darknet_ros
jetson-inference
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darknet_ros | jetson-inference | |
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4 | 11 | |
2,076 | 7,323 | |
2.8% | - | |
0.0 | 8.5 | |
7 months ago | 4 days ago | |
C++ | C++ | |
BSD 3-clause "New" or "Revised" License | MIT License |
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darknet_ros
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Resources for obstacle avoidance
Sorry got swamped with exams, here is the darknet-ros repo: https://github.com/leggedrobotics/darknet_ros
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Yolo in ROS
You are using „darknet_ros“ package. That information probably belongs in the post. I do not have experience with that package. You should look into your logfile and try debug why it is shutting down. If you have further problems with the package. You should try to open a issue on GitHub. https://github.com/leggedrobotics/darknet_ros
Look at Issue here
- How can I Integrate YOLO algorithm in ROS for Lane and Vehicle Detection ?
jetson-inference
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Can this NVIDIA Jetson Nano handle advanced machine learning tasks?
Jetson Nano’s are obsolete and no longer supported; but to answer your question, this might be a good place to start.
- help with project involving object detection and tracking with camera
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Jetson Nano 2GB Issues During Training (Out Of Memory / Process Killed) & Other Questions!
I’m trying to do the tutorial, where they retrain the neural network to detect fruits (jetson-inference/pytorch-ssd.md at master · dusty-nv/jetson-inference · GitHub 1)
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Jetson Nano
Jetson-Inference is another amazing resource to get started on. This will allow you to try out a number of neural networks (classification, detection, and segmentation) all with your own data or with sample images included in the repo.
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Pretrained image classification model for nuts and bolts (or similar)
Hello! I'm looking for some pre trained image classification models to use on a Jetson Nano. I already know about the model zoo and the pre trained models included in the https://github.com/dusty-nv/jetson-inference repo. For demonstration purposes, however, I need a model trained on small objects from the context of production, ideally nuts, bolts, and similar small objects. Does anyone happen to know a source for this? Thanks a lot!
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PyTorch 1.8 release with AMD ROCm support
> They provide some SSD-Mobilenet-v2 here: https://github.com/dusty-nv/jetson-inference
I was aware of that repository but from taking a cursory look at it I had thought dusty was just converting models from PyTorch to TensorRT, like here[0, 1]. Am I missing something?
> I get 140 fps on a Xavier NX
That really is impressive. Holy shit.
[0]: https://github.com/dusty-nv/jetson-inference/blob/master/doc...
[1]: https://github.com/dusty-nv/jetson-inference/issues/896#issu...
- NVIDIA DLSS released as a plugin for Unreal Engine 4
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Help getting started
If you have a screen and keyboard and mouse plugged into the Nano, I would recommend starting with Hello AI World on https://github.com/dusty-nv/jetson-inference#hello-ai-world
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I'm tired of this anti-Wayland horseshit
Well, don't get me wrong. I do like my Jetson Nano. For a hobbyist who likes to tinker with machine learning in their spare time it's definitely a product cool and there are quite a few repositories on Github[0, 1] with sample code.
Unfortunately… that's about it. There is little documentation about
- how to build a custom OS image (necessary if you're thinking about using Jetson as part of your own product, i.e. a large-scale deployment). What proprietary drivers and libraries do I need to install? Nvidia basically says, here's a Ubuntu image with the usual GUI, complete driver stack and everything – take it or leave it. Unfortunately, the GUI alone is eating up a lot of the precious CPU and GPU resources, so using that OS image is no option.
- how deployment works on production modules (as opposed to the non-production module in the Developer Kit)
- what production modules are available in the first place ("Please refer to our partners")
- what wifi dongles are compatible (the most recent Jetson Nano comes w/o wifi)
- how to convert your custom models to TensorRT, what you need to pay attention to etc. (The official docs basically say: Have a look at the following nondescript sample code. Good luck.)
- … (I'm sure I'm forgetting many other things that I've struggled with over the past months)
Anyway. It's not that this information isn't out there somewhere in some blog post, some Github repo or some thread on the Nvidia forums[2]. (Though I have yet to find a reliably working wifi dongle…) But it usually takes you days orweeks to find it. From a product which is supposed to be industry-grade I would have expected more.
[0]: https://github.com/dusty-nv/jetson-inference
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Basic Teaching
https://github.com/dusty-nv/jetson-inference#system-setup
What are some alternatives?
darknet - YOLOv4 / Scaled-YOLOv4 / YOLO - Neural Networks for Object Detection (Windows and Linux version of Darknet )
openpose - OpenPose: Real-time multi-person keypoint detection library for body, face, hands, and foot estimation
yolo-tf2 - yolo(all versions) implementation in keras and tensorflow 2.x
onnx-tensorrt - ONNX-TensorRT: TensorRT backend for ONNX
carla - Open-source simulator for autonomous driving research.
tensorflow - An Open Source Machine Learning Framework for Everyone
DarkHelp - C++ wrapper library for Darknet
yolov5-deepsort-tensorrt - A c++ implementation of yolov5 and deepsort
DarkPlate - License plate parsing using Darknet and YOLO
TensorRT - NVIDIA® TensorRT™ is an SDK for high-performance deep learning inference on NVIDIA GPUs. This repository contains the open source components of TensorRT.
PreciseRoIPooling - Precise RoI Pooling with coordinate gradient support, proposed in the paper "Acquisition of Localization Confidence for Accurate Object Detection" (https://arxiv.org/abs/1807.11590).
obs-studio - OBS Studio - Free and open source software for live streaming and screen recording