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onnx / onnx

Star 13k
Open

Enable ONNX Hub to download test_data_set from ONNX Model Zoo

jcwchen
jcwchen commented Jul 28, 2022

Feature Request

System information

ONNX version (you are using): 1.12

What is the problem that this feature solves?

It would be great if ONNX Hub can download test_data_set. It will be helpful for testing ONNX Model Zoo.

Describe the feature

Currently users can only download models through ONNX Hub. There is no easy API for them to download the test_data_set from.tar.gz

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good first issue enhancement onnx hub
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SynapseML

microsoft / SynapseML

Star 3.4k
Open

Python tests on build server are not using local maven repo to get scala code

2
mhamilton723
mhamilton723 commented Apr 12, 2022

Describe the bug
A clear and concise description of what the bug is.

To Reproduce
Steps to reproduce the behavior, code snippets encouraged

Expected behavior
A clear and concise description of what you expected to happen.

Info (please complete the following information):

  • SynapseML Version: [e.g. v0.17]
  • Spark Version [e.g. 2.4.3]
  • Spark Platform [e.g. Databric
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bug help wanted good first issue area/build
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PINTO_model_zoo

PINTO0309 / PINTO_model_zoo

Sponsor
Star 2k

A repository for storing models that have been inter-converted between various frameworks. Supported frameworks are TensorFlow, PyTorch, ONNX, OpenVINO, TFJS, TFTRT, TensorFlowLite (Float32/16/INT8), EdgeTPU, CoreML.

caffe computer-vision model-zoo tensorflow model models keras pytorch pretrained-models coreml onnx tensorflow-lite tflite tensorflowjs tfjs openvino edgetpu mediapipe tflite-models tf-trt
  • Updated Jul 29, 2022
  • Python

DefTruth / lite.ai.toolkit

Star 1.9k
Open

👉CONTRIBUTING (如何添加你的模型？)

11
DefTruth
DefTruth commented Feb 13, 2022

这个issue主要讲一下，如何把你自己的模型添加到lite.ai.toolkit。lite.ai.toolkit集成了一些比较新的基础模型，比如人脸检测、人脸识别、抠图、人脸属性分析、图像分类、人脸关键点识别、图像着色、目标检测等等，可以直接用到具体的场景中。但是，毕竟lite.ai.toolkit的模型还是有限的，具体的场景下，可能有你经过优化的模型，比如你自己训了一个目标检测器，可能效果更好。那么，如何把你的模型加入到lite.ai.toolkit中呢？这样既能用到lite.ai.toolkit一些已有的算法能力，也能兼容您的具体场景。这个issue主要是讲这个问题。大家有疑惑的可以提在这个issue，我会尽可能回答~

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good first issue

666DZY666 / micronet

Star 1.8k

micronet, a model compression and deploy lib. compression: 1、quantization: quantization-aware-training(QAT), High-Bit(>2b)(DoReFa/Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference)、Low-Bit(≤2b)/Ternary and Binary(TWN/BNN/XNOR-Net); post-training-quantization(PTQ), 8-bit(tensorrt); 2、 pruning: normal、regular and group convolutional channel pruning; 3、 group convolution structure; 4、batch-normalization fuse for quantization. deploy: tensorrt, fp32/fp16/int8(ptq-calibration)、op-adapt(upsample)、dynamic_shape

pytorch pruning convolutional-networks quantization xnor-net tensorrt model-compression bnn neuromorphic-computing group-convolution onnx network-in-network tensorrt-int8-python dorefa twn network-slimming integer-arithmetic-only quantization-aware-training post-training-quantization batch-normalization-fuse
  • Updated Oct 6, 2021
  • Python
AdvBox

advboxes / AdvBox

Star 1.2k

Advbox is a toolbox to generate adversarial examples that fool neural networks in PaddlePaddle、PyTorch、Caffe2、MxNet、Keras、TensorFlow and Advbox can benchmark the robustness of machine learning models. Advbox give a command line tool to generate adversarial examples with Zero-Coding.

security machine-learning deep-learning paddlepaddle adversarial-example adversarial-examples onnx fgsm adversarial-attacks deepfool graphpipe
  • Updated Jun 21, 2022
  • Jupyter Notebook

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