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

Label, train and deploy in a browser

pip install "rtdetr[studio]"
rtdetr studio                                  # http://127.0.0.1:8080

the studio

Upload a dataset, train, watch the curve, download the weights and the OpenVINO IR. For drawing the boxes in the first place:

rtdetr label source=images/ names=can,bottle   # auto-label, then fix

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
rtdetr label   source=images/ names=can,bottle
rtdetr studio  port=8080

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
Labelling — the browser tool, auto-labelling
Studio — the web app: datasets, jobs, artefacts, API
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.

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