deepdetect
DALI
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deepdetect | DALI | |
---|---|---|
4 | 5 | |
2,492 | 4,863 | |
0.0% | 1.8% | |
7.0 | 9.6 | |
about 1 month ago | about 20 hours ago | |
C++ | C++ | |
GNU General Public License v3.0 or later | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
deepdetect
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Exploring Open-Source Alternatives to Landing AI for Robust MLOps
For those seeking a lightweight solution for setting up deep learning REST APIs across platforms without the complexity of Kubernetes, Deepdetect is worth considering.
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[D] Deep Learning Framework for C++.
But you need to have good reasons to do it. Ours is that we have a multi-backend framework, and that we don't want any step in between dev & run. C++ allows for this since the same code can run on training server and edge device as needed. It also allows for building full AI applicatioms with great performances (e g. real time) We dev & use https://github.com/jolibrain/deepdetect for these purposes and it serves us very well, but it's not the faint of heart !
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[P] Real-time AR for jewelry virtual try on that looks real, done with joliGAN, based on a few 2D videos and no 3D model
- Real-time is achieved through our full C++ Open Source backend DeepDetect, https://github.com/jolibrain/deepdetect. We use CUDA along with OpenCV and TensorRT to chain multiple models (ring detection and generator mostly), and we make sure the data remain within CUDA memory at all time. This allows us to reach ~60 FPS on 1080Ti and 20% more on average on an RTX3090.
DALI
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mmap_ninja: Speedup your training dramatically by using memory-mapped files for your dataset
Small question if you are using GPU: How to this compare to GPUDirect Storage from Nvidia? can you have even more speedup by using both? I never toy with it, but the DALI project from Nvidia seem to tackle the same data loading problem.
- [D] Efficiently loading videos in PyTorch without extracting frames
What are some alternatives?
ncnn - ncnn is a high-performance neural network inference framework optimized for the mobile platform
netron - Visualizer for neural network, deep learning and machine learning models
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
Blurry - Blurry is an easy blur library for Android
tensorflow-wheels - Tensorflow Wheels
vision - Datasets, Transforms and Models specific to Computer Vision
YoloV7-ncnn-Jetson-Nano - YoloV7 for a Jetson Nano using ncnn.
mmaction2 - OpenMMLab's Next Generation Video Understanding Toolbox and Benchmark
executorch - End-to-end solution for enabling on-device AI across mobile and edge devices for PyTorch models
mdspan - Reference implementation of mdspan targeting C++23
marian - Fast Neural Machine Translation in C++
flashlight - A C++ standalone library for machine learning