ultralytics 8.4.77

v8.4.77 - Add knowledge distillation for YOLO detection models (#24894)

on Python PyPI

🌟 Summary

Ultralytics v8.4.77 is headlined by new knowledge distillation support 🎓 for YOLO training, helping smaller models learn from larger ones to boost accuracy without slowing down inference, alongside a useful RegionCounter crash fix and clearer solution docs 📚.

📊 Key Changes

• 🧠 Knowledge distillation added for YOLO training

  • New training support lets a smaller student model learn from a larger teacher model using the new distill_model argument.
  • A new dis setting controls how strongly the teacher guides training.
  • Added a new internal DistillationModel wrapper to handle:
    • frozen teacher models
    • trainable student models
    • feature matching between models
    • combined standard + distillation loss
  • Includes docs, tests, resume-training support, and checkpoint handling.

• 🚀 New Knowledge Distillation guide

  • Added a dedicated guide with:
    • Python and CLI quickstarts
    • recommended YOLO26 teacher/student pairs
    • usage notes for detect, segment, pose, and OBB
    • benchmark tables for distilled YOLO26 models
    • FAQs and training tips

• 📈 Potential accuracy gains for compact YOLO26 models

  • Release notes show improved validation results for distilled YOLO26 models, while keeping the deployed model size and runtime essentially the same as the original student model.

• 🛠️ RegionCounter crash fixed

  • Fixed a bug where regions added with add_region() could crash during processing because required polygon preparation data was missing.
  • Regions are now fully prepared when added, making RegionCounter more reliable.

• 📚 Documentation refresh for real-time solutions

  • Several YOLO26 guides were reorganized to be easier to follow, including:
    • analytics
    • object counting
    • object blurring
    • object cropping
    • speed estimation
    • distance calculation
  • The analytics guide now also documents area plots.

• 🔎 Documentation SEO/title improvements

  • Fixed duplicate page title issues for some dataset and Roboflow-related docs pages, helping users find the right content more easily.

🎯 Purpose & Impact

• 🎯 Better small-model performance

  • The biggest change is knowledge distillation: it helps smaller YOLO models become more accurate by learning from stronger models during training.
  • This is especially useful for users who want fast, lightweight deployment on edge devices or production systems.

• ⚡ No extra inference cost

  • Distillation improves training, not deployment overhead. That means users can often get a better student model without making inference slower.

• 🧪 More robust training workflows

  • Support for tests, resume behavior, and cleaner checkpoint handling makes distillation more practical for real-world experiments and longer training jobs.

• 🧰 More stable solution usage

  • The RegionCounter fix removes a frustrating crash path, especially for users creating regions programmatically instead of only through predefined setup.

• 📖 Easier onboarding

  • The updated guides make common computer vision tasks more approachable for new users, while also giving experienced users clearer references and cleaner examples.

• 🌍 Better discoverability

  • The docs metadata/title fixes improve site organization and search visibility, which helps users reach the right Ultralytics resources faster.

If you'd like, I can also turn this into a short release note version or a more technical developer-focused summary.

What's Changed

• fix(docs): resolve duplicate title tags on Roboflow and dataset overview pages by @miles-deans-ultralytics in #24917
• Restructure real-time solution guides and condense duplicated FAQ examples by @raimbekovm in #24910
• Fix RegionCounter crash when regions added via add_region by @raimbekovm in #24892
• Add knowledge distillation for YOLO detection models by @lmycross in #24894

Full Changelog: v8.4.76...v8.4.77