Image
Recognition

We analyze your visual data, lighting conditions, and target categories to select the best camera hardware and model types.

We collect, clean, and annotate target images using bounding boxes or segmentation masks to build high-quality training sets.

We choose appropriate neural network architectures and train them using PyTorch, optimizing parameters to prevent overfitting.

We convert trained models into TensorRT or ONNX formats to maximize frame rate and reduce processing latency.

We wrap the model in a microservice API and connect it to your database, CMS, or security system cameras.

We deploy the vision pipeline to the cloud or edge devices, tracking accuracy and setting up logs for model improvements.