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We analyze your image and video sources, evaluating camera placement, lighting, resolution, and frame rates. We define the target object classes and accuracy requirements.
Our team collects representative images and applies precise bounding-box labels using annotation tools. We perform data augmentation to train models on different lighting and angles.
We select the optimal model architecture, comparing YOLOv8 for real-time edge processing with Faster R-CNN for high-precision cloud deployments. We configure hyperparameter baselines.
We train the object detection model on GPU clusters, monitoring precision, recall, and mean average precision (mAP). We refine weights to minimize false positive detections.
We compile the model for target hardware using tools like TensorRT or OpenVINO. This step maximizes inference speed and minimizes memory usage on edge devices.
We deploy the detection system, connecting the API to your video feeds or factory sensors. We build real-time dashboards to display detection events and statistics.
We believe in radical transparency. You'll always know where your project stands and what comes next.
Progress reports every week
Communicate with your team
Clear deliverable checkpoints
Complete technical handoff
Let's begin with a conversation about your project goals.