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v8.4.26 - Platform NDJSON autosplit (#23990)

🌟 Summary

v8.4.26 focuses on reliability and usability improvements: smarter Platform dataset handling (auto-validation split for NDJSON), more robust Platform URL resolution, and an important FP16 SAM inference crash fix—plus CI and docs polish. 🚀

📊 Key Changes

• ✅ Platform NDJSON auto-split (PR #23990, @​glenn-jocher)
  If a dataset has a train split but no val/test, Ultralytics now automatically creates a small validation split from training data instead of failing immediately.

  • Uses a deterministic split approach for consistency.
  • Warns users to still create a manual validation split for best quality.

• 🌐 More reliable Ultralytics Platform URI resolution (PR #23990, @​glenn-jocher)
  Improved ul://... resolution with:

  • Retry logic for transient connection issues
  • Better timeout strategy (fast connect timeout + longer read timeout for large dataset processing)
  • Clearer handling for auth, permission, not-found, and processing states

• 🧠 FP16 SAM TinyViT inference crash fix (PR #23780, @​Edwin-Kevin)
  Fixed a half-precision inference error in SAM TinyViT models (like mobile_sam.pt) caused by dtype mismatch in cached tensors.

  • Model setup order was adjusted so cached eval-time tensors match FP16 expectations.
  • Added/updated CUDA test coverage with half=True.

• ⚙️ CI simplification and stability updates (mainly PR #23990)

  • Removed dedicated HUB CI job and related manual trigger
  • Standardized many CI runners from cpu-latest to ubuntu-latest
  • Temporarily pinned GPU CI to torch<2.11 until CUDA 13 driver support is ready

• 📚 Platform docs improvements

  • Added embedded onboarding videos to key Platform docs pages (Quickstart, Account, Data, Train, Deploy) via PR #23986 by @​RizwanMunawar
  • Updated docs chat script to v0.2.7 via PR #23989 by @​glenn-jocher

🎯 Purpose & Impact

• Less friction for dataset conversion 📦
  Users importing NDJSON datasets into YOLO workflows are less likely to hit hard failures when a validation split is missing.

• Better reliability in cloud/remote workflows ☁️
  Platform URI retries and timeout tuning reduce flaky failures, especially with large datasets or temporary network instability.

• More stable FP16 segmentation inference 🎯
  Users running SAM TinyViT in half precision on GPU should see fewer runtime dtype crashes and smoother deployment behavior.

• Cleaner maintenance pipeline for Ultralytics 🛠️
  CI changes reduce complexity and improve consistency, helping keep releases stable.

• Faster onboarding for new users 🎥
  The new Platform tutorial videos make it easier for broad audiences to get started quickly with data, training, and deployment.

What's Changed

• Add Platform videos to docs by @​RizwanMunawar in ultralytics/ultralytics#23986
• Fix FP16 inference crash from TinyViT cached bias dtype mismatch by @​Edwin-Kevin in ultralytics/ultralytics#23780
• Update chat.js v0.2.7 by @​glenn-jocher in ultralytics/ultralytics#23989

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• 7b3de51 ultralytics 8.4.50 New Export Integration: Deepx (#23553)
• 6a04d7c Fix dtype mismatch in fuse_conv_and_bn and fuse_deconv_and_bn (#24480)
• c025234 Docs: add RT-DETE eval_idx instruction (#24465)
• 077d20c Fix tuner reporting iterations complete when every iteration failed (#24477)
• a5ed6af ultralytics 8.4.49 Fail fast on three common training failure modes (#24478)
• 6300d16 Remove flaky DOI badges from docs badge arrays (#24476)
• aa03683 docs: add export install tip for benchmarks (#24464)
• d46b51b Fix docs language banner links (#24474)
• fb9e82f Remove unused frame_rate in BYTETracker/BOTSORT (#24457)
• 166604e Fix Tuner._mutate RNG collisions for sub-second iterations (#24451)
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