rtdetr

Real-time object detection you can ship: PyTorch training, OpenVINO inference, 100% Apache-2.0 — code and pretrained weights alike.

detections on a street scene

Install

pip install rtdetr              # inference
pip install "rtdetr[train]"     # + training

Detect

from rtdetr import RTDETR

model = RTDETR("rtdetr-r18")          # COCO weights, downloaded on first use
r = model("bus.jpg", conf=0.5)[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()

Point it at an image, a folder, a glob, a video, an RTSP stream, a webcam index, or a numpy array. Long sources stream:

for r in model.predict(0, stream=True, show=True):   # webcam, q or Esc quits
    print(r.boxes.xyxyn)

model.track("clip.mp4")        # adds r.boxes.id

Train

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

# data.yaml
path: /data/cans
train: images/train
val: images/val
names: {0: can, 1: bottle}

Command line

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

Models

name	params	COCO AP	notes
`rtdetr-r18`	20M	46.4	fastest
`rtdetr-r34`	31M	48.9
`rtdetr-r50`	43M	53.1	most accurate

Docs

Using the model — sources, results, tracking, saving
Training — datasets, validation, export, CLI
Weights — the mirror, building it, offline use
Performance — measured speeds and how to improve them
Design — architecture and provenance
Development — tests, releases

한국어

from rtdetr import RTDETR

model = RTDETR("rtdetr-r18")               # COCO 사전학습 가중치 자동 다운로드
model("bus.jpg", conf=0.5)[0].save()       # 결과 이미지 저장
model.train(data="data.yaml", epochs=100)  # 내 데이터로 학습
model.export(format="openvino", half=True) # 배포용 IR + labels.txt

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

License

Apache-2.0 — see LICENSE and NOTICE.

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

rtdetr-0.3.2.tar.gz (55.8 kB view details)

Uploaded Aug 27, 2026 Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

The dropdown lists show the available interpreters, ABIs, and platforms. Enable javascript to be able to filter the list of wheel files.

rtdetr-0.3.2-py3-none-any.whl (51.3 kB view details)

Uploaded Aug 27, 2026 Python 3

File details

Details for the file rtdetr-0.3.2.tar.gz.

File metadata

Download URL: rtdetr-0.3.2.tar.gz
Upload date: Aug 27, 2026
Size: 55.8 kB
Tags: Source
Uploaded using Trusted Publishing? Yes
Uploaded via: twine/7.0.0 CPython/3.13.14

File hashes

Hashes for rtdetr-0.3.2.tar.gz
Algorithm	Hash digest
SHA256	`66bd25e65fe3396772783e45dfb6ad0fbef3f1825cfce0b8e6bcb9d8984e191f`
MD5	`9418caf3e5403434294100142726b6b9`
BLAKE2b-256	`aaf5aa06193d4a0fffedcebbb721fb2d42621cc2368ecc2bc35b04953a2e1be6`

See more details on using hashes here.

Provenance

The following attestation bundles were made for rtdetr-0.3.2.tar.gz:

Publisher: publish.yml on leeyunjai82/rtdetr

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

Statement:
- Statement type: https://in-toto.io/Statement/v1
- Predicate type: https://docs.pypi.org/attestations/publish/v1
- Subject name: rtdetr-0.3.2.tar.gz
- Subject digest: 66bd25e65fe3396772783e45dfb6ad0fbef3f1825cfce0b8e6bcb9d8984e191f
- Sigstore transparency entry: 2614159376
- Sigstore integration time: Aug 27, 2026
Source repository:
- Permalink: leeyunjai82/rtdetr@c7c2049abf88abc9ace08ec5bcf0b364a888dddb
- Branch / Tag: refs/heads/main
- Owner: https://github.com/leeyunjai82
- Access: public
Publication detail:
- Token Issuer: https://token.actions.githubusercontent.com
- Runner Environment: github-hosted
- Publication workflow: publish.yml@c7c2049abf88abc9ace08ec5bcf0b364a888dddb
- Trigger Event: workflow_dispatch