RPi_64-bit_Zero-2-image
Raspberry Pi Zero 2 W 64-bit OS image with OpenCV, TensorFlow Lite and ncnn Framework. (by Qengineering)
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/ (by Megvii-BaseDetection)
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| RPi_64-bit_Zero-2-image | YOLOX | |
|---|---|---|
| 3 | 12 | |
| 24 | 9,012 | |
| - | 1.5% | |
| 0.0 | 1.5 | |
| about 2 years ago | about 2 months ago | |
| Python | ||
| BSD 3-clause "New" or "Revised" License | Apache License 2.0 |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
RPi_64-bit_Zero-2-image
Posts with mentions or reviews of RPi_64-bit_Zero-2-image.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2022-01-02.
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One year on, 64-bit vs 32-bit OS
For Zero 2, with 64-bit you are trading (somewhat) increased performance for available memory. So, really, the choice should depend on what you need more of.
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Building a compact Pi cluster
the 64-bit performance of Bullseye on the Pi Zero 2 W may not be ideal and I may need to rebuild with 32-bit on the nodes
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Install A Raspberry Pi Zero 2 64-bit OS Buster with OpenCV, TensorFlow Lite and ncnn
They helpfully pre-rolled an image on their github with additional instructions here https://github.com/Qengineering/RPi_64-bit_Zero-2-image
YOLOX
Posts with mentions or reviews of YOLOX.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-02-18.
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Learning Exchange, lets training YoloX
So I am trying to do my best and train YOLOX for an object detection case using Google Colab.
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Understanding heatmaps
https://github.com/Megvii-BaseDetection/YOLOX I have only tried the pretrained yolo X nano. I get corner responses even if the inference image is padded with a large margin which is unexpected
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Open discussion and useful links people trying to do Object Detection
* Nice implemention of Yolo that is BSD license (not GPL) https://github.com/Megvii-BaseDetection/YOLOX
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[P] Image search with localization and open-vocabulary reranking.
I wanted to have a few choices getting localization into image search (index and search time). I immediately thought of using a region proposal network (rpn) from mask-rcnn to create patches that can also be indexed and searched (and add the localisation). I figured it might be somewhat agnostic to classes. I did not want to use mmdetection or detectron2 due to their dependencies and just getting the rpn was not worth it. I was encouraged by the PyTorch native implementations of detection/segmentation models but ended up finding yolox the best.
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DeepSort with PyTorch(support yolo series)
Megvii-BaseDetection/YOLOX
- [D][P] YOLOv6: state-of-the-art object detection at 1242 FPS
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Looking for help for hire
Modern video can be broken into a series of still problems. AI vision models can make these types of classification in as fast as video. Here is a particularly there is a controversial company from China that does this very well on faces in video and they have open sourced the models: https://github.com/Megvii-BaseDetection/YOLOX
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High-tech
Not really a problem, see results here. Just use yolox_x. Thank you for your attention.
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Advice on Masters project | Vision transformers
From what I understand the swin transformer outputs a single dimension feature vector and the yolo head takes inputs from 3 different layers from the backbone?? and I think I will need to write the backbone implementation here.
- Is YOLOX object detector NMS free?
What are some alternatives?
When comparing RPi_64-bit_Zero-2-image and YOLOX you can also consider the following projects:
ncnn - ncnn is a high-performance neural network inference framework optimized for the mobile platform
tensorflow-yolov4-tflite - YOLOv4, YOLOv4-tiny, YOLOv3, YOLOv3-tiny Implemented in Tensorflow 2.0, Android. Convert YOLO v4 .weights tensorflow, tensorrt and tflite