Visualizer for neural network, deep learning, and machine learning models
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Updated
Aug 20, 2021 - JavaScript
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Visualizer for neural network, deep learning, and machine learning models
Caffe2 is a lightweight, modular, and scalable deep learning framework.
Set up deep learning environment in a single command line.
The convertor/conversion of deep learning models for different deep learning frameworks/softwares.
In this repository, I will share some useful notes and references about deploying deep learning-based models in production.
Pretrained EfficientNet, EfficientNet-Lite, MixNet, MobileNetV3 / V2, MNASNet A1 and B1, FBNet, Single-Path NAS
nGraph has moved to OpenVINO
Samples and Tools for Windows ML.
Python library to easily log experiments and parallelize hyperparameter search for neural networks
PyTorch implementation of the YOLO (You Only Look Once) v2
Caffe2 on iOS Real-time Demo. Test with Your Own Model and Photos.
Caffe2 implementation of Open Neural Network Exchange (ONNX)
Deep Learning Benchmarking Suite
[EXPERIMENTAL] Demo of using PyTorch 1.0 inside an Android app. Test with your own deep neural network such as ResNet18/SqueezeNet/MobileNet v2 and a phone camera.
Pytorch implementation of the DeepDream computer vision algorithm
PyTorch implementation of the OpenPose
Show how to use Caffe2 in C++ through a simple LeNet sample project
CNN model inference benchmarks for some popular deep learning frameworks
React native package to use caffe2 models. Use deep learning on hybrid mobile application within 2 min
Domain Adaptive Faster R-CNN in Detectron
Tensorboard for Caffe2
A simple human recognition api for re-ID usage, power by paper https://arxiv.org/abs/1703.07737
Collection of models and extensions for mobile deployment in PyTorch
A playground for continual, interactive neuroevolution
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The tensor-decode/bounding-box option error reported with
https://lists.lfaidata.foundation/g/nnstreamer-technical-discuss/message/43
had an incorrect option property value.
The problem is that the error was not much visible and most users won't be able to know what's wrong with it.
We need explicit error messages with such cases.
We not only need such clear error messages with bounding-b