DINOHash

Canonical model zoo for DINOHash — robust perceptual image hashing (ONNX). All inputs are (N, 3, 224, 224), ImageNet-normalized (mean=[0.485,0.456,0.406], std=[0.229,0.224,0.225]).

Recommended — hash bits baked in

The ONNX graph includes the PCA head; the output is per-bit logits (binarize with >= 0 → 1).

Model	Bits	File
DINOv2 ViT-B/14 (flagship)	512	dinov2_vitb14_reg_512bit_dynamic.onnx
DINOv2 ViT-S/14	96	dinov2_vits14_reg_96bit_dynamic.onnx

Free hosted API: https://huggingface.co/spaces/proteus-photos/DINOHash

Other backbones — raw embeddings (apply your own PCA / sign-binarization)

• DINOv3: dinov3-vits16, dinov3-vits16plus, dinov3_convnext-tiny, dinov3_convnext-small
• Distilled students (DisCo): ResNet-{50,50*2,101,152} → {ResNet-18, ResNet-34, MobileNet-v3, EfficientNet-B0, EfficientNet-B1}, plus ViT-Small→ViT-Tiny, XCiT-Small→XCiT-Tiny
• SSL baselines: DINO / MoCo / SwAV (MobileNetV2, R18, R34, ViT-T) and MAE-Lite (mae_tiny_*, mocov3_tiny)

raw/

Raw training / TorchScript checkpoints for the ViT-Tiny, XCiT-Tiny and MAE-Lite models.