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exampleModels.json
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exampleModels.json
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{
"exampleModels": [
{
"name": "32x32x1 Convolutional (Sequential)",
"fileName": "32_32_1_conv_sequential.h5",
"url": "https://userweb.wpi.edu/~rgbenasutti/models/32_32_1_conv_sequential.h5",
"description": "A simple example model.",
"freezeLayers": {"conv2d_8": {"value":true},"max_pooling2d_5": {"value": true},"dropout_10": {"value": true},"flatten_5": {"value": true},"dense_10": {"value": true},"dropout_11": {"value": true},"dense_11": {"value": true}}
},
{
"name": "Classification - MNIST",
"fileName": "32_32_1_conv_sequential.h5",
"url": "https://userweb.wpi.edu/~rgbenasutti/models/32_32_1_conv_sequential.h5",
"description": "A simple example model.",
"freezeLayers": {"conv2d_8": {"value":true},"max_pooling2d_5": {"value": true},"dropout_10": {"value": true},"flatten_5": {"value": true},"dense_10": {"value": true},"dropout_11": {"value": true},"dense_11": {"value": true}}
},
{
"name": "Classification - Fashion MNIST",
"fileName": "32_32_1_conv_sequential.h5",
"url": "https://userweb.wpi.edu/~rgbenasutti/models/32_32_1_conv_sequential.h5",
"description": "A simple example model.",
"freezeLayers": {"conv2d_8": {"value":true},"max_pooling2d_5": {"value": true},"dropout_10": {"value": true},"flatten_5": {"value": true},"dense_10": {"value": true},"dropout_11": {"value": true},"dense_11": {"value": true}}
},
{
"name": "MobileNet V2",
"fileName": "mobilenetv2_1.00_224.h5",
"url": "https://userweb.wpi.edu/~rgbenasutti/models/mobilenetv2_1.00_224.h5",
"description": "A convolutional neural network trained on ImageNet. Acceptable accuracy and moderate compute requirements for mobile robotics.",
"freezeLayers": 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},
{
"name": "Regression - Auto MPG",
"fileName": "simple-auto-mpg-model.h5",
"url": "https://userweb.wpi.edu/~rgbenasutti/models/simple-auto-mpg-model.h5",
"description": "A simple regression model for the Auto MPG dataset.",
"freezeLayers": {"dense_9": {"value": false},"dense_10": {"value": false},"dense_11": {"value": false}}
}
]
}