ultrafast-pycocotools
A drop-in replacement for pycocotools that computes COCO-style AP/AR for
object detection, instance segmentation and keypoint detection. Ground-truth
indexing, IoU/OKS matching and precision–recall accumulation run in a
multithreaded Rust core (Rayon), exposed to Python through PyO3.
- Bit-exact metrics. The complete
precision,recallandscoresarrays are bit-identical to pycocotools on every tested input, not only the rounded AP/AR summary. - Fast. On COCO val2017, 25–91× lower wall-clock time than pycocotools, 10–15× lower than faster-coco-eval and 2.5–4.4× lower than hotcoco.
- Memory-efficient. 59–86% lower peak RSS than pycocotools, and the lowest of all four evaluators on every task.
- Drop-in. The same
COCO/COCOevalAPI forbbox,segmandkeypoints, plus the LVIS federated protocol. Prebuilt wheels for Linux, macOS and Windows (CPython 3.8–3.14). - Per-batch precision, recall and F1. Scored from the same matching as AP, per batch or accumulated over a validation loop: 0.26 ms for a 16-image batch and 0.09 s for all of COCO val2017, 52–780× and 27–580× less time than hotcoco, Ultralytics, TorchMetrics, supervision, faster-coco-eval and pycocotools (comparison).
Status: alpha. Before replacing the reference evaluator in your pipeline,
check parity on your own evaluation parameters and COCOeval subclasses.
Installation
pip install ultrafast-pycocotools
Prebuilt wheels need no Rust compiler; NumPy is installed as a dependency.
Other platforms build from source with a stable Rust toolchain:
pip install git+https://github.com/developer0hye/ultrafast-pycocotools.
Quick start
from ultrafast_pycocotools import COCO, COCOeval
gt = COCO("instances_val2017.json") # ground-truth annotations
dt = gt.loadRes("detections.json") # detection results in COCO format
evaluator = COCOeval(gt, dt, "bbox") # or "segm", "keypoints"
evaluator.run() # evaluate() + accumulate() + summarize()
print(evaluator.stats_as_dict) # AP, AP50, AP75, APs, APm, APl, AR@1, ...
For LVIS, pass lvis_style=True to apply the federated annotation protocol
(negative and not-exhaustive category lists, 300 detections per image) and the
official metric names (AP, APr, APc, APf, AR@300, ...). See the
LVIS guide.
Use inside an existing framework
If a training or validation framework imports pycocotools internally,
register the replacement before importing that framework:
from ultrafast_pycocotools import init_as_pycocotools
init_as_pycocotools() # `import pycocotools` now resolves to ultrafast
This patches sys.modules for the whole Python process.
Benchmarks
COCO val2017 (all 5,000 images) with cached YOLO26n, YOLO26n-seg and YOLO26n-pose detections: 733,070 boxes, 724,953 instance masks and 134,663 pose instances. Model inference is excluded; the timed region covers JSON parsing, ground-truth indexing, matching, accumulation and summarization.
| Task | pycocotools 2.0.11 | faster-coco-eval 1.8.0 | hotcoco 1.0.1 | ultrafast 0.1.12 |
|---|---|---|---|---|
| bbox | 46.54 s · 1,926 MB | 7.40 s · 1,781 MB | 2.26 s · 2,308 MB | 0.51 s · 262 MB |
| segm | 50.88 s · 2,192 MB | 16.39 s · 2,630 MB | 5.43 s · 3,404 MB | 1.66 s · 556 MB |
| keypoints | 10.05 s · 604 MB | 4.66 s · 603 MB | 1.01 s · 643 MB | 0.41 s · 249 MB |
| Bit-identical to pycocotools | reference | ✗ (precision ≤ 2.2e-16 off on bbox/segm) |
✗ (scores differ on bbox/segm) |
✓ all tasks |
Wall-clock time · peak RSS; median of 6 runs, each in a fresh process, on an Intel Core i5-10400 with a 2-thread pool (pycocotools is single-threaded), measured 2026-09-30. Full report, method and raw data · All benchmarks, other hosts and historical results
Used by
- RF-DETR: optional
ufcocobackend for bbox and mask mAP during training and validation (TrainConfig.eval_backend="ufcoco"), merged in roboflow/rf-detr#1449 and released in RF-DETR 1.11.0. Benchmark - SAHI: optional backend for
sahi coco evaluate(pip install "sahi[ultrafast]", then--backend ultrafast), merged in obss/sahi#1452 and released in SAHI 0.12.7.
Proposed integrations under review: Ultralytics (validation) · torchvision · TorchMetrics · SAM 3 · RT-DETR · DEIMv2
Compatibility
The public COCO, COCOeval and mask (RLE) APIs are tested against
pycocotools, covering annotation query order, loadRes, compressed and
uncompressed RLE, custom Params (IoU thresholds, area ranges, maxDets),
iscrowd handling, score ties and subclass overrides. A few internals
intentionally differ:
- Per-image matching records (
evalImgs) are not stored by default; passstore_eval_imgs=Trueif your code reads them. - Box-only detections omit the polygon
segmentationderived from each box; passderive_segmentation=Trueif you read that field. COCO(annotation_dict)borrows the dictionary instead of deep-copying it, and ground-truth annotations are not rewritten in place.
Bit-exactness claims cover the tested inputs and configurations. See the compatibility notes.
Extras
The same matching results provide per-category AP, precision–recall curves, per-detection TP/FP matches, a confusion matrix, and precision, recall and F1 at a score threshold or at the best-F1 cut. Boundary IoU is available as an additional evaluation mode (an extension, not a pycocotools metric):
evaluator.per_category_stats()
evaluator.pr_curve(cat_id=1, iou_thr=0.5)
evaluator.matches(iou_thr=0.5)
evaluator.confusion_matrix()
evaluator.detection_metrics(iou_thr=0.5, score_thr="best_f1")
See the implementation notes.
Per-batch precision, recall and F1
For training and validation loops, DetectionMetrics scores batches of
per-image arrays or tensors (or COCO data) with the same matching as AP:
from ultrafast_pycocotools.metrics import DetectionMetrics
metric = DetectionMetrics(iou_thrs=0.5, score_thr="best_f1") # box_format="xyxy"
for images, targets in loader:
batch = metric.update(model(images), targets) # [{"boxes", "scores", "labels"}, ...]
total = metric.compute() # per_category / micro / macro, and the chosen score_thr
update()returns the batch's metrics;compute()returns them over every batch, bit-identical to one evaluation over all images. Only a category id, a score and one byte per IoU threshold are kept per detection, never boxes or masks (batch_results=Falseskips the per-batch summary).- Precision, recall and F1 per category, micro and macro, at any IoU
thresholds, with every detection, a score threshold, or the score cut that
maximizes F1 (
score_thr="best_f1"). Zero denominators give 0. - Boxes (
xyxyorxywh) or masks, class-aware or class-agnostic. COCO's crowd regions are honored, and its area ranges andmaxDetsare optional. - NumPy arrays, lists and PyTorch tensors; CPU tensors are read in place.
COCOeval.detection_metrics()computes the same metrics afterevaluate(), with that evaluation's parameters.
COCO val2017 with YOLO26n predictions, one thread, on an Intel Core i5-10400; each library computes its own metric (P/R/F1, F1 or AP), so the times are the cost of getting each library's numbers per batch:
| Library | 16-image batch | All 5,000 images in batches of 16 | Peak memory added by the 5,000 images |
|---|---|---|---|
| ultrafast-pycocotools | 0.26 ms | 0.09 s | 34 MB |
| hotcoco 1.1.0 | 13 ms | 2.3 s | 578 MB |
| Ultralytics 8.4.172 | 20 ms | 2.9 s | 145 MB |
| TorchMetrics 1.9.0 (faster-coco-eval backend) | 30 ms | 9.1 s | 1,582 MB |
| supervision 0.30.6 | 35 ms | 10.3 s | 74 MB |
| faster-coco-eval 1.8.0 | 170 ms | 28.3 s | 1,623 MB |
| pycocotools 2.0.11 | 197 ms | 44.7 s | 1,728 MB |
| TorchMetrics 1.9.0 (pycocotools backend) | 200 ms | 49.7 s | 1,697 MB |
What each library computes, method and raw data · Design
Development
git clone https://github.com/developer0hye/ultrafast-pycocotools.git
cd ultrafast-pycocotools
pip install -e ".[test,lvis-test]"
python bench/fetch_lvis_fixture.py
python -m pytest -q
cargo test -p ufcoco-core
python bench/reproduce.py quick --out bench/out/quick --verify-published quick
runs an end-to-end parity check on synthetic data, with no dataset download
or model weights. See the
reproduction guide,
CI and
release process.
Documentation, comments and examples are written in English.
License and credits
BSD-2-Clause. The evaluation protocol and API follow pycocotools by Piotr Dollár and Tsung-Yi Lin (BSD-2-Clause). LVIS verification uses the official LVIS API. Design decisions are in DESIGN.md.
1 maintainer
Metadata
1 maintainer
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