Trained in PyTorch,
deployed without a cloud round-trip.
We train computer vision models in PyTorch and export them to ONNX Runtime for edge and embedded deployment — portable inference that runs on the device itself, not a round-trip to a cloud GPU for every frame.
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A training framework
and a portable runtime.
PyTorch is where the model gets trained. ONNX Runtime is where it actually runs once it's on the line, on a camera, or on an embedded device — a portable format that performs well on CPU and embedded accelerators, not just a GPU-backed cloud instance.
Separating training from deployment this way means the hardware constraints of the edge device don't have to shape every decision made during training — but they do have to shape what happens between training and shipping.
The export isn't
the hard part.
Converting a model to ONNX is mechanical. Making sure it still performs once it's deployed is the actual work.
Training on conditions that match the deployment camera and environment — lighting, motion, distortion — not just a clean studio dataset.
Export and quantization settings tuned to the actual device the model runs on, so accuracy loss from compression is a measured tradeoff, not a surprise.
Accuracy measured on real deployment footage, not just the original studio test set — so the reported number is the one that holds up in production.
Part of our
vision AI work.
This stack sits inside the broader computer vision and edge deployment work we do end to end.
The full service this sits inside — from custom model training to on-device inference.
The pattern this training-to-deployment pipeline is built to prevent — a model that tests well but underperforms once deployed. (Illustrative write-up, not a specific project's exact hardware.)
Monitoring and feedback loops for flagging low-confidence predictions once a model is live on the line.
Before you
book a call.
The questions we get asked most about PyTorch and edge deployment — answered straight, no sales pitch.
Why train in PyTorch and deploy via ONNX Runtime instead of deploying PyTorch directly?
PyTorch is a training framework, not an edge runtime. Exporting to ONNX gives a portable model format that runs efficiently across different edge and embedded hardware without carrying the full training framework's footprint into a device that may have limited memory and no GPU.
Does this work on hardware without a GPU?
That's the point of the ONNX Runtime step — it's built for efficient CPU and embedded-accelerator inference, not just GPU-backed deployment. The right export and quantization settings depend on the target hardware, which we confirm before committing to an approach.
What problem does this stack actually solve?
The common failure mode isn't the model architecture — it's a mismatch between studio training conditions and real deployment conditions (lighting, motion blur, lens distortion), the kind of gap our vision field-accuracy write-up describes. PyTorch plus a disciplined training-to-deployment pipeline is how that gap gets closed, not just how the model gets exported.
Tell us what
you're trying to deploy.
Book a 30-minute call — we'll tell you honestly whether your model has a lab-to-field gap, and what it would take to close it.