Major updates and new features to this project will be listed in this document.
- Added Pose Estimation with PoseNet with pre-trained models
- Added Mononocular Depth with DepthNet with pre-trained models
- Added support for
cudaMemcpy()
from Python - Added support for drawing 2D shapes with CUDA
- Added initial support for Running in Docker Containers
- Changed OpenGL behavior to show window on first frame
- Minor bug fixes and improvements
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
andvideoOutput
APIs for C++/Python that supports multiple types of video streams:- Unified the
-console
and-camera
samples to process both images and video streams - Support for
uchar3/uchar4/float3/float4
images (default is nowuchar3
as opposed tofloat4
) - 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
- See Image Manipulation with CUDA and
- 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!
- 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 |
- 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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