yolo-tf2
yolo(all versions) implementation in keras and tensorflow 2.x (by unsignedrant)
deepsparse
Sparsity-aware deep learning inference runtime for CPUs (by neuralmagic)
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| yolo-tf2 | deepsparse | |
|---|---|---|
| 1 | 21 | |
| 747 | 2,858 | |
| - | 2.4% | |
| 7.6 | 9.6 | |
| almost 2 years ago | 7 days ago | |
| Python | Python | |
| MIT License | GNU General Public License v3.0 or later |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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.
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.
yolo-tf2
Posts with mentions or reviews of yolo-tf2.
We have used some of these posts to build our list of alternatives
and similar projects.
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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 ...
deepsparse
Posts with mentions or reviews of deepsparse.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2022-10-28.
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Fast Llama 2 on CPUs with Sparse Fine-Tuning and DeepSparse
Interesting company. Yannic Kilcher interviewed Nir Shavit last year and they went into some depth: https://www.youtube.com/watch?v=0PAiQ1jTN5k DeepSparse is on GitHub: https://github.com/neuralmagic/deepsparse
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The future of quantization techniques in deep learning.
sparsity https://github.com/neuralmagic/deepsparse
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[D] How to get the fastest PyTorch inference and what is the "best" model serving framework?
For 1), what is the easiest way to speed up inference (assume only PyTorch and primarily GPU but also some CPU)? I have been using ONNX and Torchscript but there is a bit of a learning curve and sometimes it can be tricky to get the model to actually work. Is there anything else worth trying? I am enthused by things like TorchDynamo (although I have not tested it extensively) due to its apparent ease of use. I also saw the post yesterday about Kernl using (OpenAI) Triton kernels to speed up transformer models which also looks interesting. Are things like SageMaker Neo or NeuralMagic worth trying? My only reservation with some of these is they still seem to be pretty model/architecture specific. I am a little reluctant to put much time into these unless I know others have had some success first.
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[D] Most efficient open source language model ?
You should look into deepsparse, they are working on delivering GPU level performance on consumer CPUs with some great results: https://github.com/neuralmagic/deepsparse. There is a great interview with the founder, Nir Shavit here: https://piped.kavin.rocks/watch?v=0PAiQ1jTN5k
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[R] New sparsity research (oBERT) enabled 175X increase in CPU performance for MLPerf submission
Utilizing the oBERT research we published at Neural Magic and some further iteration, we’ve enabled an increase in NLP performance of 175X while retaining 99% accuracy on the question-answering task in MLPerf. A combination of distillation, layer dropping, quantization, and unstructured pruning with oBERT enabled these large performance gains through the DeepSparse Engine. All of our contributions and research are open-sourced or free to use. Read through the oBERT paper on arxiv, try out the research in SparseML, and dive into the writeup to learn more about how we achieved these impressive results and utilize them for your own use cases!
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An open-source library for optimizing deep learning inference. (1) You select the target optimization, (2) nebullvm searches for the best optimization techniques for your model-hardware configuration, and then (3) serves an optimized model that runs much faster in inference
Open-source projects leveraged by nebullvm include OpenVINO, TensorRT, Intel Neural Compressor, SparseML and DeepSparse, Apache TVM, ONNX Runtime, TFlite and XLA. A huge thank you to the open-source community for developing and maintaining these amazing projects.
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[R] BERT-Large: Prune Once for DistilBERT Inference Performance
BERT-Large (345 million parameters) is now faster than the much smaller DistilBERT (66 million parameters) all while retaining the accuracy of the much larger BERT-Large model! We made this possible with Intel Labs by applying cutting-edge sparsification and quantization research from their Prune Once For All paper and utilizing it in the DeepSparse engine. It makes BERT-Large 12x smaller while delivering 8x latency speedup on commodity CPUs. We open-sourced the research in SparseML; run through the overview here and give it a try!
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[R] How well do sparse ImageNet models transfer? Prune once and deploy anywhere for inference performance speedups! (arxiv link in comments)
And benchmark/deploy with 8X better performance in DeepSparse!
- Sparseserver.ui – test the performance of Sparse Transformers
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[P] SparseServer.UI : A UI to test performance of Sparse Transformers
Hi _Arsenie, this runs the deepsparse.server command for multiple models. and btw, we recently updated the READMEs for the Deepsparse Engine https://github.com/neuralmagic/deepsparse
What are some alternatives?
When comparing yolo-tf2 and deepsparse you can also consider the following projects:
Deep-SORT-YOLOv4 - People detection and optional tracking with Tensorflow backend.
NudeNet - Neural Nets for Nudity Detection and Censoring
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.
yolov5 - YOLOv5 🚀 in PyTorch > ONNX > CoreML > TFLite
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
openvino - OpenVINO™ is an open-source toolkit for optimizing and deploying AI inference
yolor - implementation of paper - You Only Learn One Representation: Unified Network for Multiple Tasks (https://arxiv.org/abs/2105.04206)
model-optimization - A toolkit to optimize ML models for deployment for Keras and TensorFlow, including quantization and pruning.
FastMOT - High-performance multiple object tracking based on YOLO, Deep SORT, and KLT 🚀
sparseml - Libraries for applying sparsification recipes to neural networks with a few lines of code, enabling faster and smaller models
onnx-tensorflow - Tensorflow Backend for ONNX
tvm - Open deep learning compiler stack for cpu, gpu and specialized accelerators
yolo-tf2 vs Deep-SORT-YOLOv4
deepsparse vs NudeNet
yolo-tf2 vs Real-time-Object-Detection-for-Autonomous-Driving-using-Deep-Learning
deepsparse vs yolov5
yolo-tf2 vs Beginner-Traffic-Light-Detection-OpenCV-YOLOv3
deepsparse vs openvino
yolo-tf2 vs yolor
deepsparse vs model-optimization
yolo-tf2 vs FastMOT
deepsparse vs sparseml
yolo-tf2 vs onnx-tensorflow
deepsparse vs tvm