darkflow
YOLOv3_TensorFlow
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darkflow | YOLOv3_TensorFlow | |
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2 | 2 | |
6,126 | 1,547 | |
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0.0 | 0.0 | |
6 months ago | over 1 year ago | |
Python | Python | |
GNU General Public License v3.0 only | MIT License |
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darkflow
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FOSS self-hosted image-to-text gpu accelerated object recognition ? Is there anything on the table yet ?
https://github.com/amusi/awesome-object-detection https://mmdetection.readthedocs.io/en/latest/index.html https://github.com/thtrieu/darkflow https://github.com/OlafenwaMoses/ImageAI https://github.com/dmlc/gluon-cv https://github.com/aim-uofa/AdelaiDet/ https://github.com/aim-uofa/AdelaiDet/blob/master/configs/FCOS-Detection/README.md https://github.com/wizyoung/YOLOv3_TensorFlow
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Does the Haskell client for Selenium still work?
You could already tell from my earlier comment, but I don't think Python is a good language, for anything really. It is used for AI so that today's hardware performs like hardware we had twenty years ago: https://github.com/thtrieu/darkflow/issues/904 Python code has slow execution speed. It is not always the Selenium which is slow. sometimes we need to look at code we are using. And Python is always the slowest programming language out there in terms of performance. It's also just a stupid language that annoys me and it's the same with other people: https://medium.com/nerd-for-tech/python-is-a-bad-programming-language-2ab73b0bda5 With Python, I find it a bit too easy to write sloppy code. Haskell on the other hand really forces you to break the problem done and abstract out reusable code. That's not to say that you can't write nice code in Python, just that Haskell doesn't let you get away with doing a lot of stupid stuff. This is equally important for Selenium.
YOLOv3_TensorFlow
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FOSS self-hosted image-to-text gpu accelerated object recognition ? Is there anything on the table yet ?
https://github.com/amusi/awesome-object-detection https://mmdetection.readthedocs.io/en/latest/index.html https://github.com/thtrieu/darkflow https://github.com/OlafenwaMoses/ImageAI https://github.com/dmlc/gluon-cv https://github.com/aim-uofa/AdelaiDet/ https://github.com/aim-uofa/AdelaiDet/blob/master/configs/FCOS-Detection/README.md https://github.com/wizyoung/YOLOv3_TensorFlow
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What is the Yolov4 MakeFile Config for 3080 GPU?
Refer to this https://machinelearningmastery.com/how-to-perform-object-detection-with-yolov3-in-keras/ as a beginner's reference guide. Then look at this https://github.com/wizyoung/YOLOv3_TensorFlow to further enhance your knowledge.
What are some alternatives?
saliency - Framework-agnostic implementation for state-of-the-art saliency methods (XRAI, BlurIG, SmoothGrad, and more).
tensorflow-yolo-v3 - Implementation of YOLO v3 object detector in Tensorflow (TF-Slim)
awesome-object-detection - Awesome Object Detection based on handong1587 github: https://handong1587.github.io/deep_learning/2015/10/09/object-detection.html
gluon-cv - Gluon CV Toolkit
resnet1d - PyTorch implementations of several SOTA backbone deep neural networks (such as ResNet, ResNeXt, RegNet) on one-dimensional (1D) signal/time-series data.
tensorflow-yolov4-tflite - YOLOv4, YOLOv4-tiny, YOLOv3, YOLOv3-tiny Implemented in Tensorflow 2.0, Android. Convert YOLO v4 .weights tensorflow, tensorrt and tflite
webdriver - A Haskell client for the Selenium WebDriver protocol.
AdelaiDet - AdelaiDet is an open source toolbox for multiple instance-level detection and recognition tasks.
yolo-tf2 - yolo(all versions) implementation in keras and tensorflow 2.x
ImageAI - A python library built to empower developers to build applications and systems with self-contained Computer Vision capabilities