Skip to content

Latest commit

 

History

History
111 lines (88 loc) · 23.2 KB

README.md

File metadata and controls

111 lines (88 loc) · 23.2 KB
Raw

Introduction

This is a collection of pre-trained models in different deep learning frameworks.

You can download the model you want by simply click the download link.

With the download model, you can convert them to different frameworks.

Next session show an example to show you how to convert pre-trained model between frameworks.

Steps to Convert Model

Example: Convert vgg19 model from Tensorflow to CNTK

  1. Install the stable version of MMdnn

    pip install mmdnn

  2. Download Tensorflow pre-trained model

    • Method 1: Directly download from below model collection
    • Method 2: Use command line
        $ mmdownload -f tensorflow -n vgg19

    Downloading file [./vgg_19_2016_08_28.tar.gz] from [http://download.tensorflow.org/models/vgg_19_2016_08_28.tar.gz]
    progress: 520592.0 KB downloaded, 100%
    Model saved in file: ./imagenet_vgg19.ckpt

    NOTICE: the model name after the '-n' argument must be the models appearence in the below model collection.

  3. Convert model architecture(*.ckpt.meta) and weights(.ckpt) from Tensorflow to IR

    $ mmtoir -f tensorflow -d vgg19 -n imagenet_vgg19.ckpt.meta -w imagenet_vgg19.ckpt  --dstNodeName MMdnn_Output

Parse file [imagenet_vgg19.ckpt.meta] with binary format successfully.
Tensorflow model file [imagenet_vgg19.ckpt.meta] loaded successfully.
Tensorflow checkpoint file [imagenet_vgg19.ckpt] loaded successfully. [38] variables loaded.
IR network structure is saved as [vgg19.json].
IR network structure is saved as [vgg19.pb].
IR weights are saved as [vgg19.npy].

  4. Convert models from IR to PyTorch code snippet and weights

    $ mmtocode -f pytorch -n vgg19.pb --IRWeightPath vgg19.npy --dstModelPath pytorch_vgg19.py -dw pytorch_vgg19.npy

Parse file [vgg19.pb] with binary format successfully.
Target network code snippet is saved as [pytorch_vgg19.py].
Target weights are saved as [pytorch_vgg19.npy].

  5. Generate PyTorch model from code snippet file and weight file

    $ mmtomodel -f pytorch -in pytorch_vgg19.py -iw pytorch_vgg19.npy --o pytorch_vgg19.pth

PyTorch model file is saved as [pytorch_vgg19.pth], generated by [pytorch_vgg19.py] and [pytorch_vgg19.npy].
Notice that you may need [pytorch_vgg19.py] to load the model back.

Model Collection

Image Classification

imagenet
alexnet
Framework: caffe
Download: prototxt caffemodel
Source: Link
 inception_v1
Framework: caffe
Download: prototxt caffemodel
Source: Link
 vgg16
Framework: caffe
Download: prototxt caffemodel
Source: Link
vgg19
Framework: caffe
Download: prototxt caffemodel
Source: Link
 resnet50
Framework: caffe
Download: prototxt caffemodel
Source: Link
 resnet101
Framework: caffe
Download: prototxt caffemodel
Source: Link
resnet152
Framework: caffe
Download: prototxt caffemodel
Source: Link
 squeezenet
Framework: caffe
Download: prototxt caffemodel
Source: Link
 xception
Framework: caffe
Download: prototxt caffemodel
Source:
inception_v4
Framework: caffe
Download: prototxt caffemodel
Source:
alexnet
Framework: cntk
Download: model
Source: Link
 inception_v3
Framework: cntk
Download: model
Source: Link
resnet18
Framework: cntk
Download: model
Source: Link
 resnet50
Framework: cntk
Download: model
Source: Link
 resnet101
Framework: cntk
Download: model
Source: Link
resnet152
Framework: cntk
Download: model
Source: Link
 inception_v3
Framework: coreml
Download: mlmodel
Source:
vgg16
Framework: coreml
Download: mlmodel
Source: Link
resnet50
Framework: coreml
Download: mlmodel
Source: Link
 mobilenet
Framework: coreml
Download: mlmodel
Source: Link
 imagenet1k-inception-bn
Framework: mxnet
Download: json params
Source: Link
imagenet1k-resnet-18
Framework: mxnet
Download: json params
Source: Link
 imagenet1k-resnet-34
Framework: mxnet
Download: json params
Source: Link
 imagenet1k-resnet-50
Framework: mxnet
Download: json params
Source: Link
imagenet1k-resnet-101
Framework: mxnet
Download: json params
Source: Link
 imagenet1k-resnet-152
Framework: mxnet
Download: json params
Source: Link
 imagenet1k-resnext-50
Framework: mxnet
Download: json params
Source: Link
imagenet1k-resnext-101
Framework: mxnet
Download: json params
Source: Link
 imagenet1k-resnext-101-64x4d
Framework: mxnet
Download: json params
Source: Link
 vgg19
Framework: mxnet
Download: json params
Source: Link
vgg16
Framework: mxnet
Download: json params
Source: Link
 squeezenet_v1.0
Framework: mxnet
Download: json params
Source: Link
 squeezenet_v1.1
Framework: mxnet
Download: json params
Source: Link
alexnet
Framework: pytorch
Download: pth
Source: Link
 densenet121
Framework: pytorch
Download: pth
Source: Link
 densenet169
Framework: pytorch
Download: pth
Source: Link
densenet201
Framework: pytorch
Download: pth
Source: Link
 densenet161
Framework: pytorch
Download: pth
Source: Link
 inception_v3
Framework: pytorch
Download: pth
Source: Link
resnet18
Framework: pytorch
Download: pth
Source: Link
 resnet34
Framework: pytorch
Download: pth
Source: Link
 resnet50
Framework: pytorch
Download: pth
Source: Link
resnet101
Framework: pytorch
Download: pth
Source: Link
 resnet152
Framework: pytorch
Download: pth
Source: Link
 squeezenet1_0
Framework: pytorch
Download: pth
Source: Link
squeezenet1_1
Framework: pytorch
Download: pth
Source: Link
 vgg11
Framework: pytorch
Download: pth
Source: Link
 vgg13
Framework: pytorch
Download: pth
Source: Link
vgg16
Framework: pytorch
Download: pth
Source: Link
 vgg19
Framework: pytorch
Download: pth
Source: Link
 vgg11_bn
Framework: pytorch
Download: pth
Source: Link
vgg13_bn
Framework: pytorch
Download: pth
Source: Link
 vgg16_bn
Framework: pytorch
Download: pth
Source: Link
 vgg19_bn
Framework: pytorch
Download: pth
Source: Link
vgg16
Framework: tensorflow
Download: tgz
Source: Link
 vgg19
Framework: tensorflow
Download: tgz
Source: Link
 inception_v1
Framework: tensorflow
Download: tgz
Source: Link
inception_v1_frozen
Framework: tensorflow
Download: tgz
Source: Link
 inception_v3
Framework: tensorflow
Download: tgz
Source: Link
 inception_v3_frozen
Framework: tensorflow
Download: tgz
Source: Link
resnet_v1_50
Framework: tensorflow
Download: tgz
Source: Link
 resnet_v1_152
Framework: tensorflow
Download: tgz
Source: Link
 resnet_v2_50
Framework: tensorflow
Download: tgz
Source: Link
resnet_v2_152
Framework: tensorflow
Download: tgz
Source: Link
 resnet_v2_200
Framework: tensorflow
Download: tgz
Source: Link
 mobilenet_v1_1.0
Framework: tensorflow
Download: tgz
Source: Link
mobilenet_v1_1.0_frozen
Framework: tensorflow
Download: tgz
Source: Link
 mobilenet_v2_1.0_224
Framework: tensorflow
Download: tgz
Source: Link
 inception_resnet_v2
Framework: tensorflow
Download: tgz
Source: Link
nasnet-a_large
Framework: tensorflow
Download: tgz
Source: Link

imagenet11k
imagenet11k-resnet-152
Framework: mxnet
Download: json params
Source: Link
 imagenet11k-place365ch-resnet-152
Framework: mxnet
Download: json params
Source: Link
 imagenet11k-place365ch-resnet-50
Framework: mxnet
Download: json params
Source: Link

Object Detection

Pascal VOC
voc-fcn8s
Framework: caffe
Download: prototxt caffemodel
Source: Link
 voc-fcn16s
Framework: caffe
Download: prototxt caffemodel
Source: Link
 voc-fcn32s
Framework: caffe
Download: prototxt caffemodel
Source: Link
Fast-RCNN_Pascal
Framework: cntk
Download: model
Source: Link
 tinyyolo
Framework: coreml
Download: mlmodel
Source: Link
 yolov3
Framework: darknet
Download: cfg weights
Source: Link
yolov2
Framework: darknet
Download: cfg weights
Source: Link

grocery100
Fast-RCNN_grocery100
Framework: cntk
Download: model
Source: Link