Models · Feature backbones

DINOv3 ConvNeXt

Meta's DINOv3 distilled into ConvNeXt backbones (T, S, B, L): image features. Needs the gated DINOv3 licence.

Commercial use: yes.

variantinputbackendtime (ms)peak memory (MiB, allocated / reserved)
dinov3-convnext-tinysynthetic (224x224)original (fp32)
original
portable
kernels
portable + torch.compile
TensorRT fp16
2.96
4.47
3.04
1.18
1.96
0.681
151 / 162
198 / 206
93 / 108
86 / 112
123 / 148
72 (engine)
dinov3-convnext-smallsynthetic (224x224)original (fp32)
original
portable
kernels
5.97
8.09
5.42
1.99
233 / 274
328 / 348
134 / 150
127 / 152
dinov3-convnext-basesynthetic (224x224)original (fp32)
original
portable
kernels
7.38
8.24
5.71
2.18
384 / 394
535 / 552
208 / 232
200 / 214
dinov3-convnext-largesynthetic (224x224)original (fp32)
original
portable
kernels
10.5
9.59
5.45
3.59
819 / 898
1163 / 1252
419 / 430
441 / 494
Details
  • Original code: https://github.com/huggingface/transformers at v5.19.0
  • Code licence: LicenseRef-DINOv3-License (permissive); files: LICENSE.md
  • Weights licence: LicenseRef-DINOv3-License
  • Licence evidence: https://huggingface.co/facebook/dinov3-convnext-tiny-pretrain-lvd1689m/blob/10d30274b4d445111e2d5bf75ac93bbd94db274b/LICENSE.md (DINOv3 License; gated: accept on Hugging Face to download)
  • Training data: distilled from the DINOv3 ViT-7B teacher on LVD-1689M (1.689B images curated from public Instagram images), self-supervised, per the paper; not released
  • Licence note: Code and weights use the DINOv3 License (Meta: attribution and conditions apply); read it before commercial use.
  • Kernel libraries: fvm_conv, fvm_seg
  • Variants: dinov3-convnext-tiny, dinov3-convnext-small, dinov3-convnext-base, dinov3-convnext-large
  • Outputs: last_hidden_state, pooler_output
  • Input size: any H x W >= 1, resized or padded inside the model
  • Tested up to (portable, fp16, RTX 5060 Ti 16 GB): dinov3-convnext-tiny 3000x4000 (1.9 GiB peak allocated, 2.6 reserved); dinov3-convnext-small 3000x4000 (1.9 GiB peak allocated, 2.6 reserved); dinov3-convnext-base 3000x4000 (2.5 GiB peak allocated, 3.5 reserved); dinov3-convnext-large 3000x4000 (3.7 GiB peak allocated, 5.1 reserved)
  • dinov3-convnext-tiny 224x224, first call: portable + torch.compile 8 s (cold Inductor cache), TensorRT fp16 23 s (ONNX + engine).
  • dinov3-convnext-tiny load / first call: original 0.43 s / 1.034 s, portable 0.48 s / 0.417 s, kernels 0.08 s / 0.124 s
  • dinov3-convnext-small load / first call: original 0.27 s / 0.01 s, portable 0.25 s / 0.334 s, kernels 0.11 s / 0.074 s
  • dinov3-convnext-base load / first call: original 0.42 s / 0.015 s, portable 0.43 s / 0.27 s, kernels 0.19 s / 0.104 s
  • dinov3-convnext-large load / first call: original 1.64 s / 0.017 s, portable 0.85 s / 0.241 s, kernels 0.38 s / 0.2 s
  • Protocol, first call, 3 warm-up, peak reset, median of 3 rounds x median of 5

Measured

Two disjoint seeded subsets of the val folders; each image shorter side 256 (bicubic), centre crop 224, the CLS / pooled feature; cosine kNN, vote weighted by similarity. Original = the original code named under Details, same checkpoint and protocol.

variantdatasetoriginal fp32original fp16portablekernelsnote
dinov3-convnext-tinyImageNet-1k val, kNN top-1 % (k = 20, 10000 labelled / 5000 test images)62.5062.5062.5062.44
dinov3-convnext-smallImageNet-1k val, kNN top-1 % (k = 20, 10000 labelled / 5000 test images)67.1467.1667.1467.16
dinov3-convnext-baseImageNet-1k val, kNN top-1 % (k = 20, 10000 labelled / 5000 test images)69.8469.8869.8469.88
dinov3-convnext-largeImageNet-1k val, kNN top-1 % (k = 20, 10000 labelled / 5000 test images)73.3073.3273.3073.28

Measured on NVIDIA GeForce RTX 5060 Ti, torch 2.14.1+cu130, fastforks 939adfe8, 2026-11-06.

Parity

CPU, float32: max |diff| <= 0.0001 vs the original. GPU, float16 (the default), portable and kernels: mean |diff| <= 1 % and p99 <= 4 % of the reference's median |value| (absolute error of the output last_hidden_state).

variantkernels error vs original, mean / p99GPU, torchkernels used
dinov3-convnext-tiny0.00491 / 0.01731RTX 5060 Ti, torch 2.14.1+cu130fvm_conv@f1d064e539e1:sm_120, fvm_seg@739693dff3b4:sm_120
dinov3-convnext-small0.00412 / 0.01531RTX 5060 Ti, torch 2.14.1+cu130fvm_conv@f1d064e539e1:sm_120, fvm_seg@739693dff3b4:sm_120
dinov3-convnext-base0.0054 / 0.0206RTX 5060 Ti, torch 2.14.1+cu130fvm_conv@f1d064e539e1:sm_120, fvm_seg@739693dff3b4:sm_120
dinov3-convnext-large0.0057 / 0.02671RTX 5060 Ti, torch 2.14.1+cu130fvm_conv@f1d064e539e1:sm_120, fvm_seg@739693dff3b4:sm_120