Summary

• Adds Ultralytics YOLO26 object detection models (n/s/m/l/x, 2.4-58.9M params) as a contrib model for real-time inference on AWS Trainium2 and Inferentia2 via torch_neuronx.trace()
• All 5 detection variants compile and run with high accuracy (CosSim 0.987-0.997), plus pose and OBB task heads
• Neuron outperforms compiled A10G GPU by 1.4-4.5x on s/m/l/x variants at peak DP throughput

Validation

Validated on 4 configurations: trn2.3xlarge × {SDK 2.28, 2.29} and inf2.xlarge × {SDK 2.28, 2.29}.

Peak Throughput (trn2.3xlarge, LNC=1, DP=8)

Files

contrib/models/YOLO26/
  README.md                          # Model card, benchmarks, compatibility matrix
  yolo26_neuron_notebook.ipynb       # Complete workflow notebook (tested end-to-end)
  src/
    __init__.py                      # Exports: YOLO26NeuronModel, compile_yolo26, etc.
    modeling_yolo26.py               # Trace wrapper, DP support, validation (~280 lines)
  test/
    __init__.py
    unit/__init__.py
    integration/
      __init__.py
      test_model.py                  # 13 integration tests (compile, accuracy, DP, perf)

Key Design Decisions

• torch_neuronx.trace() (not NxDI model classes): YOLO26 is a CNN with no KV cache, no attention matrices, no token generation. All variants fit on a single NeuronCore (<180 MB NEFF). Data Parallelism provides throughput scaling.
• end2end=False: topk/sort operations are not supported on Neuron (NCC_EVRF029). Raw [B, 84, 8400] output with CPU postprocessing (~0.1ms overhead).
• BF16 for m/l/x: FP32 exceeds SB allocation for larger variants (NCC_IGCA030). n/s use FP32.
• No --auto-cast flags: matmult autocast produces NaN for Conv2d-dominant models.
• LNC-aware compilation: --lnc 1 compiler flag required when running on LNC=1 mode.

Target

aws-neuron/neuronx-distributed-inference main branch.