darkflow
PixelLib
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darkflow | PixelLib | |
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2 | 3 | |
6,126 | 1,014 | |
- | - | |
0.0 | 0.0 | |
6 months ago | 7 months 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.
PixelLib
- YOLOv6: Redefine state-of-the-art for object detection
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To separate objects detected from a video using PixelLib
This is the code, found from the reference here.
- New Project In Computer Vision For Beginner
What are some alternatives?
saliency - Framework-agnostic implementation for state-of-the-art saliency methods (XRAI, BlurIG, SmoothGrad, and more).
Human-Segmentation-PyTorch - Human segmentation models, training/inference code, and trained weights, implemented in PyTorch
awesome-object-detection - Awesome Object Detection based on handong1587 github: https://handong1587.github.io/deep_learning/2015/10/09/object-detection.html
sahi - Framework agnostic sliced/tiled inference + interactive ui + error analysis plots
resnet1d - PyTorch implementations of several SOTA backbone deep neural networks (such as ResNet, ResNeXt, RegNet) on one-dimensional (1D) signal/time-series data.
FasterRCNN - Clean and readable implementations of Faster R-CNN in PyTorch and TensorFlow 2 with Keras.
webdriver - A Haskell client for the Selenium WebDriver protocol.
rembg-greenscreen - Rembg Video Virtual Green Screen Edition
YOLOv3_TensorFlow - Complete YOLO v3 TensorFlow implementation. Support training on your own dataset.
mask-rcnn - Mask-RCNN training and prediction in MATLAB for Instance Segmentation
gluon-cv - Gluon CV Toolkit
fashion-segmentation - A tensorflow model for segmentation of fashion items out of multiple product images