ultralytics 8.4.28

v8.4.28 - `ultralytics 8.4.28` Limit autobatch to dataset size (#24020)

on Python PyPI

🌟 Summary

Ultralytics v8.4.28 improves training reliability on small datasets by making autobatch smarter, while also strengthening network robustness, backend efficiency, and docs/test stability. 🚀

📊 Key Changes

• (Most important) Autobatch now respects dataset size 📦
  From PR #24020 by @glenn-jocher: automatic batch-size selection is now capped to the number of training images.

  • Added dataset_size through Trainer.auto_batch(), check_train_batch_size(), and autobatch().
  • Autobatch profiling now skips candidate batch sizes larger than the dataset.
  • Final batch recommendation is clamped as a safety check.

• More reliable dataset/platform networking 🌐🔁
  From PR #24010 by @glenn-jocher:

  • Smarter retries for transient download failures (timeouts, connection issues, rate limiting, server errors).
  • Faster failure on non-retryable issues (like local save errors).
  • Platform URL resolution retries increased (3 → 5) with clearer retry behavior and backoff logging.

• TensorFlow backend import optimization ⚡
  From PR #24017 by @Y-T-G:

  • TensorFlow is now imported only for saved_model and pb formats in the TensorFlow backend loader.
  • Avoids unnecessary dependency loading in unrelated inference paths.

• Test reliability improvement for YOLO26 OBB CI 🧪
  From PR #24011 by @glenn-jocher:

  • Updated a test image URL (boats.jpg) to a fixed jsDelivr CDN path for more consistent test behavior.

• Ultralytics Platform docs updated to match current UI 📘
  From PR #24014 by @mykolaxboiko:

  • Removed outdated Activity tab references in model docs.
  • Expanded onboarding docs to cover all tours and restart options.

🎯 Purpose & Impact

• Better small-dataset training defaults ✅
  Users training on tiny datasets are less likely to get impractical batch recommendations, reducing setup friction and odd edge-case behavior.

• More resilient cloud/data workflows 🛡️
  Dataset downloads and platform URL resolution should fail less often on temporary network issues, especially in CI or unstable network environments.

• Cleaner runtime behavior 🧹
  Conditional TensorFlow imports can reduce startup overhead and avoid avoidable environment-related issues.

• Improved developer experience 👩‍💻
  More stable tests and clearer docs mean smoother onboarding, troubleshooting, and release confidence.

What's Changed

• NDJSON and Platform retries and timeouts improvements by @glenn-jocher in #24010
• Use jsdelivr CDN assets URL for boats.jpg reliability by @glenn-jocher in #24011
• Scope Tensorflow import in TensorflowBackend by @Y-T-G in #24017
• Update Platform docs: remove model Activity tab, expand onboarding tours by @mykolaxboiko in #24014
• ultralytics 8.4.28 Limit autobatch to dataset size by @glenn-jocher in #24020

Full Changelog: v8.4.27...v8.4.28