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ovkit

OpenVINO inference in 3 lines. One Model class, clean Results, 30+ ready models — with AUTO/NPU/GPU devices, async throughput, and INT8.

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from ovkit import Model

r = Model("detect")("image.jpg")[0]   # download -> convert -> cache -> run
r.save("out.jpg")                     # boxes drawn; r.boxes.xyxy / .conf / .cls

Or without writing Python at all:

ovkit run detect image.jpg            # prints results, saves image_out.jpg

Install

pip install ovkit

Extras: ovkit[quant] (INT8/NNCF) · ovkit[genai] (LLM/STT) · ovkit[all]. Python 3.10+. For development, install from source:

git clone https://github.com/leeyunjai82/ovkit.git && cd ovkit
python -m venv .venv && source .venv/bin/activate   # Windows: .venv\Scripts\activate
pip install -e ".[dev]"

Supported tasks

Every task below runs end-to-end through the same 3 lines — swap the alias:

Task Alias Output Example
Object detection detect, face_detection, person_detection, vehicle_detection, text_detection, license_plate r.boxes detect.py
Classification classify, person_attributes, vehicle_attributes r.probs / text classify.py
Segmentation segment, instance_segmentation r.masks segment.py
Pose / landmarks pose, face_landmarks r.keypoints pose.py
Face analysis age_gender, emotion, head_pose, face_reid r.text (e.g. "age 31 · male 98%") face_analysis.py
OCR text_recognition r.text ocr.py
Super-resolution super_resolution upscaled image via r.plot() super_resolution.py
LLM / STT (GenAI) llm, stt generated text llm.py / stt.py
NLP / audio / time series qa, translation, noise_suppression, time_series tensors via model.infer() denoise_audio.py

The registry exposes one well-tested model per capability (35 total); the HF mirror hosts the full Apache-2.0 OMZ set (other tiers, int8, sparse variants) — surfacing a variant is a one-line edit (catalog). ovkit list shows everything with descriptions.

Devices

Device How Notes
AUTO (default) Model("detect") OpenVINO picks the best device
CPU Model("detect", device="CPU") works everywhere
GPU device="GPU" Intel iGPU / Arc
NPU device="NPU" Intel® Core™ Ultra AI accelerator

Single images run synchronously; stream=True uses an AsyncInferQueue for video/webcam throughput. INT8: model.quantize(calib_images) (NNCF).

Benchmarks

python scripts/benchmark.py        # prints a paste-ready CPU/GPU/NPU table

Usage

Python 
from ovkit import Model

model = Model("face_detection")              # alias, name, .xml, or .onnx
results = model("photo.jpg", conf=0.25)      # image / ndarray / folder / video
for r in model.predict(0, stream=True):      # webcam (lazy generator)
    annotated = r.plot()

print(Model("age_gender")("face.jpg")[0].text)   # "age 31 · male 98%"

Inputs are auto-detected: image path / ndarray / folder / video / camera index → vision pipeline; .npy / .wav → raw inference. Grayscale models and all-image multi-input models (super-resolution) are handled automatically. Full control for any model: model.infer({name: tensor}) with model.inputs.

Results holds
r.boxes xyxy, xywh, conf, cls
r.masks / r.keypoints / r.probs masks · [x,y,conf] · top1/top5
r.text decoded text (OCR, face attributes)
r.tensors raw {name: ndarray}
r.plot() / r.save(path) annotated image (or the model's output image)
CLI 
ovkit run detect image.jpg --save out.jpg   # one-shot inference
ovkit run age_gender face.jpg --device NPU
ovkit list                                  # aliases + models with descriptions
ovkit info face_detection                   # source / task / license
ovkit download detect                       # warm the cache
ovkit devices                               # available OpenVINO devices
GenAI (LLM / speech-to-text) 
from ovkit.genai import pipeline

llm = pipeline("llm")                        # tinyllama_chat from the mirror
print(llm.generate("Explain OpenVINO in one sentence.", max_new_tokens=64))

stt = pipeline("stt")                        # whisper_base
print(stt.generate(audio_16k_mono_float32))

Needs pip install "ovkit[genai]".

Web demo (image / webcam / audio / text) 
pip install -r examples/requirements.txt
python examples/web_app.py                   # http://127.0.0.1:8000

Pick any model — the right input (upload / webcam / audio / text) appears automatically and results render with overlays.

Adding a model

Models are data, not code — one manifest entry (src/ovkit/manifests/):

my_model:
  src: hf
  repo: leeyunjai/ovkit-models
  filename: detect/my_model/model.xml
  task: detect
  description: Shown by `ovkit list`.
  license: apache-2.0            # must be permissive — enforced at load time

Resolution: alias → local path → cache (~/.cache/ovkit) → download → convert → cache, with atomic writes, sha256 checks, upstream fallback, and OVKIT_OFFLINE=1. Maintainer tooling (mirror build / verify / self-check / benchmark) lives in scripts/ — see the guide.

License

ovkit is Apache-2.0 and license-clean by design: only permissive (Apache/MIT/BSD) models and libraries — no AGPL model stacks, no non-commercial weights; every manifest entry must declare a permissive license (enforced at load time).

Project links

Data verified by PyPI on Aug 23, 2026
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Key dates

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  • Released:
Newer release available (0.2.0)

1 maintainer

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Avatar for leeyunjai from gravatar.com leeyunjai

Credits

Author: ovkit contributors

GitHub Statistics

Data verified by PyPI on Aug 23, 2026
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

License

Apache Software License (Apache-2.0)

Requires

Python >=3.10

Provides Extra

all anomaly dev genai quant

Tags

computer-vision detection inference npu openvino quantization

Classifiers

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