ultralytics 8.4.3

v8.4.3 - `ultralytics 8.4.3` Faster Platform NDJSON downloads (#23257)

on Python PyPI

🌟 Summary (single-line synopsis)

Ultralytics v8.4.3 boosts Ultralytics Platform NDJSON dataset downloads/conversion speed 🚀, improves training metric correctness 🧠, and refreshes defaults/docs around YOLO26 📚.

📊 Key Changes

• 🚀 Faster NDJSON → YOLO dataset conversion (Ultralytics Platform data) — PR #23257 by @glenn-jocher
  • Lazy-loads aiohttp only when NDJSON conversion is used (faster startup, fewer unnecessary deps) 📦
  • Simplifies async image download code and improves concurrency scaling to match dataset size ⚡
  • Small robustness improvement for Platform URL handling 🌐
  • Version bump: 8.4.2 → 8.4.3 🔖
• 🌐 Configurable Platform base URL — PR #23256 by @glenn-jocher
  • Adds ULTRALYTICS_PLATFORM_URL to point callbacks/links to staging or local environments 🧪
• 🏷️ Defaults and examples move to YOLO26 — PR #23242 by @Laughing-q
  • Default YOLO()/CLI fallback model becomes yolo26n.pt and many docs/examples follow suit ✅
• ⚙️ Training optimizer and warmup logic made more reliable — PR #23234 by @Laughing-q
  • Parameter groups are explicitly labeled; warmup LR now targets the bias group by name (not by position) 🔥
  • “Auto” optimizer strategy simplified around MuSGD with improved defaults 🎛️
• 🧍 Pose training logs are more accurate — PR #23230 by @lmycross
  • Only reports rle_loss when the model actually supports it (avoids confusing metrics) 🧾
• 🧩 IMX inference/export consistency improvements — PR #23235 by @Laughing-q
  • Anchor/stride refresh is more robust for changing input shapes; IMX decode path simplified 🔧
• ✅ Fix duplicated Results.summary() entries — PR #23218 by @xusuyong
  • Prevents duplicated rows in summaries (cleaner analytics/logging) 🧹
• 📈 Benchmark tables clarified with end-to-end (e2e) metrics — PR #23238
  • README tables now clearly distinguish e2e evaluation metrics for YOLO26 tasks 📊
• 🍓 Raspberry Pi 5 guide updated with YOLO26 benchmarks — PR #23227
  • Refreshes benchmark formats and adds ExecuTorch results 📌

🎯 Purpose & Impact

• 🚀 If you use Ultralytics Platform datasets (NDJSON): faster, cleaner dataset conversion and downloads means quicker “time-to-train” and fewer download bottlenecks—especially on larger datasets.
• 📦 If you don’t use NDJSON conversion: lazy dependency loading reduces unnecessary imports and can make the package feel lighter/faster in common workflows.
• 🌐 For teams using staging/local Platform: ULTRALYTICS_PLATFORM_URL makes it much easier to test integrations without patching code.
• ⚙️ For training reliability: optimizer/warmup changes reduce edge-case misconfiguration and make training behavior more predictable across models/runs.
• 🧾 For pose users: loss reporting now matches model capability, improving trust in logs/metrics during training.
• 📚 For new users: YOLO26 becomes the “default path” in examples, reducing confusion about which model name to start with.

What's Changed

• Fix: dynamically adapt pose loss names to model type by @lmycross in #23230
• Update auto optimizer strategy and fix mismatched warmup_lr by @Laughing-q in #23234
• Update IMX inference wrapper by @Laughing-q in #23235
• Update README.md metrics table by @Laughing-q in #23238
• Update Raspberry Pi 5 docs with YOLO26 benchmarks by @lakshanthad in #23227
• Use yolo26n in docs and examples by @Laughing-q in #23242
• Remove duplicate result appending logic by @xusuyong in #23218
• Platform URL ENVs by @glenn-jocher in #23256
• ultralytics 8.4.3 Faster Platform NDJSON downloads by @glenn-jocher in #23257

New Contributors

• @xusuyong made their first contribution in #23218

Full Changelog: v8.4.2...v8.4.3