v0.1.0a3

Summary

This pre-release adds powerful new capabilities for high-performance inference and post-processing:

• ONNX/TensorRT Export: Export trained models to optimized formats for 3-6x faster inference
• Post-Inference Filtering: Remove overlapping/duplicate predictions using IOU or OKS similarity
• Improved WandB Logging: Better metrics organization and run naming

For the full list of major features, breaking changes, and improvements introduced in the v0.1.0 series, see the v0.1.0a0 release notes.

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What's New in v0.1.0a3

Features

ONNX/TensorRT Export Module (#418)

A complete model export system for high-performance inference:

# Export to ONNX
sleap-nn export /path/to/model -o exports/my_model --format onnx

# Export to both ONNX and TensorRT FP16
sleap-nn export /path/to/model -o exports/my_model --format both

# Run inference on exported model
sleap-nn predict exports/my_model video.mp4 -o predictions.slp

Performance Benchmarks (NVIDIA RTX A6000):

Batch size 1 (latency-optimized):

Model	Resolution	PyTorch	ONNX-GPU	TensorRT FP16	Speedup
single_instance	192×192	1.8 ms	1.3 ms	0.31 ms	5.9x
centroid	1024×1024	2.5 ms	2.7 ms	0.77 ms	3.2x
topdown	1024×1024	11.4 ms	9.7 ms	2.31 ms	4.9x
bottomup	1024×1280	12.3 ms	9.6 ms	2.52 ms	4.9x
multiclass_topdown	1024×1024	8.3 ms	9.1 ms	1.84 ms	4.5x
multiclass_bottomup	1024×1024	9.4 ms	9.4 ms	2.64 ms	3.6x

Batch size 8 (throughput-optimized):

Model	Resolution	PyTorch	ONNX-GPU	TensorRT FP16	Speedup
single_instance	192×192	3,111 FPS	3,165 FPS	11,039 FPS	3.5x
centroid	1024×1024	453 FPS	474 FPS	1,829 FPS	4.0x
topdown	1024×1024	94 FPS	122 FPS	525 FPS	5.6x
bottomup	1024×1280	113 FPS	121 FPS	524 FPS	4.6x
multiclass_topdown	1024×1024	127 FPS	145 FPS	735 FPS	5.8x
multiclass_bottomup	1024×1024	116 FPS	120 FPS	470 FPS	4.1x

Speedup is relative to PyTorch baseline.

Supported model types:

• Single Instance, Centroid, Centered Instance
• Top-Down (combined centroid + instance)
• Bottom-Up (multi-instance with PAF grouping)
• Multi-class Top-Down and Bottom-Up (with identity classification)

New CLI commands:

• sleap-nn export - Export models to ONNX/TensorRT
• sleap-nn predict - Run inference on exported models

New optional dependencies:

uv pip install "sleap-nn[export]"      # ONNX CPU inference
uv pip install "sleap-nn[export-gpu]"  # ONNX GPU inference
uv pip install "sleap-nn[tensorrt]"    # TensorRT support

See the Export Guide for full documentation.

Post-Inference Filtering for Overlapping Instances (#420)

New capability to remove duplicate/overlapping pose predictions after model inference:

# Filter with IOU method (default)
sleap-nn track -i video.mp4 -m model/ --filter_overlapping

# Use OKS method with custom threshold
sleap-nn track -i video.mp4 -m model/ \
    --filter_overlapping \
    --filter_overlapping_method oks \
    --filter_overlapping_threshold 0.5

New CLI options for sleap-nn track:

Option	Default	Description
--filter_overlapping	False	Enable filtering using greedy NMS
--filter_overlapping_method	iou	Similarity method: iou (bbox) or oks (keypoints)
--filter_overlapping_threshold	0.8	Similarity threshold (lower = more aggressive)

Programmatic API:

from sleap_nn.inference.postprocessing import filter_overlapping_instances

labels = filter_overlapping_instances(labels, threshold=0.5, method="oks")

Why use this? Previously, IOU-based filtering only existed in the tracking pipeline. This feature allows filtering overlapping predictions without requiring --tracking.

Improvements

WandB Run Naming and Metrics Logging (#417)

• Fixed run naming: WandB runs now correctly use auto-generated run names
• Improved metrics organization: All metrics use / separator for automatic panel grouping in WandB UI:
  • train/loss, train/lr - Training metrics (epoch x-axis)
  • val/loss - Validation metrics (epoch x-axis)
  • eval/val/ - Epoch-end evaluation metrics
  • eval/test.X/ - Post-training test set metrics
• New metrics logged:
  • train/lr - Learning rate (useful for monitoring LR schedulers)
  • PCK@5, PCK@10 - PCK at 5px and 10px thresholds
  • distance/p95, distance/p99 - Additional distance percentiles

Documentation

• Exporting Guide (#419): Added comprehensive export documentation to How-to guides navigation

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Installation

This is an alpha pre-release. Pre-releases are excluded by default per PEP 440 - you must explicitly opt in.

Install with uv (Recommended)

# With --prerelease flag (requires uv 0.9.20+)
uv tool install sleap-nn[torch] --torch-backend auto --prerelease=allow

# Or pin to exact version
uv tool install "sleap-nn[torch]==0.1.0a3" --torch-backend auto

Run with uvx (One-off execution)

uvx --from "sleap-nn[torch]" --prerelease=allow --torch-backend auto sleap-nn system

Verify Installation

sleap-nn --version
# Expected output: 0.1.0a3

sleap-nn system
# Shows full system diagnostics including GPU info

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Upgrading from v0.1.0a2

If you already have v0.1.0a2 installed with --prerelease=allow:

# Simple upgrade (retains original settings like --prerelease=allow)
uv tool upgrade sleap-nn

To force a complete reinstall:

uv tool install sleap-nn[torch] --torch-backend auto --prerelease=allow --force

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Changelog

PR	Category	Title
#417	Improvement	Fix wandb run naming and improve metrics logging
#418	Feature	Add ONNX/TensorRT export module
#419	Documentation	Add Exporting guide to How-to guides section
#420	Feature	Add post-inference filtering for overlapping instances

Full Changelog: v0.1.0a2...v0.1.0a3