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ViTune

Image Classification
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PyTorch
vit
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ViTune
File size: 1,115 Bytes
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{
  "_name_or_path": "google/vit-base-patch16-224-in21k",
  "architectures": [
    "ViTForImageClassification"
  ],
  "attention_probs_dropout_prob": 0.0,
  "encoder_stride": 16,
  "hidden_act": "gelu",
  "hidden_dropout_prob": 0.0,
  "hidden_size": 768,
  "id2label": {
    "0": "aluminium_foil",
    "1": "brown_bread",
    "10": "wool",
    "2": "corduroy",
    "3": "cork",
    "4": "cotton",
    "5": "cracker",
    "6": "lettuce_leaf",
    "7": "linen",
    "8": "white_bread",
    "9": "wood"
  },
  "image_size": 224,
  "initializer_range": 0.02,
  "intermediate_size": 3072,
  "label2id": {
    "aluminium_foil": "0",
    "brown_bread": "1",
    "corduroy": "2",
    "cork": "3",
    "cotton": "4",
    "cracker": "5",
    "lettuce_leaf": "6",
    "linen": "7",
    "white_bread": "8",
    "wood": "9",
    "wool": "10"
  },
  "layer_norm_eps": 1e-12,
  "model_type": "vit",
  "num_attention_heads": 12,
  "num_channels": 3,
  "num_hidden_layers": 12,
  "patch_size": 16,
  "problem_type": "single_label_classification",
  "qkv_bias": true,
  "torch_dtype": "float32",
  "transformers_version": "4.20.1"
}