tensorrt_demos
tensorflow-yolov4-tflite
tensorrt_demos | tensorflow-yolov4-tflite | |
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5 | 1 | |
1,720 | 59 | |
- | - | |
3.1 | 5.4 | |
about 1 year ago | over 3 years ago | |
Python | Python | |
MIT License | MIT License |
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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 They supply you with a bunch of sorely outdated models for TensorRT like Inceptionv3 and SSD-MobileNetv2 and VGG-16.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
tensorflow-yolov4-tflite
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Run YOLOv3 and YOLOv4 pre-trained models with OpenCV. You can get a speed boost if OpenCV is built with CUDA support. Otherwise, it will run on CPU.
This GitHub repo contains the comparison. https://github.com/haroonshakeel/tensorflow-yolov4-tflite#fps-comparison
What are some alternatives?
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/
yolov4-deepsort - Object tracking implemented with YOLOv4, DeepSort, and TensorFlow.
torch2trt - An easy to use PyTorch to TensorRT converter
yolor - implementation of paper - You Only Learn One Representation: Unified Network for Multiple Tasks (https://arxiv.org/abs/2105.04206)
yolov4-custom-functions - A Wide Range of Custom Functions for YOLOv4, YOLOv4-tiny, YOLOv3, and YOLOv3-tiny Implemented in TensorFlow, TFLite, and TensorRT.
yolov5 - YOLOv5 🚀 in PyTorch > ONNX > CoreML > TFLite
jetson-inference - Hello AI World guide to deploying deep-learning inference networks and deep vision primitives with TensorRT and NVIDIA Jetson.
Deep-SORT-YOLOv4 - People detection and optional tracking with Tensorflow backend.
wayvnc - A VNC server for wlroots based Wayland compositors
yolo-tf2 - yolo(all versions) implementation in keras and tensorflow 2.x
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
tensorflow-yolov4-tflite - YOLOv4, YOLOv4-tiny, YOLOv3, YOLOv3-tiny Implemented in Tensorflow 2.0, Android. Convert YOLO v4 .weights tensorflow, tensorrt and tflite