Training in progress, epoch 0

config.json

@@ -2,6 +2,10 @@
   "architectures": [
     "MoEModelForImageClassification"
   ],
+  "auto_map": {
+    "AutoConfig": "configuration_moe.MoEConfig",
+    "AutoModelForImageClassification": "modeling_moe.MoEModelForImageClassification"
+  },
   "base_model": "louislu9911/BaseModel-leaf-disease-convnextv2-base-1k-224-0_1_2_3_4",
   "expert_class_mapping": {
     "0": [

configuration_moe.py

@@ -0,0 +1,38 @@
+from transformers import PretrainedConfig
+from typing import Dict, List
+
+
+MOE_MODEL_TYPE = "moe"
+DEFAULT_HUGGINGFACE_ACCOUNT = "louislu9911"
+model_checkpoint = "convnextv2-base-1k-224"
+
+EXPERTS = [
+    f"{DEFAULT_HUGGINGFACE_ACCOUNT}/Expert1-leaf-disease-{model_checkpoint}-0_4",
+    f"{DEFAULT_HUGGINGFACE_ACCOUNT}/Expert2-leaf-disease-{model_checkpoint}-1_2_3",
+]
+SWITCH_GATE = (
+    f"{DEFAULT_HUGGINGFACE_ACCOUNT}/switch_gate-leaf-disease-{model_checkpoint}"
+)
+BASE_MODEL = (
+    f"{DEFAULT_HUGGINGFACE_ACCOUNT}/BaseModel-leaf-disease-{model_checkpoint}-0_1_2_3_4"
+)
+
+
+class MoEConfig(PretrainedConfig):
+    model_type = MOE_MODEL_TYPE
+
+    def __init__(
+        self,
+        experts: List[str] = EXPERTS,
+        switch_gate: str = SWITCH_GATE,
+        base_model: str = BASE_MODEL,
+        num_classes: int = 5,
+        expert_class_mapping: Dict[int, List[int]] = None,
+        **kwargs,
+    ):
+        self.experts = experts
+        self.switch_gate = switch_gate
+        self.base_model = base_model
+        self.num_classes = num_classes
+        self.expert_class_mapping = expert_class_mapping
+        super().__init__(**kwargs)

model.safetensors

@@ -1,3 +1,3 @@
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+oid sha256:996c902806c49871bb4063f579dd93b84bc57405078adf888f6b448ca71e02cc
 size 2372917736

training_args.bin

@@ -1,3 +1,3 @@
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+oid sha256:f627c5b913dbe30b5014b3d88870ee151056a3d09224cf196a93ea68d19f5210
 size 4984