senior-computer-vision

To build an object detection pipeline, you can generate a training configuration for a YOLOv8 model using your custom image data. This streamlines the development process from data preparation through to model deployment using frameworks like Ultralytics and PyTorch.

Yes, you can optimize PyTorch vision models for high-performance inference by converting them using ONNX and TensorRT. This production optimization step ensures advanced architectures like Vision Transformers and YOLO achieve faster processing speeds during deployment.

The best way to perform image segmentation for medical diagnostics is leveraging advanced architectures like Mask R-CNN and SAM. You can build complete segmentation systems from data preparation to model deployment using frameworks like Detectron2.

Yes, this computer vision skill supports both CNN and Vision Transformer architectures. It encompasses comprehensive capabilities for training custom models and deploying visual AI systems using advanced detection frameworks like Faster R-CNN and DETR.

To deploy a real-time object detection system for autonomous vehicles, you build end-to-end pipelines using state-of-the-art models like YOLO. This facilitates optimizing inference with TensorRT and deploying the visual AI systems into production environments.