ultralytics 8.4.81

v8.4.81 - Fix `ValueError` in FastSAM `prompt()` with multiple text prompts (#24952)

on Python PyPI

🌟 Summary

This release is mainly a stability and export-quality update for Ultralytics, led by an important FastSAM fix for multiple text prompts 🧠, plus clearer and stricter model export precision handling, better YOLO26 coverage, and several reliability improvements across platforms and integrations 🚀

📊 Key Changes

• 🛠️ FastSAM multiple text prompts no longer crash

  • Fixed a ValueError in FastSAMPredictor.prompt() when users passed more than one text prompt.
  • This especially affected common cases where some small masks were filtered out before prompt matching.
  • The fix vectorizes the index remapping, so lists like ["a photo of a dog", "a photo of a person"] now work as documented.

• 📦 Export precision behavior is now clearer and stricter

  • Export logic was cleaned up to use quantize= consistently instead of relying on older half=True / int8=True internal state.
  • Unsupported precision requests now fail clearly instead of silently changing to something else.
  • Examples:
    • CoreML FP32 stays FP32 when requested
    • CPU TorchScript FP16 now raises a clear error instead of quietly falling back
    • Format-specific precision support is better documented

• 📘 Export docs got a major accuracy upgrade

  • Many integration docs were updated to explain exactly which precisions each format supports, including:
    • CoreML
    • TorchScript
    • OpenVINO
    • MNN
    • RKNN
    • TensorFlow formats
    • ExecuTorch
    • Sony IMX500
  • This makes deployment choices easier and reduces trial-and-error.

• 🤖 YOLO26 became the main export smoke-test model in more places

  • Export smoke tests were updated from YOLO11 to YOLO26 for several formats.
  • This helps ensure the latest recommended model family is tested more directly in CI and export workflows.

• 🧬 Embedding behavior is more intuitive

  • model.embed() and model.predict() now behave better when used back-to-back.
  • After extracting embeddings, a normal model.predict() call returns standard Results again instead of unexpectedly staying in embedding mode.

• ⚡ Faster, safer imports for standard YOLO use

  • SAM/SAM3-related imports were made lazier, so normal Ultralytics imports won’t eagerly load optional torchvision pieces.
  • This fixes startup issues like missing torchvision::nms in environments that don’t need SAM at all.

• 🪟 Windows ONNXRuntime C++ example handles non-ASCII paths better

  • The ONNXRuntime C++ model loader now uses a Windows-safe filesystem path conversion.
  • This improves compatibility for model files stored in folders with UTF-8 or non-English characters.

• 🧩 Axelera export reliability improved

  • Added a numpy<=2.3.5 pin to avoid broken YOLO26 attention-graph exports.
  • Export output validation was tightened so suspiciously tiny models fail loudly instead of being reported as successful.

• 📄 Hailo documentation was clarified

  • The docs now more clearly explain that Hailo HEF is currently a manual ONNX-to-HEF workflow, not yet a direct model.export(format="hailo") target.
  • Notes were expanded around YOLO11, YOLO26, supported tasks, and Hailo hardware/SDK differences.

🎯 Purpose & Impact

• ✅ FastSAM is more usable for natural-language prompting

  • Users working with multiple text prompts can now rely on FastSAM without random crashes.
  • This is the most user-visible fix in the release and especially helpful for prompt-based segmentation workflows.

• 🔒 Exports are more trustworthy

  • If you request a precision the target format cannot actually support, Ultralytics now tells you immediately.
  • This prevents hidden precision mismatches that could lead to confusing model size, speed, or accuracy results.

• 🚀 YOLO26 deployment confidence improves

  • Since YOLO26 is the latest stable and recommended Ultralytics model, broader smoke-test coverage means better confidence when exporting it to different runtimes and hardware.

• 😊 Prediction and embedding workflows feel less surprising

  • Developers switching between feature extraction and normal inference should hit fewer gotchas.

• 🧯 Fewer environment-related failures

  • Lazy SAM imports reduce import-time breakage in setups that only need standard YOLO tasks.
  • Windows path fixes and stricter export validation also reduce frustrating edge-case failures.

• 📚 Better guidance for non-experts and deployers

  • The docs now do a better job of explaining what each export target really supports, helping users choose the right deployment format without guesswork.

Overall, v8.4.81 is a polish-and-reliability release ✨: it fixes an important FastSAM bug, makes export precision behavior much safer, and strengthens YOLO26 deployment readiness across the stack.

What's Changed

• Fix export precision handling and docs by @glenn-jocher in #24950
• Fix Sentry bugs 2026-06-27 by @glenn-jocher in #24949
• Remove internal half int8 export state by @glenn-jocher in #24951
• Fix ONNXRuntime C++ model path on Windows by @glenn-jocher in #24953
• Avoid bare alls variable in Hailo docs by @glenn-jocher in #24954
• Update Hailo docs compatibility notes by @glenn-jocher in #24955
• Pin Axelera numpy and remove redundant export tests by @glenn-jocher in #24947
• Update export smoke models to YOLO26 by @glenn-jocher in #24957
• Fix embed state reset for predictions by @glenn-jocher in #24958
• Fix YOLO26 Axelera export output path by @glenn-jocher in #24956
• Reduce slow CI Jetson TensorRT coverage by @glenn-jocher in #24959
• Fix ValueError in FastSAM prompt() with multiple text prompts by @ahmet-f-gumustas in #24952

Full Changelog: v8.4.80...v8.4.81