online-face-detection 0.1.2

Streaming, frame-by-frame face detection (RetinaFace) with a unified torch/torchscript/onnx/trt runtime and export-once caching for edge devices.

online-face-detection

Streaming, frame-by-frame face detection for real-time pipelines: one small object — a frame in, structured results out. Runs under torch / torchscript / onnx / tensorrt with export-once caching, on CPU, CUDA, Apple Silicon (MPS), and Jetson.

from online_face import FaceDetector
det = FaceDetector("retinaface", device="auto")
res = det(frame)        # res.boxes, res.scores, res.landmarks

Models today: RetinaFace. More face-detection families plug in via the registry — coming later.

---

Install

pip install "online-face-detection[torch]"

That's all you need for most setups — [torch] is the default runtime and works on CPU, CUDA, and Mac (MPS). Other backends (onnx, tensorrt, serving) are optional extras you can add anytime — see Install options. (Prefer uv? See Misc.)

---

Use it (Python)

from online_face import FaceDetector

det = FaceDetector(
    "retinaface",       # model family (the only one today)
    device="auto",      # "auto" (CUDA > MPS > CPU) | "cpu" | "cuda" | "mps"
    runtime="auto",     # "auto" | "torch" | "torchscript" | "onnx" | "trt"
    conf=0.5,           # detection confidence threshold
    nms=0.4,            # NMS IoU threshold
)
res = det(frame)        # weights auto-download on first use; see "Input" below

res.boxes        # (N, 4) xyxy, in original-frame coordinates
res.scores       # (N,)
res.landmarks    # (N, 5, 2)

Input — what frame must be: a NumPy array in BGR order, shape (H, W, 3), dtype uint8 (OpenCV's native format — e.g. straight from cv2.imread(...) or cv2.VideoCapture(...).read()), or a torch.Tensor of shape (3, H, W). Any resolution; the model letterboxes internally.

Drive a video file or a live stream (FPS/latency print to the terminal):

for frame_ref, res in det.run_source("video.mp4"):                          # a file
    ...
for frame_ref, res in det.run_source("rtsp://cam/stream", is_stream=True):  # live
    ...
for frame_ref, res in det.run_source("video.mp4", is_stream=True):          # file as a stream
    ...
# frame_ref.image is the BGR frame; res is the FaceFrameResult for that frame.

Output — FaceFrameResult: boxes (N,4) xyxy · scores (N,) · landmarks (N,5,2) · frame_index · shape (H,W). Coordinates are in the original frame. det.stats.as_dict() gives rolling fps / latency / per-stage timings.

Or from the terminal

# detect on a video file and show a window (boxes + landmarks + FPS; press q/ESC to quit)
online-face --source video.mp4 --device auto --runtime auto --conf 0.5 --nms 0.4 --display

# webcam (index 0) as a live stream
online-face --source 0 --device auto --runtime auto --stream --display

# headless: write an annotated mp4 instead of showing a window
online-face --source video.mp4 --device auto --runtime auto --save-video out.mp4

# discover weights, or see every flag
online-face --list-weights
online-face --help

online-face == python -m online_face.cli.run. All flags: --source (file path | webcam index | rtsp/http url) · --device {auto,cpu,cuda,mps} · --runtime {auto,torch,torchscript,onnx,trt} · --conf · --nms · --stream · --display · --save-video PATH · --max-frames N · --list-weights.

---

Models & weights

model is the family (retinaface — the only one today); weights is the actual weight — a known key (auto-downloaded) or a file path. weights=None uses the default.

weights key	impl	exportable	notes
mobilenet0.25 (default)	biubug6	onnx / trt	light, edge-friendly; auto-downloads (~1.7 MB)
resnet50	biubug6	onnx / trt	higher accuracy; auto-downloads (~109 MB, sha256-checked)
ternaus_resnet50	ternaus	torch-only	a convenience weight; works out of the box

FaceDetector("retinaface", weights="mobilenet0.25")                            # default, auto-downloads
FaceDetector("retinaface", weights="/models/retinaface.onnx", runtime="onnx")  # a ready artifact

resnet50 is auto-downloaded (~109 MB, sha256-verified) from the official biubug6 mirror on first use — nothing to do. If Google Drive ever rate-limits you, download Resnet50_Final.pth from biubug6/Pytorch_Retinaface and pass the path:

FaceDetector("retinaface", weights="/path/to/Resnet50_Final.pth")     # or --weights on the CLI/serve

…or drop it at ~/.cache/online_inference/weights/retinaface_resnet50.pth and use weights="resnet50". (Keep resnet/r50 in the filename — the arch is inferred from the name. The same applies to any custom weight file.)

---

Runtimes & the export cache

runtime="auto" picks the best backend per device: Jetson/CUDA → tensorrt (else onnx-CUDA), macOS → torch (MPS), CPU → onnx/torch. The first time a non-torch runtime is used, the artifact (torchscript / onnx / trt engine) is built once and cached under ~/.cache/online_inference/ (override with $ONLINE_INFERENCE_CACHE); later runs load it. TensorRT engines are keyed to the exact GPU/JetPack so they never load on the wrong device.

---

Install options

[torch] is all most people need. Add extras for other backends. Extras are additive — if you already installed [torch], running pip install "online-face-detection[serve]" later just adds those packages (it won't reinstall torch). You can also install several at once: pip install "online-face-detection[torch,onnx,serve]".

Extra	Adds	Install when you want to…
[torch]	torch, torchvision, retinaface-pytorch	default runtime (CPU / CUDA / MPS)
[onnx]	onnxruntime, onnx, onnxsim	run or export the ONNX backend
[trt]	tensorrt (also pulls [onnx])	build/run TensorRT engines on NVIDIA. Needs a CUDA build of torch with torch + tensorrt + CUDA on the same version (e.g. cu12). Jetson: use JetPack's TensorRT (below)
[serve]	fastapi, uvicorn	host the model as an HTTP service (below)
[client]	requests	call a remote service (torch-free, below)

Which do I actually need?

• pip install online-face-detection (no [...]) → core only (numpy/opencv); no runtime, can't run inference. Use this only when torch is provided another way (e.g. Jetson/JetPack wheels).
• [torch] → the foundation; required to run the model locally (CPU/CUDA/MPS). Start here.
• [onnx] / [trt] → add a backend on top of torch (they don't replace it). [trt] also pulls [onnx] and needs a matching CUDA build of torch (see the table). Install together: pip install "online-face-detection[torch,trt]".
• [serve] → runs the model in-process, so it needs torch too: pip install "online-face-detection[torch,serve]".
• [client] → the only torch-free one — it just calls a remote service, so pip install "online-face-detection[client]" alone is enough.

---

(Optional) Serve it as an HTTP service

Besides the in-process use above, the model can run as its own HTTP service (local or cloud) and be called by URL. Needs the [serve] extra (adds only fastapi/uvicorn on top of [torch]).

pip install "online-face-detection[serve]"
online-face-serve --model retinaface --device auto --runtime auto --host 127.0.0.1 --port 8001

Server flags (all optional; defaults shown): --model retinaface · --weights KEY|PATH (default: family default) · --device {auto,cpu,cuda,mps} · --runtime {auto,torch,torchscript,onnx,trt} · --precision {auto,fp32,fp16,int8} · --conf 0.5 · --nms 0.4 · --input-size N · --host 127.0.0.1 · --port 8001.

Route	What it does
GET /meta	self-describing: named, typed inputs/outputs (input frame: image; outputs boxes/scores/landmarks)
GET /healthz	readiness + resolved runtime/device
POST /predict	multipart with a frame image part → JSON {outputs, stats}

curl http://127.0.0.1:8001/meta
curl -F 'frame=@frame.png;type=image/png' http://127.0.0.1:8001/predict

Call it from another process with the torch-free [client] proxy (mirrors det(frame)):

pip install "online-face-detection[client]"

from online_face.client import FaceClient

face = FaceClient(
    "http://127.0.0.1:8001",   # the service URL (local or cloud)
    encode="png",              # how frames go over the wire: "png" (lossless) | "jpeg" (smaller)
    timeout=30,                # request timeout, seconds
)
res = face(frame)              # same shape as det(frame): res.boxes / res.scores / res.landmarks
face.meta()                    # the service's /meta;  face.healthz() -> readiness

Compose two services into a pipeline (e.g. face → emotion) by URL — see ../testing-pipeline for a ready-to-run example.

---

Misc

Install with uv

Same as pip, with uv:

uv add "online-face-detection[torch]"            # into a uv project
uv pip install "online-face-detection[torch]"    # into the active venv

Jetson (JetPack)

On Jetson the whole GPU stack (CUDA / cuDNN / TensorRT) is part of JetPack, and torch/onnxruntime must be NVIDIA's Jetson wheels — the PyPI [torch]/[onnx] wheels are x86_64 and won't use the GPU.

1. Pick a JetPack version.

Board	JetPack	Stack
Orin (AGX/NX/Nano)	6.x	CUDA 12.6 · TensorRT 10.3 · PyTorch 2.6 wheel
Xavier / older	5.1.x	torch ~2.1

Both are above this package's torch>=2.1 floor.

2. Install these into the JetPack env first — from NVIDIA's PyTorch for Jetson guide, or the jetson-ai-lab wheel index matched to your JetPack (e.g. --index-url https://pypi.jetson-ai-lab.io/jp6/cu126 for JetPack 6.x):

• torch, torchvision — the Jetson GPU wheels (not from PyPI)
• onnxruntime-gpu — only if you'll use the ONNX backend
• opencv-python, numpy — usually already present in JetPack; install if missing
• TensorRT — already installed by JetPack (nothing to do)

3. Then install this package with NO runtime extra, so it uses the system ones:

pip install online-face-detection      # no [torch] / [onnx]

It adapts to whatever JetPack provides and keys each cached TensorRT engine to the exact board.

Conflicting model requirements? One Jetson has a single system torch/TRT. If two models need incompatible torch/CUDA, run each as its own HTTP service (e.g. an nvcr.io/nvidia/l4t-pytorch container) and compose them by URL with the [client] proxy.

Pre-build & cache an artifact

Optional — otherwise built on first use. Choose the runtime you'll deploy with for the target device:

online-face-export --model retinaface --weights mobilenet0.25 --runtime trt --device auto

Flags: --model · --weights KEY|PATH · --runtime {torchscript,onnx,trt} · --device {auto,cpu,cuda,mps} · --precision {auto,fp32,fp16,int8} · --input-size N.

License

MIT © Surya Chand Rayala