darkflow
yolo-tf2
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| darkflow | yolo-tf2 | |
|---|---|---|
| 2 | 1 | |
| 6,126 | 747 | |
| - | - | |
| 0.0 | 7.6 | |
| 6 months ago | almost 2 years ago | |
| Python | Python | |
| GNU General Public License v3.0 only | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
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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.
yolo-tf2
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How to write a resume for python / ML jobs?
my most useful project is yolo object detector implementation in tf2 and I'm currently working on 2 other projects, one of which is the implementation of various drl algorithms in tf and the other project will be based on the latter and it's concerned with trading. The rest are more of scripts rather than projects ex: web scraping, file management, programming challenges ...
What are some alternatives?
saliency - Framework-agnostic implementation for state-of-the-art saliency methods (XRAI, BlurIG, SmoothGrad, and more).
Deep-SORT-YOLOv4 - People detection and optional tracking with Tensorflow backend.
awesome-object-detection - Awesome Object Detection based on handong1587 github: https://handong1587.github.io/deep_learning/2015/10/09/object-detection.html
Real-time-Object-Detection-for-Autonomous-Driving-using-Deep-Learning - My Computer Vision project from my Computer Vision Course (Fall 2020) at Goethe University Frankfurt, Germany. Performance comparison between state-of-the-art Object Detection algorithms YOLO and Faster R-CNN based on the Berkeley DeepDrive (BDD100K) Dataset.
resnet1d - PyTorch implementations of several SOTA backbone deep neural networks (such as ResNet, ResNeXt, RegNet) on one-dimensional (1D) signal/time-series data.
Beginner-Traffic-Light-Detection-OpenCV-YOLOv3 - This is a python program using YOLO and OpenCV to detect traffic lights. Works in The Netherlands, possibly other countries
webdriver - A Haskell client for the Selenium WebDriver protocol.
deepsparse - Sparsity-aware deep learning inference runtime for CPUs
YOLOv3_TensorFlow - Complete YOLO v3 TensorFlow implementation. Support training on your own dataset.
yolor - implementation of paper - You Only Learn One Representation: Unified Network for Multiple Tasks (https://arxiv.org/abs/2105.04206)
gluon-cv - Gluon CV Toolkit
FastMOT - High-performance multiple object tracking based on YOLO, Deep SORT, and KLT 🚀