Add/Update backbone checkpoints (count=6)

ds_proc.py

@@ -128,6 +128,7 @@ class BackboneMLPHead224ImageProcessor(ImageProcessingMixin):
         self._delegate = AutoImageProcessor.from_pretrained(
             self.backbone_name_or_path,
             use_fast=self.use_fast,
+            # trust_remote_code = True,
         )
 
     @staticmethod
@@ -257,17 +258,22 @@ class BackboneMLPHead224ImageProcessor(ImageProcessingMixin):
         - Read config.json via AutoConfig and recover backbone_name_or_path.
           AutoConfig로 config.json을 읽고 backbone_name_or_path를 복구합니다.
         """
-        cfg = AutoConfig.from_pretrained(pretrained_model_name_or_path, **kwargs)
-        backbone = getattr(cfg, "backbone_name_or_path", None)
-        if backbone is None:
-            raise ValueError("Cannot build processor: backbone_name_or_path not found in config.json")
-
+        
         # is_training is runtime-only and should default to False for inference/serving.
         # is_training은 런타임 전용이며 추론/서빙 기본값은 False가 맞습니다.
         #
         # IMPORTANT:
         # - use_fast는 kwargs로 전달될 수 있으므로, 있으면 반영합니다.
         use_fast = bool(kwargs.pop("use_fast", False))
+        
+        kwargs.pop("trust_remote_code", None)
+        cfg = AutoConfig.from_pretrained(
+            pretrained_model_name_or_path, 
+            trust_remote_code =True,
+            **kwargs)
+        backbone = getattr(cfg, "backbone_name_or_path", None)
+        if backbone is None:
+            raise ValueError("Cannot build processor: backbone_name_or_path not found in config.json")
 
         return cls(backbone_name_or_path=backbone, is_training=False, use_fast=use_fast)
 

manifest_20260210_142559.json

@@ -0,0 +1,55 @@
+{
+  "timestamp": "20260210_142559",
+  "repo_id": "dsaint31/bb_mlp_224",
+  "revision": "main",
+  "tag": null,
+  "num_labels": 3,
+  "build_device": "mps",
+  "count": 6,
+  "items": [
+    {
+      "backbone": "google/vit-base-patch16-224",
+      "subdir": "models/google__vit-base-patch16-224",
+      "dirname": "google__vit-base-patch16-224"
+    },
+    {
+      "backbone": "microsoft/swin-tiny-patch4-window7-224",
+      "subdir": "models/microsoft__swin-tiny-patch4-window7-224",
+      "dirname": "microsoft__swin-tiny-patch4-window7-224"
+    },
+    {
+      "backbone": "microsoft/resnet-50",
+      "subdir": "models/microsoft__resnet-50",
+      "dirname": "microsoft__resnet-50"
+    },
+    {
+      "backbone": "google/efficientnet-b0",
+      "subdir": "models/google__efficientnet-b0",
+      "dirname": "google__efficientnet-b0"
+    },
+    {
+      "backbone": "timm/densenet121.tv_in1k",
+      "subdir": "models/timm__densenet121.tv_in1k",
+      "dirname": "timm__densenet121.tv_in1k"
+    },
+    {
+      "backbone": "torchvision/densenet121",
+      "subdir": "models/torchvision__densenet121",
+      "dirname": "torchvision__densenet121"
+    }
+  ],
+  "root_code_included": true,
+  "root_code_files": [
+    "ds_proc.py",
+    "ds_model.py",
+    "ds_cfg.py",
+    "ds_meta.py"
+  ],
+  "subfolder_code_included": true,
+  "subfolder_code_files": [
+    "ds_proc.py",
+    "ds_model.py",
+    "ds_cfg.py",
+    "ds_meta.py"
+  ]
+}

models/google__efficientnet-b0/config.json

@@ -24,7 +24,7 @@
   "num_labels": 3,
   "transformers_version": "5.1.0",
   "ds_provenance": {
-    "created_at": "20260210_132916",
+    "created_at": "20260210_142559",
     "repo_id": "dsaint31/bb_mlp_224",
     "subdir": "models/google__efficientnet-b0",
     "wrapper_class": "BackboneWithMLPHeadForImageClassification",

models/google__efficientnet-b0/ds_proc.py

@@ -128,6 +128,7 @@ class BackboneMLPHead224ImageProcessor(ImageProcessingMixin):
         self._delegate = AutoImageProcessor.from_pretrained(
             self.backbone_name_or_path,
             use_fast=self.use_fast,
+            # trust_remote_code = True,
         )
 
     @staticmethod
@@ -257,17 +258,22 @@ class BackboneMLPHead224ImageProcessor(ImageProcessingMixin):
         - Read config.json via AutoConfig and recover backbone_name_or_path.
           AutoConfig로 config.json을 읽고 backbone_name_or_path를 복구합니다.
         """
-        cfg = AutoConfig.from_pretrained(pretrained_model_name_or_path, **kwargs)
-        backbone = getattr(cfg, "backbone_name_or_path", None)
-        if backbone is None:
-            raise ValueError("Cannot build processor: backbone_name_or_path not found in config.json")
-
+        
         # is_training is runtime-only and should default to False for inference/serving.
         # is_training은 런타임 전용이며 추론/서빙 기본값은 False가 맞습니다.
         #
         # IMPORTANT:
         # - use_fast는 kwargs로 전달될 수 있으므로, 있으면 반영합니다.
         use_fast = bool(kwargs.pop("use_fast", False))
+        
+        kwargs.pop("trust_remote_code", None)
+        cfg = AutoConfig.from_pretrained(
+            pretrained_model_name_or_path, 
+            trust_remote_code =True,
+            **kwargs)
+        backbone = getattr(cfg, "backbone_name_or_path", None)
+        if backbone is None:
+            raise ValueError("Cannot build processor: backbone_name_or_path not found in config.json")
 
         return cls(backbone_name_or_path=backbone, is_training=False, use_fast=use_fast)
 

models/google__efficientnet-b0/model.safetensors

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:23745db9141f378b25b88834d6f6bda100120733dc1caea4eb6217b02363e933
+oid sha256:7020f515e549776c727f92374975faf8bb9878444809463d1d1069e08f68d735
 size 17558436

models/google__vit-base-patch16-224/config.json

@@ -24,7 +24,7 @@
   "num_labels": 3,
   "transformers_version": "5.1.0",
   "ds_provenance": {
-    "created_at": "20260210_132916",
+    "created_at": "20260210_142559",
     "repo_id": "dsaint31/bb_mlp_224",
     "subdir": "models/google__vit-base-patch16-224",
     "wrapper_class": "BackboneWithMLPHeadForImageClassification",

models/google__vit-base-patch16-224/ds_proc.py

@@ -128,6 +128,7 @@ class BackboneMLPHead224ImageProcessor(ImageProcessingMixin):
         self._delegate = AutoImageProcessor.from_pretrained(
             self.backbone_name_or_path,
             use_fast=self.use_fast,
+            # trust_remote_code = True,
         )
 
     @staticmethod
@@ -257,17 +258,22 @@ class BackboneMLPHead224ImageProcessor(ImageProcessingMixin):
         - Read config.json via AutoConfig and recover backbone_name_or_path.
           AutoConfig로 config.json을 읽고 backbone_name_or_path를 복구합니다.
         """
-        cfg = AutoConfig.from_pretrained(pretrained_model_name_or_path, **kwargs)
-        backbone = getattr(cfg, "backbone_name_or_path", None)
-        if backbone is None:
-            raise ValueError("Cannot build processor: backbone_name_or_path not found in config.json")
-
+        
         # is_training is runtime-only and should default to False for inference/serving.
         # is_training은 런타임 전용이며 추론/서빙 기본값은 False가 맞습니다.
         #
         # IMPORTANT:
         # - use_fast는 kwargs로 전달될 수 있으므로, 있으면 반영합니다.
         use_fast = bool(kwargs.pop("use_fast", False))
+        
+        kwargs.pop("trust_remote_code", None)
+        cfg = AutoConfig.from_pretrained(
+            pretrained_model_name_or_path, 
+            trust_remote_code =True,
+            **kwargs)
+        backbone = getattr(cfg, "backbone_name_or_path", None)
+        if backbone is None:
+            raise ValueError("Cannot build processor: backbone_name_or_path not found in config.json")
 
         return cls(backbone_name_or_path=backbone, is_training=False, use_fast=use_fast)
 

models/google__vit-base-patch16-224/model.safetensors

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:20da01895e6d64d099a4638d59ef3792fcb7657d610faf45ecf2a8c540c36332
+oid sha256:c051b46d9d05d5ee0182d84ec6a01a6b2f03e4a3197e1989dbb2119b1c1554a9
 size 346372132

models/microsoft__resnet-50/config.json

@@ -24,7 +24,7 @@
   "num_labels": 3,
   "transformers_version": "5.1.0",
   "ds_provenance": {
-    "created_at": "20260210_132916",
+    "created_at": "20260210_142559",
     "repo_id": "dsaint31/bb_mlp_224",
     "subdir": "models/microsoft__resnet-50",
     "wrapper_class": "BackboneWithMLPHeadForImageClassification",

models/microsoft__resnet-50/ds_proc.py

@@ -128,6 +128,7 @@ class BackboneMLPHead224ImageProcessor(ImageProcessingMixin):
         self._delegate = AutoImageProcessor.from_pretrained(
             self.backbone_name_or_path,
             use_fast=self.use_fast,
+            # trust_remote_code = True,
         )
 
     @staticmethod
@@ -257,17 +258,22 @@ class BackboneMLPHead224ImageProcessor(ImageProcessingMixin):
         - Read config.json via AutoConfig and recover backbone_name_or_path.
           AutoConfig로 config.json을 읽고 backbone_name_or_path를 복구합니다.
         """
-        cfg = AutoConfig.from_pretrained(pretrained_model_name_or_path, **kwargs)
-        backbone = getattr(cfg, "backbone_name_or_path", None)
-        if backbone is None:
-            raise ValueError("Cannot build processor: backbone_name_or_path not found in config.json")
-
+        
         # is_training is runtime-only and should default to False for inference/serving.
         # is_training은 런타임 전용이며 추론/서빙 기본값은 False가 맞습니다.
         #
         # IMPORTANT:
         # - use_fast는 kwargs로 전달될 수 있으므로, 있으면 반영합니다.
         use_fast = bool(kwargs.pop("use_fast", False))
+        
+        kwargs.pop("trust_remote_code", None)
+        cfg = AutoConfig.from_pretrained(
+            pretrained_model_name_or_path, 
+            trust_remote_code =True,
+            **kwargs)
+        backbone = getattr(cfg, "backbone_name_or_path", None)
+        if backbone is None:
+            raise ValueError("Cannot build processor: backbone_name_or_path not found in config.json")
 
         return cls(backbone_name_or_path=backbone, is_training=False, use_fast=use_fast)
 

models/microsoft__resnet-50/model.safetensors

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:e8e23998ad1e6c1608ab302fcf862881e9fdd01fdc19acfde659375bfdbc910f
+oid sha256:672f7ce423ab8366b6988ab17a5946d3a9191438e7b2831ee0ac03e9ee9b28f0
 size 96388660

models/microsoft__swin-tiny-patch4-window7-224/config.json

@@ -24,7 +24,7 @@
   "num_labels": 3,
   "transformers_version": "5.1.0",
   "ds_provenance": {
-    "created_at": "20260210_132916",
+    "created_at": "20260210_142559",
     "repo_id": "dsaint31/bb_mlp_224",
     "subdir": "models/microsoft__swin-tiny-patch4-window7-224",
     "wrapper_class": "BackboneWithMLPHeadForImageClassification",

models/microsoft__swin-tiny-patch4-window7-224/ds_proc.py

@@ -128,6 +128,7 @@ class BackboneMLPHead224ImageProcessor(ImageProcessingMixin):
         self._delegate = AutoImageProcessor.from_pretrained(
             self.backbone_name_or_path,
             use_fast=self.use_fast,
+            # trust_remote_code = True,
         )
 
     @staticmethod
@@ -257,17 +258,22 @@ class BackboneMLPHead224ImageProcessor(ImageProcessingMixin):
         - Read config.json via AutoConfig and recover backbone_name_or_path.
           AutoConfig로 config.json을 읽고 backbone_name_or_path를 복구합니다.
         """
-        cfg = AutoConfig.from_pretrained(pretrained_model_name_or_path, **kwargs)
-        backbone = getattr(cfg, "backbone_name_or_path", None)
-        if backbone is None:
-            raise ValueError("Cannot build processor: backbone_name_or_path not found in config.json")
-
+        
         # is_training is runtime-only and should default to False for inference/serving.
         # is_training은 런타임 전용이며 추론/서빙 기본값은 False가 맞습니다.
         #
         # IMPORTANT:
         # - use_fast는 kwargs로 전달될 수 있으므로, 있으면 반영합니다.
         use_fast = bool(kwargs.pop("use_fast", False))
+        
+        kwargs.pop("trust_remote_code", None)
+        cfg = AutoConfig.from_pretrained(
+            pretrained_model_name_or_path, 
+            trust_remote_code =True,
+            **kwargs)
+        backbone = getattr(cfg, "backbone_name_or_path", None)
+        if backbone is None:
+            raise ValueError("Cannot build processor: backbone_name_or_path not found in config.json")
 
         return cls(backbone_name_or_path=backbone, is_training=False, use_fast=use_fast)
 

models/microsoft__swin-tiny-patch4-window7-224/model.safetensors

@@ -1,3 +1,3 @@
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-oid sha256:bafc09e3b9c502c7a6795ddca6447866a7e8a7f93ffe67eb2a4804a50f840479
+oid sha256:e6a66b3ed3993cd9e93967288f9019ef2c355abdeacb5db056bd9cc5192b2624
 size 111128348

models/timm__densenet121.tv_in1k/config.json

@@ -24,7 +24,7 @@
   "num_labels": 3,
   "transformers_version": "5.1.0",
   "ds_provenance": {
-    "created_at": "20260210_132916",
+    "created_at": "20260210_142559",
     "repo_id": "dsaint31/bb_mlp_224",
     "subdir": "models/timm__densenet121.tv_in1k",
     "wrapper_class": "BackboneWithMLPHeadForImageClassification",

models/timm__densenet121.tv_in1k/ds_proc.py

@@ -128,6 +128,7 @@ class BackboneMLPHead224ImageProcessor(ImageProcessingMixin):
         self._delegate = AutoImageProcessor.from_pretrained(
             self.backbone_name_or_path,
             use_fast=self.use_fast,
+            # trust_remote_code = True,
         )
 
     @staticmethod
@@ -257,17 +258,22 @@ class BackboneMLPHead224ImageProcessor(ImageProcessingMixin):
         - Read config.json via AutoConfig and recover backbone_name_or_path.
           AutoConfig로 config.json을 읽고 backbone_name_or_path를 복구합니다.
         """
-        cfg = AutoConfig.from_pretrained(pretrained_model_name_or_path, **kwargs)
-        backbone = getattr(cfg, "backbone_name_or_path", None)
-        if backbone is None:
-            raise ValueError("Cannot build processor: backbone_name_or_path not found in config.json")
-
+        
         # is_training is runtime-only and should default to False for inference/serving.
         # is_training은 런타임 전용이며 추론/서빙 기본값은 False가 맞습니다.
         #
         # IMPORTANT:
         # - use_fast는 kwargs로 전달될 수 있으므로, 있으면 반영합니다.
         use_fast = bool(kwargs.pop("use_fast", False))
+        
+        kwargs.pop("trust_remote_code", None)
+        cfg = AutoConfig.from_pretrained(
+            pretrained_model_name_or_path, 
+            trust_remote_code =True,
+            **kwargs)
+        backbone = getattr(cfg, "backbone_name_or_path", None)
+        if backbone is None:
+            raise ValueError("Cannot build processor: backbone_name_or_path not found in config.json")
 
         return cls(backbone_name_or_path=backbone, is_training=False, use_fast=use_fast)
 

models/timm__densenet121.tv_in1k/model.safetensors

@@ -1,3 +1,3 @@
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-oid sha256:7a67cf2b0e6c745b32e43a31a2dbf4c32a2046f9bcb55c78f01a32f4d498fce6
+oid sha256:eda54a9ead50e30b80b8b0e60e9024149fd0cdeada25ea7023aa27333235090f
 size 29293620

models/torchvision__densenet121/config.json

@@ -24,7 +24,7 @@
   "num_labels": 3,
   "transformers_version": "5.1.0",
   "ds_provenance": {
-    "created_at": "20260210_132916",
+    "created_at": "20260210_142559",
     "repo_id": "dsaint31/bb_mlp_224",
     "subdir": "models/torchvision__densenet121",
     "wrapper_class": "BackboneWithMLPHeadForImageClassification",

models/torchvision__densenet121/ds_proc.py

@@ -128,6 +128,7 @@ class BackboneMLPHead224ImageProcessor(ImageProcessingMixin):
         self._delegate = AutoImageProcessor.from_pretrained(
             self.backbone_name_or_path,
             use_fast=self.use_fast,
+            # trust_remote_code = True,
         )
 
     @staticmethod
@@ -257,17 +258,22 @@ class BackboneMLPHead224ImageProcessor(ImageProcessingMixin):
         - Read config.json via AutoConfig and recover backbone_name_or_path.
           AutoConfig로 config.json을 읽고 backbone_name_or_path를 복구합니다.
         """
-        cfg = AutoConfig.from_pretrained(pretrained_model_name_or_path, **kwargs)
-        backbone = getattr(cfg, "backbone_name_or_path", None)
-        if backbone is None:
-            raise ValueError("Cannot build processor: backbone_name_or_path not found in config.json")
-
+        
         # is_training is runtime-only and should default to False for inference/serving.
         # is_training은 런타임 전용이며 추론/서빙 기본값은 False가 맞습니다.
         #
         # IMPORTANT:
         # - use_fast는 kwargs로 전달될 수 있으므로, 있으면 반영합니다.
         use_fast = bool(kwargs.pop("use_fast", False))
+        
+        kwargs.pop("trust_remote_code", None)
+        cfg = AutoConfig.from_pretrained(
+            pretrained_model_name_or_path, 
+            trust_remote_code =True,
+            **kwargs)
+        backbone = getattr(cfg, "backbone_name_or_path", None)
+        if backbone is None:
+            raise ValueError("Cannot build processor: backbone_name_or_path not found in config.json")
 
         return cls(backbone_name_or_path=backbone, is_training=False, use_fast=use_fast)
 

models/torchvision__densenet121/model.safetensors

@@ -1,3 +1,3 @@
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 size 33394052