Model Zoo

November 22, 2019 · View on GitHub

This page lists model archives that are pre-trained and pre-packaged, ready to be served for inference with MMS. To propose a model for inclusion, please submit a pull request.

Special thanks to the Apache MXNet community whose Model Zoo and Model Examples were used in generating these model archives.

Model File	Type	Dataset	Source	Size	Download
AlexNet	Image Classification	ImageNet	ONNX	233 MB	.mar
ArcFace-ResNet100	Face Recognition	Refined MS-Celeb1M	ONNX	236.4 MB	.mar
Character-level Convolutional Networks for Text Classification	Text Classification	Amazon Product Data	Gluon	40 MB	.mar
CaffeNet	Image Classification	ImageNet	MXNet	216 MB	.mar
FERPlus	Emotion Detection	FER2013	ONNX	35MB	.mar
Inception v1	Image Classification	ImageNet	ONNX	27 MB	.mar
Inception v3 w/BatchNorm	Image Classification	ImageNet	MXNet	45 MB	.mar
LSTM PTB	Language Modeling	PennTreeBank	MXNet	16 MB	.mar
MobileNetv2-1.0	Image Classification	ImageNet	ONNX	13.7 MB	.mar
Network in Network (NiN)	Image Classification	ImageNet	MXNet	30 MB	.mar
ResNet-152	Image Classification	ImageNet	MXNet	241 MB	.mar
ResNet-18	Image Classification	ImageNet	MXNet	43 MB	.mar
ResNet50-SSD	SSD (Single Shot MultiBox Detector)	ImageNet	MXNet	124 MB	.mar
ResNext101-64x4d	Image Classification	ImageNet	MXNet	334 MB	.mar
ResNet-18v1	Image Classification	ImageNet	ONNX	45 MB	.mar
ResNet-34v1	Image Classification	ImageNet	ONNX	83 MB	.mar
ResNet-50v1	Image Classification	ImageNet	ONNX	98 MB	.mar
ResNet-101v1	Image Classification	ImageNet	ONNX	171 MB	.mar
ResNet-152v1	Image Classification	ImageNet	ONNX	231 MB	.mar
ResNet-18v2	Image Classification	ImageNet	ONNX	45 MB	.mar
ResNet-34v2	Image Classification	ImageNet	ONNX	83 MB	.mar
ResNet-50v2	Image Classification	ImageNet	ONNX	98 MB	.mar
ResNet-101v2	Image Classification	ImageNet	ONNX	171 MB	.mar
ResNet-152v2	Image Classification	ImageNet	ONNX	231 MB	.mar
Shufflenet	Image Classification	ImageNet	ONNX	8.1 MB	.mar
SqueezeNet_v1.1	Image Classification	ImageNet	ONNX	5 MB	.mar
SqueezeNet v1.1	Image Classification	ImageNet	MXNet	5 MB	.mar
VGG16	Image Classification	ImageNet	MXNet	490 MB	.mar
VGG16	Image Classification	ImageNet	ONNX	527 MB	.mar
VGG16_bn	Image Classification	ImageNet	ONNX	527 MB	.mar
VGG19	Image Classification	ImageNet	MXNet	509 MB	.mar
VGG19	Image Classification	ImageNet	ONNX	548 MB	.mar
VGG19_bn	Image Classification	ImageNet	ONNX	548 MB	.mar

Details on Each Model

Each model below comes with a basic description, and where available, a link to a scholarly article about the model.

Many of these models use a kitten image to test inference. Use the following to get one that will work:

curl -O https://s3.amazonaws.com/model-server/inputs/kitten.jpg

AlexNet

Type: Image classification trained on ImageNet
Reference: Krizhevsky, et al.
Model Service: mxnet_vision_service.py
Start Server:

multi-model-server --start --models alexnet=https://s3.amazonaws.com/model-server/model_archive_1.0/alexnet.mar

Run Prediction:

curl -X POST http://127.0.0.1:8080/predictions/alexnet -T kitten.jpg

ArcFace-ResNet100 (from ONNX model zoo)

Type: Face Recognition model trained on refined MS-Celeb1M dataset (model imported from ONNX)
Reference: Deng et al.
Model Service:
- arcface_service.py
- mtcnn_detector.py
Install dependencies:

pip install opencv-python
pip install scikit-learn
pip install easydict
pip install scikit-image
pip install numpy

Start Server:

multi-model-server --start --models arcface=https://s3.amazonaws.com/model-server/model_archive_1.0/onnx-arcface-resnet100.mar

Get two test images:

curl -O https://s3.amazonaws.com/model-server/inputs/arcface-input1.jpg

curl -O https://s3.amazonaws.com/model-server/inputs/arcface-input2.jpg

Run Prediction:

curl -X POST http://127.0.0.1:8080/predictions/arcface -F "img1=@arcface-input1.jpg" -F "img2=@arcface-input2.jpg"

CaffeNet

Type: Image classification trained on ImageNet
Reference: Krizhevsky, et al.
Model Service: mxnet_vision_service.py
Start Server:

multi-model-server --start --models caffenet=https://s3.amazonaws.com/model-server/model_archive_1.0/caffenet.mar

Run Prediction:

curl -X POST http://127.0.0.1:8080/predictions/caffenet -T kitten.jpg

Character-level Convolutional Networks for text Classification

Type: Character-level Convolutional network for text classification trained on Amazon Product Data.
Reference: R. He, J. McAuley et al., J. McAuley, C. Targett, J. Shi, A. van den Hengel et al.
Model Service: gluon_crepe.py
Start Server:

multi-model-server --start --models crepe=https://s3.amazonaws.com/model-server/model_archive_1.0/crepe.mar

Run Prediction:

curl -X POST http://127.0.0.1:8080/predictions/crepe -F "data=[{\"review_title\":\"Inception is the best\",\"review\": \"great direction and story\"}]"

DUC-ResNet101 (from ONNX model zoo)

Type: Semantic Segmentation model trained on the Cityscapes dataset (model imported from ONNX)
Reference: Wang et al.
Model Service:
- duc_service.py
- cityscapes_labels.py
Install dependencies:

pip install opencv-python
pip install pillow

Start Server:

multi-model-server --models duc=https://s3.amazonaws.com/model-server/model_archive_1.0/onnx-duc.mar

Get the test image:

curl -O https://s3.amazonaws.com/multi-model-server/onnx-duc/city1.jpg

Download inference script:

The script makes an inference call to the server using the test image, displays the colorized segmentation map and prints the confidence score.

curl -O https://s3.amazonaws.com/multi-model-server/onnx-duc/duc-inference.py

Run Prediction:

python duc-inference.py city1.jpg

FERPlus

Type: Emotion detection trained on FER2013 dataset (model imported from ONNX)
Reference: Barsoum et al.
Model Service: emotion_detection_service.py
Start Server:

multi-model-server --start --models FERPlus=https://s3.amazonaws.com/model-server/model_archive_1.0/FERPlus.mar

Get a test image:

curl -O https://s3.amazonaws.com/model-server/inputs/ferplus-input.jpg

Run Prediction:

curl -X POST http://127.0.0.1:8080/predictions/FERPlus -T ferplus-input.jpg

Inception v1

Type: Image classification trained on ImageNet
Reference: Szegedy, et al., Google
Model Service: mxnet_vision_service.py
Start Server:

multi-model-server --start --models onnx-inception-v1=https://s3.amazonaws.com/model-server/model_archive_1.0/onnx-inception_v1.mar

Run Prediction:

curl -X POST http://127.0.0.1:8080/predictions/onnx-inception-v1 -T kitten.jpg

Inception v3

Type: Image classification trained on ImageNet
Reference: Szegedy, et al., Google
Model Service: mxnet_vision_service.py
Start Server:

multi-model-server --start --models inception-bn=https://s3.amazonaws.com/model-server/model_archive_1.0/inception-bn.mar

Run Prediction:

curl -X POST http://127.0.0.1:8080/predictions/inception-bn -T kitten.jpg

LSTM PTB

Long short-term memory network trained on the PennTreeBank dataset.

Reference: Hochreiter, et al.
Model Service: lstm_ptb_service.py
Start Server:

multi-model-server --start --models lstm_ptb=https://s3.amazonaws.com/model-server/model_archive_1.0/lstm_ptb.mar

Run Prediction:

curl -X POST http://127.0.0.1:8080/predictions/lstm_ptb -H "Content-Type: application/json" -d '[{"input_sentence": "on the exchange floor as soon as ual stopped trading we <unk> for a panic said one top floor trader"}]'

MobileNetv2-1.0 (from ONNX model zoo)

Type: Image classification trained on ImageNet (imported from ONNX)
Reference: Sandler et al.
Model Service: mxnet_vision_service.py
Start Server:

multi-model-server --start --models mobilenet=https://s3.amazonaws.com/model-server/model_archive_1.0/onnx-mobilenet.mar

Run Prediction:

curl -X POST http://127.0.0.1:8080/predictions/mobilenet -T kitten.jpg

Network in Network

Type: Image classification trained on ImageNet
Reference: Lin, et al.
Model Service: mxnet_vision_service.py
Start Server:

multi-model-server --start --models nin=https://s3.amazonaws.com/model-server/model_archive_1.0/nin.mar

Run Prediction:

curl -X POST http://127.0.0.1:8080/predictions/nin -T kitten.jpg

ResNet-152

Type: Image classification trained on ImageNet
Reference: Lin, et al.
Model Service: mxnet_vision_service.py
Start Server:

multi-model-server --start --models resnet-152=https://s3.amazonaws.com/model-server/model_archive_1.0/resnet-152.mar

Run Prediction:

curl -X POST http://127.0.0.1:8080/predictions/resnet-152 -T kitten.jpg

ResNet-18

Type: Image classification trained on ImageNet
Reference: He, et al.
Model Service: mxnet_vision_service.py
Start Server:

multi-model-server --start --models resnet-18=https://s3.amazonaws.com/model-server/model_archive_1.0/resnet-18.mar

Run Prediction:

curl -X POST http://127.0.0.1:8080/predictions/resnet-18 -T kitten.jpg

ResNet50-SSD

Type: Image classification trained on ImageNet
Reference: Liu, et al.
Model Service: ssd_service.py
Start Server:

multi-model-server --start --models SSD=https://s3.amazonaws.com/model-server/model_archive_1.0/resnet50_ssd.mar

Run Prediction:

curl -O https://www.dphotographer.co.uk/users/21963/thm1024/1337890426_Img_8133.jpg

curl -X POST http://127.0.0.1:8080/predictions/SSD -T 1337890426_Img_8133.jpg

ResNext101-64x4d

Type: Image classification trained on ImageNet
Reference: Xie, et al.
Model Service: mxnet_vision_service.py
Start Server:

multi-model-server --start --models resnext101=https://s3.amazonaws.com/model-server/model_archive_1.0/resnext-101-64x4d.mar

Run Prediction:

curl -X POST http://127.0.0.1:8080/predictions/resnext101 -T kitten.jpg

ResNet (from ONNX model zoo)

ResNet18-v1

Type: Image classification trained on ImageNet (imported from ONNX)
Reference: He, et al.
Model Service: mxnet_vision_service.py
Start Server:

multi-model-server --start --models resnet18-v1=https://s3.amazonaws.com/model-server/model_archive_1.0/onnx-resnet18v1.mar

Run Prediction:

curl -X POST http://127.0.0.1:8080/predictions/resnet18-v1 -T kitten.jpg

ResNet34-v1

Type: Image classification trained on ImageNet (imported from ONNX)
Reference: He, et al.
Model Service: mxnet_vision_service.py
Start Server:

multi-model-server --start --models resnet34-v1=https://s3.amazonaws.com/model-server/model_archive_1.0/onnx-resnet34v1.mar

Run Prediction:

curl -X POST http://127.0.0.1:8080/predictions/resnet34-v1 -T kitten.jpg

ResNet50-v1

Type: Image classification trained on ImageNet (imported from ONNX)
Reference: He, et al.
Model Service: mxnet_vision_service.py
Start Server:

multi-model-server --start --models resnet50-v1=https://s3.amazonaws.com/model-server/model_archive_1.0/onnx-resnet50v1.mar

Run Prediction:

curl -X POST http://127.0.0.1:8080/predictions/resnet50-v1 -T kitten.jpg

ResNet101-v1

Type: Image classification trained on ImageNet (imported from ONNX)
Reference: He, et al.
Model Service: mxnet_vision_service.py
Start Server:

multi-model-server --start --models resnet101-v1=https://s3.amazonaws.com/model-server/model_archive_1.0/onnx-resnet101v1.mar

Run Prediction:

curl -X POST http://127.0.0.1:8080/predictions/resnet101-v1 -T kitten.jpg

ResNet152-v1

Type: Image classification trained on ImageNet (imported from ONNX)
Reference: He, et al.
Model Service: mxnet_vision_service.py
Start Server:

multi-model-server --start --models resnet152-v1=https://s3.amazonaws.com/model-server/model_archive_1.0/onnx-resnet152v1.mar

Run Prediction:

curl -X POST http://127.0.0.1:8080/predictions/resnet152-v1 -T kitten.jpg

ResNet18-v2

Type: Image classification trained on ImageNet (imported from ONNX)
Reference: He, et al.
Model Service: mxnet_vision_service.py
Start Server:

multi-model-server --start --models resnet18-v2=https://s3.amazonaws.com/model-server/model_archive_1.0/onnx-resnet18v2.mar

Run Prediction:

curl -X POST http://127.0.0.1:8080/predictions/resnet18-v2 -T kitten.jpg

ResNet34-v2

Type: Image classification trained on ImageNet (imported from ONNX)
Reference: He, et al.
Model Service: mxnet_vision_service.py
Start Server:

multi-model-server --start --models resnet34-v2=https://s3.amazonaws.com/model-server/model_archive_1.0/onnx-resnet34v2.mar

Run Prediction:

curl -X POST http://127.0.0.1:8080/predictions/resnet34-v2 -T kitten.jpg

ResNet50-v2

Type: Image classification trained on ImageNet (imported from ONNX)
Reference: He, et al.
Model Service: mxnet_vision_service.py
Start Server:

multi-model-server --start --models resnet50-v2=https://s3.amazonaws.com/model-server/model_archive_1.0/onnx-resnet50v2.mar

Run Prediction:

curl -X POST http://127.0.0.1:8080/predictions/resnet50-v2 -T kitten.jpg

ResNet101-v2

Type: Image classification trained on ImageNet (imported from ONNX)
Reference: He, et al.
Model Service: mxnet_vision_service.py
Start Server:

multi-model-server --start --models resnet101-v2=https://s3.amazonaws.com/model-server/model_archive_1.0/onnx-resnet101v2.mar

Run Prediction:

curl -X POST http://127.0.0.1:8080/predictions/resnet101-v2 -T kitten.jpg

ResNet152-v2

Type: Image classification trained on ImageNet (imported from ONNX)
Reference: He, et al.
Model Service: mxnet_vision_service.py
Start Server:

multi-model-server --start --models resnet152-v2=https://s3.amazonaws.com/model-server/model_archive_1.0/onnx-resnet152v2.mar

Run Prediction:

curl -X POST http://127.0.0.1:8080/predictions/resnet152-v2 -T kitten.jpg

Shufflenet_v2

Type: Image classification trained on ImageNet
Reference: Zhang, et al.
Model Service: mxnet_vision_service.py
Start Server:

multi-model-server --start --models shufflenet=https://s3.amazonaws.com/model-server/model_archive_1.0/shufflenet.mar

Run Prediction:

curl -X POST http://127.0.0.1:8080/predictions/shufflenet -T kitten.jpg

SqueezeNet v1.1

Type: Image classification trained on ImageNet
Reference: Iandola, et al.
Model Service: mxnet_vision_service.py
Start Server:

multi-model-server --start --models squeezenet_v1.1=https://s3.amazonaws.com/model-server/model_archive_1.0/squeezenet_v1.1.mar

Run Prediction:

curl -X POST http://127.0.0.1:8080/predictions/squeezenet_v1.1 -T kitten.jpg

SqueezeNet v1.1 (from ONNX model zoo)

Type: Image classification trained on ImageNet (imported from ONNX)
Reference: Iandola, et al.
Model Service: mxnet_vision_service.py
Start Server:

multi-model-server --start --models onnx-squeezenet=https://s3.amazonaws.com/model-server/model_archive_1.0/onnx-squeezenet.mar

Run Prediction:

curl -X POST http://127.0.0.1:8080/predictions/onnx-squeezenet -T kitten.jpg

VGG16

Type: Image classification trained on ImageNet
Reference: Simonyan, et al.
Model Service: mxnet_vision_service.py
Start Server:

multi-model-server --start --models vgg16=https://s3.amazonaws.com/model-server/model_archive_1.0/vgg16.mar

Run Prediction:

curl -X POST http://127.0.0.1:8080/predictions/vgg16 -T kitten.jpg

VGG19

Type: Image classification trained on ImageNet
Reference: Simonyan, et al.
Model Service: mxnet_vision_service.py
Start Server:

multi-model-server --start --models vgg19=https://s3.amazonaws.com/model-server/model_archive_1.0/vgg19.mar

Run Prediction:

curl -X POST http://127.0.0.1:8080/predictions/vgg19 -T kitten.jpg

VGG (from ONNX model zoo)

VGG16

Type: Image classification trained on ImageNet (imported from ONNX)
Reference: Simonyan, et al.
Model Service: mxnet_vision_service.py
Start Server:

multi-model-server --start --models onnx-vgg16=https://s3.amazonaws.com/model-server/model_archive_1.0/onnx-vgg16.mar

Run Prediction:

curl -X POST http://127.0.0.1:8080/predictions/onnx-vgg16 -T kitten.jpg

VGG16_bn

Type: Image classification trained on ImageNet (imported from ONNX)
Reference: Simonyan, et al. (Batch normalization applied after each conv layer of VGG16)
Model Service: mxnet_vision_service.py
Start Server:

multi-model-server --start --models onnx-vgg16_bn=https://s3.amazonaws.com/model-server/model_archive_1.0/onnx-vgg16_bn.mar

Run Prediction:

curl -X POST http://127.0.0.1:8080/predictions/onnx-vgg16_bn -T kitten.jpg

VGG19

Type: Image classification trained on ImageNet (imported from ONNX)
Reference: Simonyan, et al.
Model Service: mxnet_vision_service.py
Start Server:

multi-model-server --start --models onnx-vgg19=https://s3.amazonaws.com/model-server/model_archive_1.0/onnx-vgg19.mar

Run Prediction:

curl -X POST http://127.0.0.1:8080/predictions/onnx-vgg19 -T kitten.jpg

VGG19_bn

Type: Image classification trained on ImageNet (imported from ONNX)
Reference: Simonyan, et al. (Batch normalization applied after each conv layer of VGG19)
Model Service: mxnet_vision_service.py
Start Server:

multi-model-server --start --models onnx-vgg19_bn=https://s3.amazonaws.com/model-server/model_archive_1.0/onnx-vgg19_bn.mar

Run Prediction:

curl -X POST http://127.0.0.1:8080/predictions/onnx-vgg19_bn -T kitten.jpg