{
  "description": "ResNet-50 (Residual Network with 50 layers)",
  "architecture_details": "ResNet-50 is a convolutional neural network that uses residual connections to enable training of very deep networks. It consists of 50 layers with skip connections.",
  "model_id": "trustformers/resnet50",
  
  "num_classes": 1000,
  "image_height": 224,
  "image_width": 224,
  "activation": "ReLU",
  "dropout": 0.0,
  "use_batch_norm": true,
  
  "has_stem": true,
  "has_residual": true,
  "has_depthwise": false,
  "has_se_module": false,
  "has_neck": false,
  "supports_detection": false,
  "supports_segmentation": false,
  
  "layers": [
    {
      "out_channels": 64,
      "kernel_size": 7,
      "stride": 2,
      "padding": 3,
      "num_blocks": 3
    },
    {
      "out_channels": 128,
      "kernel_size": 3,
      "stride": 2,
      "padding": 1,
      "num_blocks": 4
    },
    {
      "out_channels": 256,
      "kernel_size": 3,
      "stride": 2,
      "padding": 1,
      "num_blocks": 6
    },
    {
      "out_channels": 512,
      "kernel_size": 3,
      "stride": 2,
      "padding": 1,
      "num_blocks": 3
    }
  ],
  
  "custom_params": [
    {
      "name": "zero_init_residual",
      "type": "bool",
      "description": "Zero-initialize the last BN in each residual branch",
      "default": "false"
    },
    {
      "name": "groups",
      "type": "usize",
      "description": "Number of groups for grouped convolution",
      "default": "1"
    },
    {
      "name": "width_per_group",
      "type": "usize",
      "description": "Width of each group",
      "default": "64"
    }
  ],
  
  "references": "- Original paper: [Deep Residual Learning for Image Recognition](https://arxiv.org/abs/1512.03385)"
}