LiDAR-Guide
darkflow
| LiDAR-Guide | darkflow | |
|---|---|---|
| 1 | 2 | |
| 57 | 6,128 | |
| - | - | |
| 3.2 | 0.0 | |
| 4 months ago | 6 months ago | |
| Python | Python | |
| - | GNU General Public License v3.0 only |
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LiDAR-Guide
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?
VeloView - VeloView performs real-time visualization and easy processing of live captured 3D LiDAR data from Velodyne sensors (Alpha Prime™, Puck™, Ultra Puck™, Puck Hi-Res™, Alpha Puck™, Puck LITE™, HDL-32, HDL-64E). Runs on Windows, Linux and MacOS. This repository is a mirror of https://gitlab.kitware.com/LidarView/VeloView-Velodyne.
saliency - Framework-agnostic implementation for state-of-the-art saliency methods (XRAI, BlurIG, SmoothGrad, and more).
Photogrammetry-Guide - Photogrammetry Guide. Photogrammetry is widely used for Aerial surveying, Agriculture, Architecture, 3D Games, Robotics, Archaeology, Construction, Emergency management, and Medical.
awesome-object-detection - Awesome Object Detection based on handong1587 github: https://handong1587.github.io/deep_learning/2015/10/09/object-detection.html
softlearning - Softlearning is a reinforcement learning framework for training maximum entropy policies in continuous domains. Includes the official implementation of the Soft Actor-Critic algorithm.
resnet1d - PyTorch implementations of several SOTA backbone deep neural networks (such as ResNet, ResNeXt, RegNet) on one-dimensional (1D) signal/time-series data.
awesome-experimental-standards-deep-learning - Repository collecting resources and best practices to improve experimental rigour in deep learning research.
webdriver - A Haskell client for the Selenium WebDriver protocol.
Data-Engineering-Roadmap - Roadmap for Data Engineering
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