Title: Ultralytics v8.3.205 — cleaner post‑training behavior, clearer tuning plots, predictable inference 
Summary
Ultralytics v8.3.205 focuses on reliability and clarity. Training now restores configs from checkpoints more safely, Tune plots are cleaner via robust outlier filtering, and inference docs better explain padding so you get predictable results. Plus, we’ve added a one‑click Construction‑PPE training notebook and refreshed our SAM references. 
Highlights
- More reliable post‑training behavior with safe checkpoint override resets
- Clearer Tune scatterplots using iterative low‑outlier rejection
- Predictable inference with clarified padding behavior
- One‑click training notebook for the Construction‑PPE dataset
- CI is more resilient to flaky tests
New Features
- One‑click training for Construction‑PPE: start fast with the ready‑to‑run notebook in the update Open the Colab tutorial authored by @RizwanMunawar. This is a great way to try YOLO11 training without local setup.
Improvements
- Reset checkpoint overrides after training: the change in Reset checkpoint arguments after training by @Y-T-G introduces a dedicated
_reset_ckpt_argshelper to restore overrides safely, preventing results from being written to unintended run directories. - Smarter Tune scatterplots: the plotting refinement in 3-sigma outlier rejection for Tune plots by @glenn-jocher applies iterative rejection of low outliers, yielding clearer fitness plots and more reliable best‑run identification.
- CI reliability boost: the workflow tweak in Retry slow CI tests once by @glenn-jocher wraps pytest in a retryable action to reduce flaky failures.
Documentation
- Predict padding clarified: the note in Predict mode docs update by @Y-T-G explains default minimal padding behavior during
predict, and howimgszand batch size influence memory, speed, and output consistency. - SAM reference updated: the link refresh in SAM documentation update by @onuralpszr now points to the official Segment Anything GitHub for accurate, up‑to‑date references.
Why it matters
- More reliable training workflows: safe restoration of checkpoint arguments reduces surprises when resuming or finalizing training and fixes incorrect results directory issues.
- Clearer insights during tuning: outlier‑filtered plots are easier to read and improve the stability of best‑run selection.
- Predictable inference behavior: clarified padding helps you plan for memory and speed while keeping results consistent.
- Faster onboarding: the Construction‑PPE notebook offers a frictionless way to train YOLO models with zero local setup.
- Stable development pipeline: CI retries cut down on noisy failures for a smoother contributor experience.
Get started
- Upgrade
pip install -U ultralytics
- Quick predict with predictable padding (YOLO11 recommended)
from ultralytics import YOLO
model = YOLO('yolo11n.pt') # latest recommended default
results = model.predict(source='path/to/images', imgsz=640, batch=1) # minimal, predictable padding
- Resume training safely (checkpoint overrides handled automatically)
yolo train model=yolo11n.pt data=coco128.yaml epochs=50 resume=True
Release links
- Explore the details on the v8.3.205 release page.
- Review every change in the full changelog diff between v8.3.204 and v8.3.205.
We’d love your feedback
Give this release a try and let us know how it goes. Share ideas, questions, or issues in Ultralytics Discussions. Your input helps the YOLO community and the Ultralytics team keep improving—thank you!