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 |
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AlexNet | Image Classification | ImageNet | ONNX | 233 MB | .model |
CaffeNet | Image Classification | ImageNet | MXNet | 216 MB | .model |
Inception v1 | Image Classification | ImageNet | ONNX | 27 MB | .model |
Inception v3 w/BatchNorm | Image Classification | ImageNet | MXNet | 45 MB | .model |
LSTM PTB | Language Modeling | PennTreeBank | MXNet | 16 MB | .model |
Network in Network (NiN) | Image Classification | ImageNet | MXNet | 30 MB | .model |
ResNet-152 | Image Classification | ImageNet | MXNet | 241 MB | .model |
ResNet-18 | Image Classification | ImageNet | MXNet | 43 MB | .model |
ResNet50-SSD | SSD (Single Shot MultiBox Detector) | ImageNet | MXNet | 124 MB | .model |
ResNext101-64x4d | Image Classification | ImageNet | MXNet | 334 MB | .model |
SqueezeNet | Image Classification | ImageNet | ONNX | 5 MB | .model |
SqueezeNet v1.1 | Image Classification | ImageNet | MXNet | 5 MB | .model |
VGG16 | Image Classification | ImageNet | MXNet | 490 MB | .model |
VGG19 | Image Classification | ImageNet | MXNet | 509 MB | .model |
VGG19 | Image Classification | ImageNet | ONNX | 548 MB | .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
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Type: Image classification trained on ImageNet
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Reference: Krizhevsky, et al.
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Model Service: mxnet_vision_service.py
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Start Server:
mxnet-model-server --models alexnet=https://s3.amazonaws.com/model-server/models/onnx-alexnet/alexnet.model
- Run Prediction:
curl -X POST http://127.0.0.1:8080/alexnet/predict -F "[email protected]"
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Type: Image classification trained on ImageNet
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Reference: Krizhevsky, et al.
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Model Service: mxnet_vision_service.py
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Start Server:
mxnet-model-server --models caffenet=https://s3.amazonaws.com/model-server/models/caffenet/caffenet.model
- Run Prediction:
curl -X POST http://127.0.0.1:8080/caffenet/predict -F "[email protected]"
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Type: Image classification trained on ImageNet
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Reference: Szegedy, et al., Google
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Model Service: mxnet_vision_service.py
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Start Server:
mxnet-model-server --models inception-v1=https://s3.amazonaws.com/model-server/models/onnx-inception_v1/inception_v1.model
- Run Prediction:
curl -X POST http://127.0.0.1:8080/inception-v1/predict -F "[email protected]"
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Type: Image classification trained on ImageNet
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Reference: Szegedy, et al., Google
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Model Service: mxnet_vision_service.py
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Start Server:
mxnet-model-server --models inception-bn=https://s3.amazonaws.com/model-server/models/inception-bn/Inception-BN.model
- Run Prediction:
curl -X POST http://127.0.0.1:8080/inception-bn/predict -F "[email protected]"
Long short-term memory network trained on the PennTreeBank dataset.
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Reference: Hochreiter, et al.
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Model Service: lstm_ptb_service.py
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Start Server:
mxnet-model-server --models lstm_ptb=https://s3.amazonaws.com/model-server/models/lstm_ptb/lstm_ptb.model
- Run Prediction:
curl -X POST http://127.0.0.1:8080/lstm_ptb/predict -F "data=[{'input_sentence': 'on the exchange floor as soon as ual stopped trading we <unk> for a panic said one top floor trader'}]"
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Type: Image classification trained on ImageNet
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Reference: Lin, et al.
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Model Service: mxnet_vision_service.py
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Start Server:
mxnet-model-server --models nin=https://s3.amazonaws.com/model-server/models/nin/nin.model
- Run Prediction:
curl -X POST http://127.0.0.1:8080/nin/predict -F "[email protected]"
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Type: Image classification trained on ImageNet
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Reference: Lin, et al.
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Model Service: mxnet_vision_service.py
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Start Server:
mxnet-model-server --models resnet-152=https://s3.amazonaws.com/model-server/models/resnet-152/resnet-152.model
- Run Prediction:
curl -X POST http://127.0.0.1:8080/resnet-152/predict -F "[email protected]"
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Type: Image classification trained on ImageNet
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Reference: He, et al.
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Model Service: mxnet_vision_service.py
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Start Server:
mxnet-model-server --models resnet-18=https://s3.amazonaws.com/model-server/models/resnet-18/resnet-18.model
- Run Prediction:
curl -X POST http://127.0.0.1:8080/resnet-18/predict -F "[email protected]"
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Type: Image classification trained on ImageNet
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Reference: Liu, et al.
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Model Service: ssd_service.py
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Start Server:
mxnet-model-server --models SSD=https://s3.amazonaws.com/model-server/models/resnet50_ssd/resnet50_ssd_model.model
- Run Prediction:
wget https://www.dphotographer.co.uk/users/21963/thm1024/1337890426_Img_8133.jpg
curl -X POST http://127.0.0.1:8080/SSD/predict -F "data=@1337890426_Img_8133.jpg"
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Type: Image classification trained on ImageNet
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Reference: Xie, et al.
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Model Service: mxnet_vision_service.py
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Start Server:
mxnet-model-server --models resnext101=https://s3.amazonaws.com/model-server/models/resnext-101-64x4d/resnext-101-64x4d.model
- Run Prediction:
curl -X POST http://127.0.0.1:8080/resnext101/predict -F "[email protected]"
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Type: Image classification trained on ImageNet
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Reference: Iandola, et al.
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Model Service: mxnet_vision_service.py
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Start Server:
mxnet-model-server --models squeezenet=https://s3.amazonaws.com/model-server/models/onnx-squeezenet/squeezenet.model
- Run Prediction:
curl -X POST http://127.0.0.1:8080/squeezenet/predict -F "[email protected]"
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Type: Image classification trained on ImageNet
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Reference: Iandola, et al.
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Model Service: mxnet_vision_service.py
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Start Server:
mxnet-model-server --models squeezenet_v1.1=https://s3.amazonaws.com/model-server/models/squeezenet_v1.1/squeezenet_v1.1.model
- Run Prediction:
curl -X POST http://127.0.0.1:8080/squeezenet_v1.1/predict -F "[email protected]"
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Type: Image classification trained on ImageNet
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Reference: Simonyan, et al.
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Model Service: mxnet_vision_service.py
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Start Server:
mxnet-model-server --models vgg16=https://s3.amazonaws.com/model-server/models/vgg16/vgg16.model
- Run Prediction:
curl -X POST http://127.0.0.1:8080/vgg16/predict -F "[email protected]"
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Type: Image classification trained on ImageNet
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Reference: Simonyan, et al.
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Model Service: mxnet_vision_service.py
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Start Server:
mxnet-model-server --models vgg19=https://s3.amazonaws.com/model-server/models/vgg19/vgg19.model
- Run Prediction:
curl -X POST http://127.0.0.1:8080/vgg19/predict -F "[email protected]"
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Type: Image classification trained on ImageNet (imported from ONNX)
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Reference: Simonyan, et al.
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Model Service: mxnet_vision_service.py
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Start Server:
mxnet-model-server --models vgg19=https://s3.amazonaws.com/model-server/models/onnx-vgg19/vgg19.model
- Run Prediction:
curl -X POST http://127.0.0.1:8080/vgg19/predict -F "[email protected]"