ffmpeg-templates
tensorrt_demos
| ffmpeg-templates | tensorrt_demos | |
|---|---|---|
| 1 | 5 | |
| 24 | 1,720 | |
| - | - | |
| 0.0 | 3.1 | |
| 10 months ago | about 1 year ago | |
| TypeScript | Python | |
| MIT License | MIT License |
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ffmpeg-templates
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I'm tired of this anti-Wayland horseshit
This is super off topic, but I also find working with video UIs frustrating for this reason. For my own needs, I have been working on a video editing tool that has a workflow more similar to web dev. E.g. describe changes in text files and render the final video on the command line. https://github.com/andykais/ffmpeg-templates
tensorrt_demos
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lowering size of YOLOV4 detection model
tensorrt_demo github repository
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Jetson Nano: TensorFlow model. Possibly I should use PyTorch instead?
https://github.com/NVIDIA-AI-IOT/torch2trt <- pretty straightforward https://github.com/jkjung-avt/tensorrt_demos <- this helped me a lot
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PyTorch 1.8 release with AMD ROCm support
> I'll also add a caveat that toolage for Jetson boards is extremely incomplete.
A hundred times this. I was about to write another rant here but I already did that[0] a while ago, so I'll save my breath this time. :)
Another fun fact regarding toolage: Today I discovered that many USB cameras work poorly on Jetsons (at least when using OpenCV), probably due to different drivers and/or the fact that OpenCV doesn't support ARM64 as well as it does x86_64. :(
> They supply you with a bunch of sorely outdated models for TensorRT like Inceptionv3 and SSD-MobileNetv2 and VGG-16.
They supply you with such models? That's news to me. AFAIK converting something like SSD-MobileNetv2 from TensorFlow to TensorRT still requires substantial manual work and magic, as this code[1] attests to. There are countless (countless!) posts on the Nvidia forums by people complaining that they're not able to convert their models.
[0]: https://news.ycombinator.com/item?id=26004235
[1]: https://github.com/jkjung-avt/tensorrt_demos/blob/master/ssd... (In fact, this is the only piece of code I've found on the entire internet that managed to successfully convert my SSD-MobileNetV2.)
- I'm tired of this anti-Wayland horseshit
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H.264 hardware acceleration for surveillance station performance
It was some work getting compiled on nano but I used this guy's work to get started. https://jkjung-avt.github.io/tensorrt-yolov4/ and https://github.com/jkjung-avt/tensorrt_demos
What are some alternatives?
wayvnc - A VNC server for wlroots based Wayland compositors
YOLOX - YOLOX is a high-performance anchor-free YOLO, exceeding yolov3~v5 with MegEngine, ONNX, TensorRT, ncnn, and OpenVINO supported. Documentation: https://yolox.readthedocs.io/
emacs - Mirror of GNU Emacs
torch2trt - An easy to use PyTorch to TensorRT converter
xdg-desktop-portal-wlr - xdg-desktop-portal backend for wlroots
yolov4-custom-functions - A Wide Range of Custom Functions for YOLOv4, YOLOv4-tiny, YOLOv3, and YOLOv3-tiny Implemented in TensorFlow, TFLite, and TensorRT.
wl-clipboard - Command-line copy/paste utilities for Wayland
tensorflow-yolov4-tflite - YOLOv4, YOLOv4-tiny, YOLOv3, YOLOv3-tiny Implemented in Tensorflow 2.3.1, Android. Convert YOLO v4 .weights tensorflow, tensorrt and tflite
wlroots - A modular Wayland compositor library
jetson-inference - Hello AI World guide to deploying deep-learning inference networks and deep vision primitives with TensorRT and NVIDIA Jetson.
grim - Grab images from a Wayland compositor