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config.json

@@ -1,12 +1,10 @@
 {
-  "_name_or_path": "./efficientnet/temp",
   "architectures": [
-    "EfficientNetModelForImageClassification"
+    "EfficientNetModel"
   ],
   "auto_map": {
     "AutoConfig": "configuration_efficientnet.EfficientNetConfig",
-    "AutoModel": "modeling_efficientnet.EfficientNetModel",
-    "AutoModelForImageClassification": "modeling_efficientnet.EfficientNetModelForImageClassification"
+    "AutoModel": "modeling_efficientnet.EfficientNetModel"
   },
   "model_name": "efficientnet_b0",
   "model_type": "efficientnet",

configuration_efficientnet.py

@@ -34,68 +34,6 @@ class EfficientNetConfig(PretrainedConfig):
         
         self.model_name = model_name
         self.pretrained = pretrained
-
-        # add attributes
-        # "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": { IMAGE NET DATASET
-        # "0": "LABEL_0",
-        # "1": "LABEL_1",
-        # "2": "LABEL_2",
-        # ...
-        # },
-        # "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,
-        # ...
-        # },
-        # "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
-        # ],
-        # "width_coefficient": 2.0
-        # }
        
         super().__init__(**kwargs)