ultralytics 8.4.88

v8.4.88 - Cap dataloader workers for tiny datasets (#25031)

on Python PyPI

🌟 Summary

Ultralytics v8.4.88 focuses on more reliable training for tiny datasets, stable NCNN export, and clearer documentation for model training concepts. 🚀

📊 Key Changes

• Tiny dataset dataloader fix by @glenn-jocher 🧠

  • Dataloader worker processes are now capped based on the actual number of final batches.
  • Single-batch training loaders now run in-process instead of spawning extra persistent workers.
  • Empty dataset handling now correctly relies on PyTorch DataLoader validation instead of triggering internal worker-count errors.
  • Added regression tests for tiny loaders, distributed sampling with drop_last, and empty dataset behavior.

• Improved NCNN export stability by @glenn-jocher 🛠️

  • PNNX is pinned to 20260526 for NCNN export and CI checks.
  • This avoids a known newer PNNX regression that could cause NCNN inference crashes.
  • NCNN remains paired with the latest available ncnn wheel.

• Clearer training documentation for dfl by @fcakyon 📚

  • The dfl training argument description now correctly explains Distribution Focal Loss as a bounding box localization term, not a classification feature.
  • This helps users better understand how YOLO models learn accurate box positions.

• Knowledge distillation guide visual update by @RizwanMunawar 🖼️

  • Added a new workflow image to the knowledge distillation documentation.
  • The image helps explain the teacher-student model training concept more intuitively.

• Version bump 📦

  • Package version updated from 8.4.87 to 8.4.88.

🎯 Purpose & Impact

• Better experience for small experiments and quick tests ⚡
  Users training on very small datasets should see fewer unnecessary background worker processes, lower overhead, and fewer cases where stuck workers keep CUDA resources busy.

• More robust distributed and edge-case training behavior ✅
  The dataloader update preserves existing drop_last behavior while improving worker handling after sampler construction, making training safer across normal, tiny, and distributed dataset setups.

• More dependable NCNN deployment workflows 📱
  Pinning PNNX improves reliability for users exporting YOLO models, including YOLO26 models, to NCNN for lightweight or edge-device inference.

• Clearer learning resources for all users 🌐
  Documentation updates make advanced concepts like knowledge distillation and Distribution Focal Loss easier to understand for both new users and experienced developers.

What's Changed

• Add cover image for knowledge-distillation.md by @RizwanMunawar in #25026
• Pin PNNX for NCNN export stability by @glenn-jocher in #25028
• Clarify DFL argument describes box localization not classification by @fcakyon in #25029
• Cap dataloader workers for tiny datasets by @glenn-jocher in #25031

Full Changelog: v8.4.87...v8.4.88