins or rephrasing to recommend pinning only when an error message indicates a required version, and to match Kaggle’s preinstalled versions.

💡 MEDIUM: Installing from git+https://github.com/ultralytics/ultralytics.git on Kaggle can be fragile because Kaggle images often lack git (or block it) and users may hit authentication/proxy issues. Consider either linking to a known-working Kaggle notebook as the recommended path, or add a short fallback note (e.g., install the PyPI package instead) to prevent a dead-end.

💡 MEDIUM: Saying the dependency resolver message "can be safely ignored" is risky; in some cases it indicates an incompatible environment that will fail at import/runtime. Consider wording this as "often a warning" and instruct users to run ultralytics.checks() (or a quick import + inference) to confirm everything works, and then apply the troubleshooting methods if it doesn’t.

📝 LOW: The code block mixes shell-style !pip with a UI instruction comment. Since Kaggle UI labels vary, consider using more general wording like "restart the kernel/session" outside the code block, or ensure the label matches Kaggle’s current UI to avoid confusion.