CodonProject/DINOv3-ViT-Base

DINOv3 ViT-B/16 — converted weights (codon layout, float16)

This repository hosts the weights of `facebook/dinov3-vitb16-pretrain-lvd1689m` converted for the codon implementation of DINOv3:

• —renamed to the codon naming convention (codon.impl.DINOv3ViT),
• —cast to float16 (2× smaller than the original fp32 checkpoint),
• —numerically verified against the original weights and against the transformers reference implementation.

The model itself is unchanged: same architecture, same parameter values, same numerics up to fp16 quantization.

Why this exists

The upstream checkpoints use the transformers/Meta naming layout (layer.{i}.attention.*, layer_scale{1,2}.lambda1, embeddings.*) and ship in fp32. codon.impl.DINOv3ViT uses its own convention (all linear layers are *_proj, the block list is blocks, Layer Scale is gamma{1,2}, register tokens are storage_tokens), so the weights need a one-time remap. This repository is that remap, pre-applied and stored in fp16 so it can be loaded directly with a single call.

Files

Key mapping

Two notes on the conversion:

• —The inv_freq rotary table is not stored. In this implementation the 2D axial RoPE frequencies are recomputed from rope_theta and the input resolution at runtime (a non-persistent buffer), which is what lets the same weights run at any image size.
• —A zero-initialized mask_token is included as a placeholder. It is part of this implementation's MAE architecture but is absent from the original checkpoint; load_pretrained(strict=True) does not require it.

Model overview

Usage

import torch
from codon.impl import DINOv3ViT_Base

# Pulls config.json + model.safetensors from this repository.
# config.json says dtype=float16, so the model is built in float16.
model = DINOv3ViT_Base.from_remote().eval()

# Prefer fp32 compute? The weights are upcast on load.
model32 = DINOv3ViT_Base.from_remote(dtype=torch.float32).eval()

from_remote() reads the architecture from config.json — num_register_tokens, rope_theta, pos_embed_rescale, layer_norm_eps, key_bias and dtype are all applied automatically, so no manual configuration is needed.

To load the file yourself:

model = DINOv3ViT_Base().half()
model.load_pretrained('model.safetensors', strict=True, dtype=torch.float16)

Feature extraction

x = torch.randn(1, 3, 224, 224).half()

with torch.no_grad():
    feats = model.forward_features(x)

feats['x_norm_clstoken']        # [1, 768]        CLS token, post-norm
feats['x_storage_tokens']       # [1, 4, 768]     4 register tokens, post-norm
feats['x_norm_patchtokens']     # [1, 196, 768]   14x14 patch tokens, post-norm
feats['x_norm_alltokens']       # [1, 201, 768]   full post-norm sequence
feats['x_prenorm']              # [1, 201, 768]   full pre-norm sequence

forward(x) (the default, is_training=False) returns just the CLS token of shape [N, 768].

Dense features at arbitrary resolutions work without interpolation, because the RoPE frequencies are derived from the actual patch grid:

model.forward_features(torch.randn(1, 3, 256, 192).half())['x_norm_patchtokens'].shape
# torch.Size([1, 192, 768])   -> 16x12 patch grid

Intermediate layers, optionally reshaped to feature maps:

with torch.no_grad():
    layers = model.get_intermediate_layers(x, n=[0, 5, 11])          # tuple of [N, HW, C]
    maps = model.get_intermediate_layers(x, n=1, reshape=True)       # [N, C, 14, 14]

Verification

The conversion was checked at every step, and these checks are reproducible via test/test_dinov3_vit.py in the codon repository:

The fp16 error is well within the expected range for a 12-layer model: activations reach an order of magnitude of ~10-30, and fp16 carries roughly three significant decimal digits.

Precision notes

• —Inference in float16 works on CPU and CUDA. The rotary cos/sin tables are computed in fp32 and then cast to the activation dtype, so attention inputs stay in float16 throughout instead of being silently promoted to fp32.
• —If you need maximum fidelity, use dtype=torch.float32; the weights are exact float16 representations of the original fp32 values, so this only recovers the rounding that happened at export time.

License

The weights are derived from Meta's DINOv3 and remain subject to the DINOv3 License. Please read and comply with that license before use. In particular, the license governs acceptable use, redistribution and attribution; this conversion adds no additional permissions and no warranty.