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tiny-random-EfficientNetForImageClassification

Image Classification
Transformers
PyTorch
efficientnet
Inference Endpoints
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3
tiny-random-EfficientNetForImageClassification
File size: 1,286 Bytes 
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{
  "architectures": [
    "EfficientNetForImageClassification"
  ],
  "batch_norm_eps": 0.001,
  "batch_norm_momentum": 0.99,
  "depth_coefficient": 3.1,
  "depth_divisor": 8,
  "depthwise_padding": [],
  "drop_connect_rate": 0.2,
  "dropout_rate": 0.5,
  "expand_ratios": [
    1,
    6,
    6
  ],
  "hidden_act": "gelu",
  "hidden_dim": 2560,
  "id2label": {
    "0": "LABEL_0",
    "1": "LABEL_1",
    "2": "LABEL_2",
    "3": "LABEL_3",
    "4": "LABEL_4",
    "5": "LABEL_5",
    "6": "LABEL_6",
    "7": "LABEL_7",
    "8": "LABEL_8",
    "9": "LABEL_9"
  },
  "image_size": 600,
  "in_channels": [
    32,
    16,
    24
  ],
  "initializer_range": 0.02,
  "kernel_sizes": [
    3,
    3,
    5
  ],
  "label2id": {
    "LABEL_0": 0,
    "LABEL_1": 1,
    "LABEL_2": 2,
    "LABEL_3": 3,
    "LABEL_4": 4,
    "LABEL_5": 5,
    "LABEL_6": 6,
    "LABEL_7": 7,
    "LABEL_8": 8,
    "LABEL_9": 9
  },
  "model_type": "efficientnet",
  "num_block_repeats": [
    1,
    1,
    2
  ],
  "num_channels": 3,
  "num_hidden_layers": 16,
  "out_channels": [
    16,
    24,
    40
  ],
  "pooling_type": "mean",
  "squeeze_expansion_ratio": 0.25,
  "strides": [
    1,
    1,
    2
  ],
  "torch_dtype": "float32",
  "transformers_version": "4.28.0.dev0",
  "width_coefficient": 2.0
}