Sign-Language-Interpreter-using-Deep-Learning
darkflow
Sign-Language-Interpreter-using-Deep-Learning | darkflow | |
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1 | 2 | |
468 | 6,128 | |
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0.0 | 0.0 | |
16 days ago | 6 months ago | |
Python | Python | |
MIT License | GNU General Public License v3.0 only |
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Sign-Language-Interpreter-using-Deep-Learning
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AI tool to convert ASL(American Sign language) into English.
While I could not find that one exactly, I did find this
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.
What are some alternatives?
head-pose-estimation - Realtime human head pose estimation with ONNXRuntime and OpenCV.
saliency - Framework-agnostic implementation for state-of-the-art saliency methods (XRAI, BlurIG, SmoothGrad, and more).
Breast-Cancer-Detection-Mammogram-Deep-Learning-Publication - Code for published article in PLOS ONE. Breast cancer detection in mammograms using DL techniques. Contains source code for data and methods used.
awesome-object-detection - Awesome Object Detection based on handong1587 github: https://handong1587.github.io/deep_learning/2015/10/09/object-detection.html
resnet1d - PyTorch implementations of several SOTA backbone deep neural networks (such as ResNet, ResNeXt, RegNet) on one-dimensional (1D) signal/time-series data.
webdriver - A Haskell client for the Selenium WebDriver protocol.
YOLOv3_TensorFlow - Complete YOLO v3 TensorFlow implementation. Support training on your own dataset.
gluon-cv - Gluon CV Toolkit
yolo-tf2 - yolo(all versions) implementation in keras and tensorflow 2.x
open-lpr - Open Source and Free License Plate Recognition Software
PixelLib - Visit PixelLib's official documentation https://pixellib.readthedocs.io/en/latest/
salsa