Skip to content

🚀 SAHI v0.12.0 Batch Inference, Torch-Free Core & New Open-Vocabulary Models

Choose a tag to compare

View all tags
@onuralpszr onuralpszr released this 31 May 22:47
· 37 commits to main since this release
0.12.0
2a860bd
This commit was created on GitHub.com and signed with GitHub’s verified signature.
GPG key ID: B5690EEEBB952194
Verified
Learn about vigilant mode.

Sliced Aided Hyper Inference detect the small stuff in big images.

This is one of the largest SAHI releases to date: 95 commits since 0.11.34 (rolling in the 0.11.35/0.11.36 hotfixes), spanning a re-architected post-processing engine, true batch inference, a lighter torch-free core, six new model families, and a full documentation overhaul.

🚀 Highlights

⚡ Batch inference, torch-free core & accelerated post-processing backends (#1336)

A ground-up reworking of how SAHI runs and merges predictions:

  • Batch inference slices are now processed in batches end-to-end, dramatically improving throughput on GPU.
  • Torch-free core the core slicing/ post-processing path no longer hard-depends on PyTorch. Install only what your model backend needs.
  • Pluggable post-processing backends NMS / NMM now run on a selectable backend:
    • NumPy zero heavy deps, runs anywhere
    • Numba JIT-accelerated CPU path for large slice counts
    • TorchVision GPU-accelerated when torch is available
    • Backend auto-selects based on what's installed, and can be forced explicitly.

🎚️ Finer control over slicing & post-processing

  • force_postprocess_type in get_sliced_prediction for explicit control over how overlapping detections are merged (#1346).
  • Per-call confidence_threshold override across the prediction APIs tune confidence without rebuilding the model object (#1352).
  • Progress bar + progress callback for get_sliced_prediction, available in both the Python API and CLI (#1255).

🧠 New model support

  • GroundingDINO (HuggingFace) zero-shot, text-prompted open-vocabulary detection. Describe what you're looking for in natural language and run it through SAHI's sliced pipeline perfect for finding small, rare, or unlabeled objects in large images ships with a dedicated demo notebook (#1361).
  • Universal segmentation from HuggingFace run any HF universal/panoptic segmentation model through SAHI's sliced pipeline (#1360).
  • RF-DETR-Seg segmentation variant of RF-DETR (#1315).
  • YOLOE detection model (#1268).
  • YOLO-World open-vocabulary detection model (#1267).
  • YOLO26 support across the Ultralytics backend, CLI, docs, and notebooks (#1321, #1322, #1356).

📚 Documentation overhaul

  • Migrated docs to Zensical with full code typing & formatting cleanup (#1344).
  • Chinese (zh) translation of the documentation added and kept in sync (#1253, #1332, #1347).
  • New API reference, ** post-processing backends guide**, security policy, and Code of Conduct (#1257, #1336, #1272, #1349).

✨ Performance & Improvements

  • Significantly faster post-processing NMS, NMM, and GREEDYNMM now use a shapely STRtree spatial index, dramatically speeding up merging on images with many slices/detections (#1248). Thanks @nikvo1!
  • Faster read_image_as_pil for quicker slicing throughput (#1353).
  • Improved performance & resource management in prediction and slicing (#1263).
  • Better nms performance with correct handling of empty predictions (#1288).
  • Replaced pybboxes with a lightweight in-house yolo_bbox_to_voc_bbox (#1320).
  • Dropped the pybboxes and pinned opencv-python version constraints for cleaner installs (#1325).

🐞 Bug Fixes

  • Fixed empty bounding boxes caused by an empty shapely_annotation.multipolygon (#1140).
  • Fixed invalid segmentation masks for Detectron2 models (#1262).
  • Corrected margin calculation in BoundingBox (#1286).
  • Fixed CHW-format image handling in read_image_as_pil (#1287).
  • Validate overlap ratios in get_slice_bboxes (must be < 1.0) (#1285).
  • Corrected error message for invalid model path in RTDetrDetectionModel (#1266).
  • Fixed incorrect type annotations in the postprocess module (#1327).
  • Ultralytics model now supports additional formats with improved task handling (#1321).
  • Added pywinpty for Windows dev compatibility (#1319).

🧹 Maintenance & CI

  • Pinned all GitHub Actions to commit SHAs for supply-chain security (#1351).
  • Multi-OS CI matrix and clearer workflow naming (#1334).
  • Bumped to Python 3.12/3.13 in CI and docs (#1259, #1260).
  • Removed deprecated YOLOv5 helpers, legacy requirements.txt, MMDet workflow, and unused Netlify config (#1326, #1342, #1341, #1335).
  • numpy<3.0, torchvision 0.23.0, and many dependency bumps via Dependabot (now also covering pip).

🙏 Contributors

Thanks to everyone who contributed to this release:
@onuralpszr, @fcakyon, @siromermer, @ZephyrKeXiner, @yogendrasinghx, @srikrishnavignesh, @ibuldakov, @ducviet00, @volks73, @RizwanMunawar, @nikvo1, @vinnik-dmitry07, and @gboeer and @dependabot for keeping dependencies fresh.

🌟 New Contributors

Full Changelog: 0.11.34...0.12.0

Contributors

gboeer, onuralpszr, and 12 other contributors
Assets 2
Loading
1 Join discussion