v0.5.0 — Your checkpoint, your dataset, proven

v0.5.0 — Your checkpoint, your dataset, proven

The most requested capability since v0.4.0: taking this pipeline to a model that is not COCO.
This release makes that a first-class, verifiable path — export a fine-tuned D-FINE checkpoint
without a training checkout, evaluate it on your own COCO-format dataset, and compare operating
points through a chain that refuses to compare the wrong things. The published model artifacts
are byte-identical to v0.3.1–v0.4.0; no re-export is required.

Validate on your own dataset

• preset_sweep (dfine sweep) runs the whole comparison in one command: export, slim
  conversion, engine builds, accuracy on your split, baseline deltas, and native throughput over
  configurable operating points (graph × precision × engine profile × runtime mode), assembled
  into one decision table with an --ap-budget recommendation. Every step is the documented tool
  run with its argv recorded, so the table is reproducible by hand.
• D-FINE-seg YOLO splits convert on the fly (--yolo-dataset); yolo_to_coco.py is the
  standalone converter.
• Validation is a reproducible workflow for a custom checkpoint and any
  COCO-format detection dataset: any class count, the same 640×640 stretch contract, standard
  COCO bbox metrics.
• Accuracy reports carry AP/AR slices, per-IoU and per-class metrics, GT density, and
  original-pixel versus model-space object-size breakdowns, plus annotation, image, evaluator,
  artifact, and lineage hashes.
• accuracy_chain.py assembles export → TensorRT → precision → preset deltas and rejects reports
  with different annotations, image bytes, preprocessing, thresholds, Top-K, inference batch,
  evaluator source, GT population, model contract, or artifact lineage. Reports that predate
  lineage recording are rejected unless --allow-missing-lineage is passed.
• The v0.5 preset report is the worked example: four
  independently fine-tuned cross-domain datasets (RF4) plus full COCO, five graph presets, every
  backend transition isolated.

Self-contained checkpoint export

• The detection-only D-FINE definition is bundled; export no longer needs a training repository
  checkout. Breaking: export requires an explicit --checkpoint; --dfine-src and
  DFINE_SEG_DIR/DFINE_SEG_SRC are removed from the export toolchain.
• A path-gated CI job proves the bundled model bit-exact against the pinned D-FINE-seg revision
  over all five published checkpoints whenever the bundle or the pin changes.
• Checkpoint provenance is strict, model packs are validated canonically, and engine builders
  deserialize the published plan to verify its compiled batch profile before recording metadata.

Benchmark surface

• The throughput surface was re-measured under an idle-gated protocol: five interleaved
  single-round processes per configuration, published medians with min–max ranges. Batch-8 gains
  are monotonic in pruned query count; Q200→C100 is the fastest preset at +9.7%.
• Runtime modes are now published points: full-pipeline CUDA Graph and 1/8/8 serving profiles,
  including the max configuration — 1.99 ms at batch 1 under graph capture and 662 img/s at
  batch 8 (−0.96 COCO AP), measured with the standard fidelity chain.
• A controlled eager versus torch.compile baseline accompanies the study.

Runtime and tooling contracts

• GPU-decode host staging buffers are pinned, so the mode's async copies stay asynchronous and
  the reported dispatch/wait timing split is truthful; detections are unchanged.
• profile.py enforces --topk on the native accuracy backend and rejects values above the
  native decode limit.
• Evaluation reports are stretch-only end to end; the producer-less letterbox report schema was
  removed and accuracy_chain rejects letterbox geometry.

Assets

The wheel is dfine-0.5.0-py3-none-linux_x86_64.whl; the model pack republishes the pinned
v0.3.1-lineage artifacts unchanged. Install and verification commands are in
Getting started; the asset grammar is in Naming.