🌟 Summary Cleaner post-training behavior and clearer visuals: v8.3.205 refines how training configs are restored from checkpoints, improves fitness plots with smarter outlier filtering, and updates docs for more predictable inference and easy-start notebooks. ✅ 📊 Key Changes Reset checkpoint overrides after training (@Y-T-G) 🔁 Trainer now restores overrides via a new helper (_reset_ckpt_args) instead of using raw model.args. Fixes a community-reported issue where results could be saved to unintended run directories. Smarter Tune scatterplots (@glenn-jocher) 📈 Applies iterative 3-sigma rejection on low outliers to produce clearer, more reliable fitness plots and best-run selection. CI reliability boost (@glenn-jocher) 🔄 Pytest wrapped in a retryable GitHub Action with a single automatic retry to reduce flaky failures. Inference docs: rect padding clarified (@Y-T-G) 🧠 Added note explaining default minimal padding behavior during predict, and how batch size and image sizes affect padding. New one-click training for Construction-PPE dataset (@RizwanMunawar) 🚀 Added a Colab badge to quickly launch a ready-to-run notebook. SAM docs link updated (@onuralpszr) 🔗 Now points to the official Segment Anything GitHub for accurate, up-to-date references. Useful links: Current PR: Reset checkpoint arguments after training Plotting improvements: 3-sigma outlier rejection for Tune plots CI stability: Retry slow CI tests once Inference behavior note: Predict mode docs update Construction-PPE notebook: Open the Colab tutorial SAM reference: Segment Anything GitHub 🎯 Purpose & Impact More reliable training workflows 🛠️ Prevents stale or unintended args from leaking from checkpoints—reducing surprises when resuming or finalizing training and fixing incorrect results directory issues. Clearer insights during tuning/evolution 📊 Outlier filtering makes plots easier to read and improves the stability of best-run identification. Predictable inference behavior 🧩 Better understanding of minimal vs. square padding helps you plan for memory, speed, and consistent outputs when running predict. Faster onboarding and experimentation 🚀 One-click Colab for the Construction-PPE dataset lowers the barrier for training YOLO models without local setup. Stable development pipeline ✅ CI retries reduce flaky failures, improving contributor experience and build reliability. What's Changed Docs: Add rect behavior note to predict.md by @Y-T-G in https://github.com/ultralytics/ultralytics/pull/22266 Add Construction-PPE notebook in docs by @RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/22269 docs: 📝 Update link to Segment Anything GitHub in SAM documentation by @onuralpszr in https://github.com/ultralytics/ultralytics/pull/22268 Apply 3-sigma iterative rejection to Tune scatterplots by @glenn-jocher in https://github.com/ultralytics/ultralytics/pull/22290 Retry Slow CI tests once by @glenn-jocher in https://github.com/ultralytics/ultralytics/pull/22292 ultralytics 8.3.205 Reset checkpoint arguments after training by @Y-T-G in https://github.com/ultralytics/ultralytics/pull/22286 Full Changelog: https://github.com/ultralytics/ultralytics/compare/v8.3.204...v8.3.205

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