{
  "dataset_files": [
    "shape-3-224-224-3-32-2-0",
    "shape-3-192-192-3-32-2-0",
    "shape-3-160-160-3-32-2-0",
    "shape-3-128-128-3-32-2-0",
    "shape-3-224-224-3-24-2-0",
    "shape-3-192-192-3-24-2-0",
    "shape-3-160-160-3-24-2-0",
    "shape-3-128-128-3-24-2-0",
    "shape-3-224-224-3-16-2-0",
    "shape-3-192-192-3-16-2-0",
    "shape-3-160-160-3-16-2-0",
    "shape-3-128-128-3-16-2-0",
    "shape-3-224-224-3-8-2-0",
    "shape-3-192-192-3-8-2-0",
    "shape-3-160-160-3-8-2-0",
    "shape-3-128-128-3-8-2-0"
  ],
  "desc_dataset_files": {
    "shape-3-128-128-3-16-2-0": {
      "name": "MobileNet_0.50_128 Conv2d_0 input 128x128x3 filter 3x3x16 stride 2 pad 0"
    },
    "shape-3-128-128-3-24-2-0": {
      "name": "MobileNet_0.75_128 Conv2d_0 input 128x128x3 filter 3x3x24 stride 2 pad 0"
    },
    "shape-3-128-128-3-32-2-0": {
      "name": "MobileNet_1.0_128 Conv2d_0 input 128x128x3 filter 3x3x32 stride 2 pad 0"
    },
    "shape-3-128-128-3-8-2-0": {
      "name": "MobileNet_0.25_128 Conv2d_0 input 128x128x3 filter 3x3x8 stride 2 pad 0"
    },
    "shape-3-160-160-3-16-2-0": {
      "name": "MobileNet_0.50_160 Conv2d_0 input 160x160x3 filter 3x3x16 stride 2 pad 0"
    },
    "shape-3-160-160-3-24-2-0": {
      "name": "MobileNet_0.75_160 Conv2d_0 input 160x160x3 filter 3x3x24 stride 2 pad 0"
    },
    "shape-3-160-160-3-32-2-0": {
      "name": "MobileNet_1.0_160 Conv2d_0 input 160x160x3 filter 3x3x32 stride 2 pad 0"
    },
    "shape-3-160-160-3-8-2-0": {
      "name": "MobileNet_0.25_160 Conv2d_0 input 160x160x3 filter 3x3x8 stride 2 pad 0"
    },
    "shape-3-192-192-3-16-2-0": {
      "name": "MobileNet_0.50_192 Conv2d_0 input 192x192x3 filter 3x3x16 stride 2 pad 0"
    },
    "shape-3-192-192-3-24-2-0": {
      "name": "MobileNet_0.75_192 Conv2d_0 input 192x192x3 filter 3x3x24 stride 2 pad 0"
    },
    "shape-3-192-192-3-32-2-0": {
      "name": "MobileNet_1.0_192 Conv2d_0 input 192x192x3 filter 3x3x32 stride 2 pad 0"
    },
    "shape-3-192-192-3-8-2-0": {
      "name": "MobileNet_0.25_192 Conv2d_0 input 192x192x3 filter 3x3x8 stride 2 pad 0"
    },
    "shape-3-224-224-3-16-2-0": {
      "name": "MobileNet_0.50_224 Conv2d_0 input 224x224x3 filter 3x3x16 stride 2 pad 0"
    },
    "shape-3-224-224-3-24-2-0": {
      "name": "MobileNet_0.75_224 Conv2d_0 input 224x224x3 filter 3x3x24 stride 2 pad 0"
    },
    "shape-3-224-224-3-32-2-0": {
      "name": "MobileNet_1.0_224 Conv2d_0 input 224x224x3 filter 3x3x32 stride 2 pad 0"
    },
    "shape-3-224-224-3-8-2-0": {
      "name": "MobileNet_0.25_224 Conv2d_0 input 224x224x3 filter 3x3x8 stride 2 pad 0"
    }
  },
  "tags": [
    "dataset",
    "nntest",
    "tensor-conv",
    "tensor-conv-mobilenets",
    "conv",
    "convolution",
    "mobilenets"
  ]
}