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