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README.md

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+# Real vs Fake Image Classifier (ONNX)
+
+This model classifies images as either REAL or FAKE. It is fine-tuned on the [raw_real_fake_images](https://huggingface.co/datasets/date3k2/raw_real_fake_images) dataset.
+
+- Architecture: ViT (Vision Transformer)
+- Format: ONNX
+- Usage: For browser-based inference using [transformers.js](https://github.com/xenova/transformers.js)

config.json

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+{
+  "_attn_implementation_autoset": true,
+  "architectures": [
+    "ViTForImageClassification"
+  ],
+  "attention_probs_dropout_prob": 0.0,
+  "encoder_stride": 16,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.0,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "Fake",
+    "1": "Real"
+  },
+  "image_size": 224,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "Fake": "0",
+    "Real": "1"
+  },
+  "layer_norm_eps": 1e-12,
+  "model_type": "vit",
+  "num_attention_heads": 12,
+  "num_channels": 3,
+  "num_hidden_layers": 12,
+  "patch_size": 16,
+  "pooler_act": "tanh",
+  "pooler_output_size": 768,
+  "problem_type": "single_label_classification",
+  "qkv_bias": true,
+  "torch_dtype": "float32",
+  "transformers_version": "4.51.3"
+}

metadata.json

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+{
+  "task": "image-classification",
+  "format": "onnx",
+  "labels": {
+    "0": "REAL",
+    "1": "FAKE"
+  },
+  "quantized": false
+}

onnx/model.onnx

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+version https://git-lfs.github.com/spec/v1
+oid sha256:75c0b4b2157170c78e4d7cfae5fc1630f1c71ebc245918fc03fd909e2794b0e8
+size 343477784

preprocessor_config.json

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+{
+  "do_convert_rgb": null,
+  "do_normalize": true,
+  "do_rescale": true,
+  "do_resize": true,
+  "image_mean": [
+    0.5,
+    0.5,
+    0.5
+  ],
+  "image_processor_type": "ViTImageProcessor",
+  "image_std": [
+    0.5,
+    0.5,
+    0.5
+  ],
+  "resample": 2,
+  "rescale_factor": 0.00392156862745098,
+  "size": {
+    "height": 224,
+    "width": 224
+  }
+}