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Royboker/
docuguard-vit-checkpoints

Name: docuguard-vit-checkpoints
Availability: InStock

pytorch

image-classification

document-forgery-detection

vision-transformer

vit

apache-2.0

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DocuGuard — ViT checkpoints

Trained model weights for the DocuGuard identity-document forgery detection pipeline (Roy Boker, B.Sc. final project, 2026). Powers the live demo at royboker.github.io via the Royboker/docuguard-demo HF Space.

Files

File	Architecture	Task	Classes	Training samples
`vit_document_classifier_9k.pth`	`vit_tiny_patch16_224`	Document type	ID Card / Passport / Driver License	9,000
`vit_passport_binary_20k.pth`	`vit_small_patch16_224`	Forgery (binary)	Real / Fake	20,000
`vit_passport_fraud_type_20k.pth`	`vit_small_patch16_224`	Fraud type	face_morphing / face_replacement	20,000
`vit_id_card_binary_20k.pth`	`vit_small_patch16_224`	Forgery (binary)	Real / Fake	20,000
`vit_id_card_fraud_type_20k.pth`	`vit_small_patch16_224`	Fraud type	face_morphing / face_replacement	20,000
`vit_drivers_license_binary_15k.pth`	`vit_small_patch16_224`	Forgery (binary)	Real / Fake	15,000
`vit_drivers_license_fraud_type_15k.pth`	`vit_small_patch16_224`	Fraud type	face_morphing / face_replacement	15,000

Architecture notes

Stage 1 (vit_document_classifier_9k.pth): ViTTinyClassifier — timm backbone + Sequential(Dropout(0.2), Linear → 3).
Stage 2 & 3 (all _binary_ / _fraud_type_ files): ViTBinaryClassifier / ViTFraudTypeClassifier — timm backbone + Sequential(LayerNorm, Linear → d/2, GELU, Dropout(0.1), Linear → 2).
Image size: 224 × 224, ImageNet normalization, RGB.
Inference applies 4-view Test-Time Augmentation on stages 2 & 3 (base / scale-down / scale-up / brightness) — see docuguard-demo/model_loader.py.

Dataset

Trained on the IDNet identity-document analysis dataset (~290k images, 9 countries).

Source

Code: github.com/royboker/-final-project

Royboker/docuguard-vit-checkpoints