ultralytics 8.4.160

v8.4.160 - `ultralytics 8.4.160` Invalidate dataset caches when files are edited in place (#26283)

on Python PyPI

🌟 Summary

Ultralytics 8.4.160 makes dataset edits less likely to go unnoticed and improves memory use, training reliability, and everyday API behavior—without introducing a new model architecture.

📊 Key Changes

• Dataset cache invalidation (PR #26283): Cache keys now account for each file’s size and modification time, so editing a label in place—such as changing its class without changing its length—triggers a rescan. Detection, depth, semantic, and other shared-cache users benefit.
• Lower peak VRAM for SAM3 text prompting: Text prompts are processed in chunks of up to 16, helping reduce memory use on large prompt sets and avoid out-of-memory errors.
• More reliable training after out-of-memory errors: When training retries with a smaller batch size, it now preserves optimizer and scheduler state instead of restarting optimizer state.
• Clearer validation dataset selection: Validation uses the checkpoint’s recorded dataset when it can be found; otherwise, it warns and falls back to the task’s default dataset. Users can specify data to choose a dataset explicitly. Depth calibration now requires data.
• Lower host RAM during TensorRT 11 INT8 calibration: Calibration releases consumed batch data and normalizes in place, reducing peak host memory without changing calibration values.
• Safer and more convenient utilities: CUDA-backed results can now convert to NumPy; NMS no longer modifies raw prediction tensors; and box-cropping, image-to-label path conversion, and repeated downloads of single-file ZIPs handle common inputs more reliably.
• Documentation improvements: Task guides clarify validation behavior, result fields, dataset formats, and training options, including cloud training.

🎯 Purpose & Impact

• More trustworthy dataset updates: Changed labels are less likely to remain silently stale in cached datasets. Existing caches will rescan once after upgrading; if a file’s size and modification time are both preserved, remove the affected cache manually.
• Better usability across hardware and workflows: SAM3 can handle more text prompts within available GPU memory, while TensorRT 11 calibration can use less host RAM.
• More dependable training and inference: OOM retries retain learned optimizer state, and repeated NMS calls no longer risk corrupting the original predictions.
• Fewer surprising API failures: CUDA results and common Python path or box inputs work more consistently.
• Less ambiguity in validation: Checkpoint dataset settings are reused when available, with a warning and sensible fallback when they are not.

What's Changed

• Add https://youtu.be/VxP9kHUwE6o to docs by @RizwanMunawar in #26274
• Reduce peak VRAM for SAM3 text prompting via chunked grounding by @Laughing-q in #26281
• Validate on the checkpoint's dataset when val gets no data and require data= in calibrate() by @raimbekovm in #26277
• Reduce host memory during TensorRT 11 INT8 calibration by @amanharshx in #26275
• Fix Results.numpy() for CUDA-backed results by @aswanth-07 in #26282
• Fix repeated safe_download of single-file ZIP archives by @Nikhi00718 in #26273
• Stop non_max_suppression() from mutating input predictions by @Nikhi00718 in #26278
• Fix save_one_box() for list, tuple and NumPy boxes by @MohammadHijjawi97 in #26279
• Accept pathlib.Path inputs in img2label_paths() by @MohammadHijjawi97 in #26280
• Preserve optimizer state when OOM auto-reduces batch size by @Nikhi00718 in #26284
• Unify task docs structure and align val and Results guidance by @raimbekovm in #26271
• ultralytics 8.4.160 Invalidate dataset caches when files are edited in place by @aswanth-07 in #26283

New Contributors

• @MohammadHijjawi97 made their first contribution in #26279

Full Changelog: v8.4.159...v8.4.160