Vision DNN library for NVIDIA Jetson devices
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This project provides a comprehensive guide and library for deploying deep learning inference networks and real-time vision primitives on NVIDIA Jetson devices. It targets developers and researchers working with embedded AI, offering optimized inference via TensorRT and training capabilities with PyTorch, enabling applications from image classification to action recognition.
How It Works
The library leverages NVIDIA's TensorRT for highly optimized deep learning inference on Jetson GPUs, supporting FP16 precision for maximum throughput. It provides pre-trained models and APIs for common vision tasks like classification (ImageNet), object detection (SSD, TAO), segmentation (SegNet), pose estimation, and action recognition. The project also includes utilities for camera streaming, CUDA manipulation, and ROS integration.
Quick Start & Requirements
Highlighted Details
Maintenance & Community
The project is actively maintained by NVIDIA (dusty-nv). Community support channels are not explicitly listed, but NVIDIA's Jetson AI Lab offers additional tutorials and resources.
Licensing & Compatibility
The project appears to be primarily licensed under a permissive BSD 3-Clause license, allowing for commercial use and integration into closed-source projects.
Limitations & Caveats
While supporting a broad range of Jetson hardware, performance benchmarks are often tied to specific JetPack versions and hardware configurations (e.g., JetPack 4.2.1, nvpmodel 0). Some older tutorials (e.g., DIGITS/Caffe) are marked as deprecated.
9 months ago
Inactive