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Open-source object detection for Python developers. Frictionless installation. Free for commercial use

Project description

OpenDetect

OpenDetect header

Open-source object detection for production Python applications.
Commercial-use friendly. Python API + CLI.

opendetect gives you one consistent interface for modern ONNX detectors across CPU and accelerated runtimes.

Why OpenDetect

  • Unified API across detector families
  • Runtime-aware acceleration with ONNX Runtime
  • Stable OpenCV/NumPy-first workflow for real video/image pipelines
  • Built-in model registry, auto-download, and caching
  • First-class CLI for inference, benchmarking, and model management

Install

pip install opendetect

Optional extras:

pip install "opendetect[cpu]"
pip install "opendetect[gpu]"
pip install "opendetect[tensorrt]"

Quickstart (Python)

import cv2
from opendetect import Detector

detector = Detector(model="rfdetr-m")

image = cv2.imread("input.jpg")
detections = detector.predict(image, color="bgr")
annotated = detector.annotate(image, detections, color="bgr")

cv2.imwrite("output.jpg", annotated)

File helpers:

from opendetect import Detector

detector = Detector(model="yolox-s")
detector.infer_image_file("input.jpg", output_path="output.jpg")
detector.infer_video_file("input.mp4", output_path="output.mp4", max_frames=300)

Quickstart (CLI)

opendetect-infer --image input.jpg --model-id rfdetr-m --output output.png
opendetect-infer --video input.mp4 --model-id yolox-s --tensor-rt --output output.mp4
opendetect-benchmark --model-id rfdetr-l --mode dummy --warmup 20 --iterations 200
opendetect-models list

Runtime Support

OpenDetect uses ONNX Runtime execution providers and selects the best available runtime automatically:

  • CPU
  • CoreML (Apple Silicon / macOS)
  • CUDA
  • TensorRT
  • DirectML
  • OpenVINO
  • ROCm / MIGraphX

TensorRT note:

  • opendetect[tensorrt] installs Python dependencies only.
  • A compatible system TensorRT/CUDA stack is still required.

Model Families

Family Year License posture
RF-DETR 2026 Open-source, commercial-friendly
YOLOX 2021 Apache-2.0

Documentation

Full guides are in docs/:

  • Start here: docs/index.md
  • Installation and runtimes: docs/getting-started/installation.md, docs/getting-started/runtimes.md
  • Python and CLI usage: docs/getting-started/python.md, docs/getting-started/cli.md
  • Benchmarks: docs/guides/benchmarks.md

Build docs locally:

pip install -r docs/requirements.txt
make -C docs html

License

Apache License 2.0 (LICENSE).

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