tensorrt_demos
yolov4-custom-functions
| tensorrt_demos | yolov4-custom-functions | |
|---|---|---|
| 5 | 3 | |
| 1,720 | 596 | |
| - | - | |
| 3.1 | 0.0 | |
| about 1 year ago | over 1 year 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 They supply you with a bunch of sorely outdated models for TensorRT like Inceptionv3 and SSD-MobileNetv2 and VGG-16.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
yolov4-custom-functions
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How can i count Enter and Exit in Yolov Object Detection?
Object detection and counting project: https://github.com/theAIGuysCode/yolov4-custom-functions
- Contagem de motocicletas no evento do Bozo em SP. Total: 6253.
- Contagem em tempo real das motos durante a motocada promovida pelo presidente Bolsonaro (contagem final: 6253)
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/
yolov5 - YOLOv5 🚀 in PyTorch > ONNX > CoreML > TFLite
torch2trt - An easy to use PyTorch to TensorRT converter
FastMOT - High-performance multiple object tracking based on YOLO, Deep SORT, and KLT 🚀
tensorflow-yolov4-tflite - YOLOv4, YOLOv4-tiny, YOLOv3, YOLOv3-tiny Implemented in Tensorflow 2.3.1, Android. Convert YOLO v4 .weights tensorflow, tensorrt and tflite
Swin-Transformer-Tensorflow - Unofficial implementation of "Swin Transformer: Hierarchical Vision Transformer using Shifted Windows" (https://arxiv.org/abs/2103.14030)
jetson-inference - Hello AI World guide to deploying deep-learning inference networks and deep vision primitives with TensorRT and NVIDIA Jetson.
tensorflow-yolov4-tflite - YOLOv4, YOLOv4-tiny, YOLOv3, YOLOv3-tiny Implemented in Tensorflow 2.0, Android. Convert YOLO v4 .weights tensorflow, tensorrt and tflite
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
mousehunter-edge - Cat with prey detection on Raspberry Pi. Lock cat pet flap if prey is detected. Object detection implemented in TFLite with ImageNet v1 SSD. Inference on EdgeTPU (Google Coral USB). Stores images on AWS S3 and sends notifications to iOS device.