CHANGELOG.md

Change Log

Major updates and new features to this project will be listed in this document.

August 31, 2020

• Added initial support for Running in Docker Containers
• Changed OpenGL behavior to show window on first frame
• Minor bug fixes and improvements

July 15, 2020

note: API changes from this update are intended to be backwards-compatible, so previous code should still run.

• Re-training SSD-Mobilenet Object Detection tutorial with PyTorch
• Support for collection of object detection datasets and bounding-box labeling in camera-capture tool
• videoSource and videoOutput APIs for C++/Python that supports multiple types of video streams:
  • MIPI CSI cameras
  • V4L2 cameras
  • RTP / RTSP
  • Videos & Images
  • Image sequences
  • OpenGL windows
• Unified the -console and -camera samples to process both images and video streams
  • imagenet.cpp / imagenet.py
  • detectnet.cpp / detectnet.py
  • segnet.cpp / segnet.py
• Support for uchar3/uchar4/float3/float4 images (default is now uchar3 as opposed to float4)
• Replaced opaque Python memory capsule with jetson.utils.cudaImage object
  • See Image Capsules in Python for more info
  • Images are now subscriptable/indexable from Python to directly access the pixel dataset
  • Numpy ndarray conversion now supports uchar3/uchar4/float3/float4 formats
• cudaConvertColor() automated colorspace conversion function (RGB, BGR, YUV, Bayer, grayscale, ect)
• Python CUDA bindings for cudaResize(), cudaCrop(), cudaNormalize(), cudaOverlay()
  • See Image Manipulation with CUDA and cuda-examples.py for examples of using these
• Transitioned to using Python3 by default since Python 2.7 is now past EOL
• DIGITS tutorial is now marked as deprecated (replaced by PyTorch transfer learning tutorial)
• Logging can now be controlled/disabled from the command line (e.g. --log-level=verbose)

Thanks to everyone from the forums and GitHub who helped to test these updates in advance!

October 3, 2019

• Added new pre-trained FCN-ResNet18 semantic segmentation models:

Dataset	Resolution	CLI Argument	Accuracy	Jetson Nano	Jetson Xavier
Cityscapes	512x256	fcn-resnet18-cityscapes-512x256	83.3%	48 FPS	480 FPS
Cityscapes	1024x512	fcn-resnet18-cityscapes-1024x512	87.3%	12 FPS	175 FPS
Cityscapes	2048x1024	fcn-resnet18-cityscapes-2048x1024	89.6%	3 FPS	47 FPS
DeepScene	576x320	fcn-resnet18-deepscene-576x320	96.4%	26 FPS	360 FPS
DeepScene	864x480	fcn-resnet18-deepscene-864x480	96.9%	14 FPS	190 FPS
Multi-Human	512x320	fcn-resnet18-mhp-512x320	86.5%	34 FPS	370 FPS
Multi-Human	640x360	fcn-resnet18-mhp-512x320	87.1%	23 FPS	325 FPS
Pascal VOC	320x320	fcn-resnet18-voc-320x320	85.9%	45 FPS	508 FPS
Pascal VOC	512x320	fcn-resnet18-voc-512x320	88.5%	34 FPS	375 FPS
SUN RGB-D	512x400	fcn-resnet18-sun-512x400	64.3%	28 FPS	340 FPS
SUN RGB-D	640x512	fcn-resnet18-sun-640x512	65.1%	17 FPS	224 FPS

July 19, 2019

• Python API support for imageNet, detectNet, and camera/display utilities
• Python examples for processing static images and live camera streaming
• Support for interacting with numpy ndarrays from CUDA
• Onboard re-training of ResNet-18 models with PyTorch
• Example datasets: 800MB Cat/Dog and 1.5GB PlantCLEF
• Camera-based tool for collecting and labeling custom datasets
• Text UI tool for selecting/downloading pre-trained models
• New pre-trained image classification models (on 1000-class ImageNet ILSVRC)
  • ResNet-18, ResNet-50, ResNet-101, ResNet-152
  • VGG-16, VGG-19
  • Inception-v4
• New pre-trained object detection models (on 90-class MS-COCO)
  • SSD-Mobilenet-v1
  • SSD-Mobilenet-v2
  • SSD-Inception-v2
• API Reference documentation for C++ and Python
• Command line usage info for all examples, run with --help
• Output of network profiler times, including pre/post-processing
• Improved font rasterization using system TTF fonts

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