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PRIMER

Feature Extraction
PyTorch
primer
medical
radiology
mammography
contrastive-learning
embeddings
computer-vision
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PRIMER
File size: 2,506 Bytes 
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{
  "model_type": "primer",
  "architecture": "resnet50",
  "task": "feature-extraction",
  "framework": "pytorch",

  "model_config": {
    "architecture": "resnet50",
    "pretrained_radimagenet": true,
    "embedding_dim": 2048,
    "projection_dim": 128,
    "dropout": 0.2,
    "freeze_backbone": false,
    "use_projection_head": true
  },

  "input_config": {
    "image_size": 224,
    "num_channels": 3,
    "input_shape": [3, 224, 224],
    "data_format": "channels_first",
    "color_mode": "rgb"
  },

  "preprocessing_config": {
    "use_clahe": true,
    "clahe_clip_limit": 2.0,
    "clahe_tile_grid_size": [8, 8],
    "normalize_hu": true,
    "percentile_clip": [2, 98],
    "normalization": {
      "mean": [0.485, 0.456, 0.406],
      "std": [0.229, 0.224, 0.225],
      "description": "ImageNet normalization (required for RadImageNet compatibility)"
    }
  },

  "training_config": {
    "method": "contrastive",
    "framework": "simclr",
    "batch_size": 128,
    "num_epochs": 50,
    "learning_rate": 0.0001,
    "weight_decay": 0.00001,
    "warmup_epochs": 10,
    "patience": 15,
    "gradient_clip": 1.0,
    "optimizer": {
      "name": "adamw",
      "betas": [0.9, 0.999]
    },
    "scheduler": {
      "name": "cosine",
      "min_lr": 0.000001
    }
  },

  "contrastive_learning": {
    "loss": "nt_xent",
    "temperature": 0.07,
    "use_cosine_similarity": true,
    "negative_samples": "all",
    "description": "NT-Xent (Normalized Temperature-scaled Cross Entropy) loss from SimCLR"
  },

  "augmentation_config": {
    "horizontal_flip": 0.5,
    "vertical_flip": 0.3,
    "rotate_limit": 15,
    "brightness_limit": 0.2,
    "contrast_limit": 0.2,
    "shift_scale_rotate": true,
    "elastic_transform": false,
    "grid_distortion": false
  },

  "data_config": {
    "dataset": "CMMD",
    "train_split": 0.7,
    "val_split": 0.15,
    "test_split": 0.15,
    "random_seed": 42,
    "num_training_samples": 13000,
    "modality": "mammography",
    "format": "dicom"
  },

  "output_config": {
    "embedding_dim": 2048,
    "normalize_embeddings": true,
    "normalization_type": "l2"
  },

  "hardware_config": {
    "mixed_precision": true,
    "gpu_memory_required": "12GB",
    "recommended_batch_size": 128
  },

  "metrics": {
    "silhouette_score": 0.289,
    "davies_bouldin_score": 1.653,
    "calinski_harabasz_score": 3621,
    "embedding_variance": 0.024
  },

  "version": "1.0.0",
  "pytorch_version": "2.1.0",
  "timm_version": "0.9.12"
}