rtdetr

Ultralytics-style API, 100% Apache-2.0. RT-DETR object detection you can ship inside a product: train with PyTorch, run with OpenVINO, and keep the workflow your team already knows — model.train(...), model.predict(...), results[0].boxes.xyxy.

Every line of the network, loss, trainer, validator and exporter is an original implementation. No Ultralytics code, no AGPL weights, no license surprises.

pip install rtdetr              # inference (numpy, opencv, openvino, pyyaml)
pip install "rtdetr[train]"     # + training (torch, torchvision, scipy, onnx)

from rtdetr import RTDETR

model = RTDETR("rtdetr-r18")          # weights from the mirror (see "Pretrained weights")
results = model("bus.jpg", conf=0.5)  # list[Results]

r = results[0]
r.boxes.xyxy, r.boxes.conf, r.boxes.cls    # plain numpy
r.names[int(r.boxes.cls[0])]               # "person"
r.plot(); r.save(); r.show()

model = RTDETR("rtdetr-r18")
model.train(data="data.yaml", epochs=100, imgsz=640, batch=8, device=0)
metrics = model.val(data="data.yaml")       # metrics.box.map50 / .box.map
model.export(format="openvino", half=True)  # IR + labels.txt, ready to deploy

Why this exists

Ultralytics YOLO is excellent and its API is muscle memory for a lot of people — but the code and weights are AGPL-3.0, which rules them out of most shipped products. rtdetr keeps the ergonomics and drops the license problem.

Predicting

Any source a YOLO user would expect:

for r in model.predict("clip.mp4", conf=0.4, stream=True):   # generator, O(1) memory
    print(r.boxes.xyxyn)

model.predict("bus.jpg", save=True)     # writes runs/detect/predict/bus.jpg
model.track("clip.mp4")                 # IoU tracker -> r.boxes.id
for r in model.predict(0, stream=True, show=True):  # webcam window, q or Esc quits
    pass                                           # (a generator only runs when iterated)

A webcam loop you can copy, with an FPS counter and optional tracking, lives in examples/webcam.py:

python examples/webcam.py --track          # camera 0
python examples/webcam.py --source rtsp://camera/live --conf 0.4 --save

The log line reads the way you expect:

image 1/1 bus.jpg: 640x640 4 persons, 1 bus, 12.3ms

Results

Which variant, and how fast

Those FPS numbers are the upstream figures for a T4 GPU with TensorRT FP16. On a CPU this is a transformer doing 640×640 attention, and it is far slower — measured here on 4 CPU cores, median over 12 frames:

So if a live camera feels slow, in order of payoff:

1. Use a GPU device. OpenVINO talks to Intel integrated graphics too: RTDETR("rtdetr-r18", device="GPU") (AUTO picks one when it can).
2. Export smaller. Input size dominates: model.export(format="openvino", imgsz=320, half=True) then point RTDETR(...) at that IR. 320 still detects people and cars at conversational distance; 640 is for small or far objects.
3. Use FP16. half=True on export (or tools/build_mirror.py --half).
4. Skip frames. predict(0, vid_stride=2, ...) runs every other frame.

Training

YOLO-format labels — the same data.yaml and images/ + labels/*.txt layout:

path: /data/cans
train: images/train
val: images/val
names:
  0: can
  1: bottle

model = RTDETR("rtdetr-r18")
best = model.train(
    data="data.yaml", epochs=100, imgsz=640, batch=8,
    device=0, workers=4, project="runs", name="train",
    resume=False, patience=50, lr0=1e-4, seed=0,
)
# runs/train/weights/best.pt  (last.pt too — resume=True picks the run back up)

AdamW with a 10× lower LR on the backbone, linear warmup into cosine decay, AMP on CUDA, mAP50-95 after every epoch, early stop on patience.

Exporting and deploying

model = RTDETR("runs/train/weights/best.pt")
xml = model.export(format="openvino", half=True, imgsz=640)

Writes best.xml + best.bin, plus labels.txt (one class name per line) next to the IR — that is how downstream runtimes discover class names.

The exported graph emits probabilities, not logits. Anything that decodes it must not apply a sigmoid a second time; this package checks the score range and skips it (rtdetr/predictor.py).

CLI

Same key=value shape as the yolo command:

rtdetr predict model=rtdetr-r18 source=bus.jpg conf=0.5
rtdetr train   model=rtdetr-r18 data=data.yaml epochs=100
rtdetr val     model=best.pt data=data.yaml
rtdetr export  model=best.pt format=openvino half=true
rtdetr track   model=best.pt source=clip.mp4

Pretrained weights

RTDETR("rtdetr-r18") downloads the IR for that name on first use and caches it in ~/.rtdetr/ ($RTDETR_HOME to move it, $RTDETR_ASSETS_URL to point at an internal mirror — handy for air-gapped sites). Known names: rtdetr-r18, rtdetr-r34, rtdetr-r50; each mirror entry is <name>/<name>.xml, .bin, .pt and labels.txt.

The weights are the original RT-DETR COCO checkpoints, which their authors release under Apache-2.0. This package's network matches that reference layout module for module, so they load with strict=True — no remapping and no re-training. Building the whole mirror takes a few CPU-minutes:

python tools/build_mirror.py --out mirror   # r18 + r34 + r50, .pt + IR + labels

pip install -U "huggingface_hub[cli]"       # ships the `hf` command
hf auth login                               # a token with write access
hf upload leeyunjai/rtdetr mirror . --repo-type=model

(huggingface-cli is the old name for hf and still works if you have it. No CLI on PATH? python -m huggingface_hub.cli.hf upload … does the same, and HfApi().upload_folder(folder_path="mirror", repo_id=…) does it from Python.)

Until that upload happens, RTDETR("rtdetr-r18") raises a ModelNotFoundError naming the ways forward — it never silently falls back to an untrained network. Point $RTDETR_ASSETS_URL at any host serving the same layout to use it now.

For a single checkpoint without the IR:

python tools/convert_official.py --variant r18 --out rtdetr-r18.pt

Design notes

• Backbone PResNet-vd 18/34/50 — three-conv stem, average-pooled shortcuts.
• Encoder AIFI transformer on the top level + CCFF (CSPRepLayer) FPN/PAN fusion.
• Decoder two-stage: dense heads pick the top-300 encoder tokens as queries, 3/4/6 layers refine them with multi-scale deformable cross-attention.
• Loss Hungarian matching, varifocal + L1 + GIoU, over every decoder layer and the encoder's own proposals.
• Validation COCO-style mAP50 / mAP50-95, 101-point interpolation, no pycocotools dependency.
• Inference OpenVINO, plain resize (no letterbox) to match training.

The network definition under rtdetr/nn/ is adapted from the RT-DETR reference implementation (lyuwenyu/RT-DETR, Apache-2.0) precisely so its released weights load here unchanged; see NOTICE. Everything around it — packaging, API, trainer, validator, exporter, predictor, CLI — is this project's own. Nothing here derives from an AGPL-licensed project.

Deformable attention is the grid_sample formulation, so the export path stays plain ONNX (opset 16+) → OpenVINO; a test pins it to within 1e-4 of eager PyTorch, and the assembled model matches the reference implementation to ~1e-5.

Development

pip install -e ".[train,dev]"
pytest -q          # the parity checklist lives in tests/
ruff check .

Releasing

Push a v* tag and GitHub Actions builds and uploads to PyPI:

git tag v0.1.0
git push origin v0.1.0

.github/workflows/publish.yml uses PyPI trusted publishing — no API token lives in the repo. One-time setup on PyPI (Publishing → add a pending publisher):

The job refuses to publish when the tag and pyproject.toml version disagree, so bump the version in the same commit you tag.

한국어 빠른 시작

Ultralytics YOLO와 사용법이 같지만 라이선스는 Apache-2.0입니다. 제품에 넣어도 문제없습니다.

from rtdetr import RTDETR

model = RTDETR("rtdetr-r18")               # 미러에서 가중치 다운로드 (아래 "Pretrained weights" 참고)
results = model("bus.jpg", conf=0.5)       # list[Results]
results[0].boxes.xyxy                      # numpy 배열
results[0].save()                          # 결과 이미지 저장

model.train(data="data.yaml", epochs=100)  # YOLO 형식 라벨 그대로
model.val(data="data.yaml").box.map50      # mAP50
model.export(format="openvino", half=True) # IR + labels.txt

가중치 캐시는 ~/.rtdetr/, 사내 미러는 RTDETR_ASSETS_URL 환경변수로 지정합니다.

License

Apache-2.0. See LICENSE.