0.11.22

[fcakyon]

What's Changed

• Improve suppot for latest mmdet (v3.3.0) by @fcakyon in Improve suppot for latest mmdet (v3.3.0) #1129
• Improve support for latest yolov5-pip and ultralytics versions by @fcakyon in Improve support for latest yolov5-pip and ultralytics versions #1130
• support latest huggingface/transformers models by @fcakyon in support latest huggingface/transformers models #1131
• refctor coco to yolo conversion, update docs by @fcakyon in refctor coco to yolo conversion, update docs #1132
• bump version by @fcakyon in bump version #1134

Full Changelog: 0.11.21...0.11.22

Core Documentation Files

Prediction Utilities

• Detailed guide for performing object detection inference
• Standard and sliced inference examples
• Batch prediction usage
• Class exclusion during inference
• Visualization parameters and export formats
• Interactive examples with various model integrations (YOLOv8, MMDetection, etc.)

Slicing Utilities

• Guide for slicing large images and datasets
• Image slicing examples
• COCO dataset slicing examples
• Interactive demo notebook reference

COCO Utilities

• Comprehensive guide for working with COCO format datasets
• Dataset creation and manipulation
• Slicing COCO datasets
• Dataset splitting (train/val)
• Category filtering and updates
• Area-based filtering
• Dataset merging
• Format conversion (COCO ↔ YOLO)
• Dataset sampling utilities
• Statistics calculation
• Result validation

CLI Commands

• Complete reference for SAHI command-line interface
• Prediction commands
• FiftyOne integration
• COCO dataset operations
• Environment information
• Version checking
• Custom script usage

FiftyOne Integration

• Guide for visualizing and analyzing predictions with FiftyOne
• Dataset visualization
• Result exploration
• Interactive analysis

Interactive Examples

All documentation files are complemented by interactive Jupyter notebooks in the demo directory:

• slicing.ipynb - Slicing operations demonstration
• inference_for_ultralytics.ipynb - YOLOv8/YOLO11/YOLO12 integration
• inference_for_yolov5.ipynb - YOLOv5 integration
• inference_for_mmdetection.ipynb - MMDetection integration
• inference_for_huggingface.ipynb - HuggingFace models integration
• inference_for_torchvision.ipynb - TorchVision models integration
• inference_for_rtdetr.ipynb - RT-DETR integration
• inference_for_sparse_yolov5.ipynb - DeepSparse optimized inference

Getting Started

If you're new to SAHI:

1. Start with the prediction utilities to understand basic inference
2. Explore the slicing utilities to learn about processing large images
3. Check out the CLI commands for command-line usage
4. Dive into COCO utilities for dataset operations
5. Try the interactive notebooks in the demo directory for hands-on experience

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This discussion was created from the release 0.11.22.