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efficientnet-b5-Brain_Tumors_Image_Classification

Image Classification

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efficientnet-b5-Brain_Tumors_Image_Classification / config.json

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	{
	"_name_or_path": "google/efficientnet-b5",
	"architectures": [
	"EfficientNetForImageClassification"
	],
	"batch_norm_eps": 0.001,
	"batch_norm_momentum": 0.99,
	"depth_coefficient": 2.2,
	"depth_divisor": 8,
	"depthwise_padding": [
	13,
	27
	],
	"drop_connect_rate": 0.2,
	"dropout_rate": 0.4,
	"expand_ratios": [
	1,
	6,
	6,
	6,
	6,
	6,
	6
	],
	"hidden_act": "swish",
	"hidden_dim": 2048,
	"id2label": {
	"0": "glioma_tumor",
	"1": "meningioma_tumor",
	"2": "no_tumor",
	"3": "pituitary_tumor"
	},
	"image_size": 456,
	"in_channels": [
	32,
	16,
	24,
	40,
	80,
	112,
	192
	],
	"initializer_range": 0.02,
	"kernel_sizes": [
	3,
	3,
	5,
	3,
	5,
	5,
	3
	],
	"label2id": {
	"glioma_tumor": "0",
	"meningioma_tumor": "1",
	"no_tumor": "2",
	"pituitary_tumor": "3"
	},
	"model_type": "efficientnet",
	"num_block_repeats": [
	1,
	2,
	2,
	3,
	3,
	4,
	1
	],
	"num_channels": 3,
	"num_hidden_layers": 64,
	"out_channels": [
	16,
	24,
	40,
	80,
	112,
	192,
	320
	],
	"pooling_type": "mean",
	"problem_type": "single_label_classification",
	"squeeze_expansion_ratio": 0.25,
	"strides": [
	1,
	2,
	2,
	2,
	1,
	2,
	1
	],
	"torch_dtype": "float32",
	"transformers_version": "4.28.1",
	"width_coefficient": 1.6
	}