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README.md

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+---
+license: mit
+library_name: onnx
+tags:
+  - image-classification
+  - ai-image-detection
+  - deepfake-detection
+  - onnx
+  - webgpu
+base_model: OwensLab/commfor-model-384
+---
+
+# Detectra detector v1
+
+On-device AI-image detector powering the [Detectra Chrome extension](https://github.com/ashhart/Detectra):
+a ViT-S/16 @384 binary classifier (`sigmoid(logit)` = probability the image is
+AI-generated), exported to single-file fp16 ONNX for ONNX Runtime Web
+(WebGPU/WASM).
+
+Fine-tuned from the MIT-licensed
+[Community Forensics ViT-S](https://huggingface.co/OwensLab/commfor-model-384)
+(Park & Owens, CVPR 2025) on modern generators (DALL·E 3, Midjourney, Flux,
+SD3.5, Recraft, HiDream), in-the-wild social-media data (WildRF train), and a
+replay slice of CommunityForensics-Small, with web-realism augmentation
+(JPEG cascades, resize chains). GPT-4o and Ideogram were held out entirely.
+
+Preprocessing: shortest edge → 440 (bilinear) → center-crop 384 → [0,1] →
+ImageNet normalize. Input `pixel_values` 1×3×384×384 fp32, output `logit` 1×1.
+
+Held-out results through the extension pipeline @0.65 threshold:
+WildRF-test 97.6% balanced accuracy (mangled 97.1%), modern-generator eval
+98.3%. Full training + evaluation code: https://github.com/ashhart/Detectra
+
+sha256(model.onnx) = 94fac18030826da368304e0cd79ff831df3ab6e46f95463a84b19793a9d2692d
+
+## Files
+- model.onnx — fp16 ONNX export (deployed by the extension)
+- model.safetensors — fp32 fine-tuned checkpoint (for further training/audit)

model.onnx

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+size 43684258

model.safetensors

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