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AIArtDetector.pth-af59f7fa.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:40eb1aef6b5d269dfaaa500534552add0dbb847c24f18fae6f6c2a32ae859b32
+size 134

README.md

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----
-license: unknown
----
+---
+license: other
+language:
+- en
+metrics:
+- accuracy
+library_name: timm
+pipeline_tag: image-classification
+---

config.json

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+{
+  "model_type": "timm_backbone",
+  "architecture": "eva02_base_patch14_448",
+  "num_classes": 3,
+  "num_features": 768,
+  "global_pool": "avg",
+  "pretrained_cfg": {
+    "tag": "mim_in22k_ft_in22k_in1k",
+    "custom_load": false,
+    "input_size": [
+      3,
+      448,
+      448
+    ],
+    "fixed_input_size": true,
+    "interpolation": "bicubic",
+    "crop_pct": 1.0,
+    "crop_mode": "squash",
+    "mean": [
+      0.48145466,
+      0.4578275,
+      0.40821073
+    ],
+    "std": [
+      0.26862954,
+      0.26130258,
+      0.27577711
+    ],
+    "num_classes": 3,
+    "pool_size": null,
+    "first_conv": "patch_embed.proj",
+    "classifier": "head",
+    "license": "mit"
+  }
+}

gitattributes

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+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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handler.py

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+from typing import  Dict, List, Any
+import timm
+import torch
+from timm.utils import ParseKwargs
+from timm.data.constants import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, DEFAULT_CROP_PCT
+
+class EndpointHandler():
+    def __init__(self, path=""):
+        self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+        # self.aiGeneratorModel = timm.create_model('eva02_base_patch14_448.mim_in22k_ft_in22k_in1k', num_classes=9, in_chans=3, checkpoint_path=path + '/AIModelDetector.pth-6ff3631e.pth')
+        aiArtModel = timm.create_model('eva02_base_patch14_448.mim_in22k_ft_in22k_in1k', num_classes=3, in_chans=3, checkpoint_path=path + '/AIArtDetector.pth-af59f7fa.pth')
+        # aiGeneratorModel = aiGeneratorModel.to(self.device)
+        aiArtModel = aiArtModel.to(self.device)
+        # aiGeneratorModel.eval()
+        aiArtModel.eval()
+
+        self.transform = timm.data.create_transform(input_size=(3, 448, 448),
+        is_training=False,
+        use_prefetcher=False,
+        no_aug=False,
+        scale=None,
+        ratio=None,
+        hflip=0,
+        vflip=0.,
+        color_jitter=0,
+        auto_augment=None,
+        interpolation='bicubic',
+        # mean=(0.5, 0.5, 0.5),
+        # std=(0.5, 0.5, 0.5),
+        re_prob=0.,
+        re_mode='const',
+        re_count=1,
+        re_num_splits=0,
+        crop_pct=1.0,
+        # crop_mode='center',
+        crop_mode='squash',
+        tf_preprocessing=False,
+        separate=False)
+
+    def __call__(self, data):
+        """
+       data args:
+            inputs: Dict[str, Any]
+      Return:
+            A :obj:`list` | `dict`: will be serialized and returned
+        """
+        # get inputs
+        image = data.pop("inputs", data)
+        
+        image_tensor1 = self.transform(image).to(self.device)
+        with torch.no_grad():
+            output1 = self.aiArtModel(image_tensor1.unsqueeze(0))
+
+        return output1
+        
+
+    

pytorch_model.bin

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+version https://git-lfs.github.com/spec/v1
+oid sha256:af59f7fa807b4fa8c6fb7177c700b0a0aebb81f16bd0ba96828c7c95d8843a90
+size 345475737

requirements.txt

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+torch
+timm