Navid MoazzezNavid Moazzez

Dinov3

new: [2026-06-12]:fire: Metadata-guided training code and reference recipes (FMoW satellite imagery, HPA-WholeHR fluorescence imagery) are now released on the FINO branch, accompanying Who Needs Labels? Adapting Vision Foundation Models With the Metadata You Already Have (Gardès et al., 2026). [2026-03-10] The Canopy Height Maps v2 (CHMv2) model and inference code are now available (more details on downloading the model weights and using the code here). The model weights are also available in Hugging Face Hub and supported by the Hugging Face Transformers library. Building on our original high-resolution canopy height maps released in 2024, CHMv2 delivers substantial improvements in accuracy, detail, and global consistency by leveraging DINOv3. [2025-11-20] Distillation code and configurations for ConvNeXt backbones are now released!

View Dinov3 on GitHub
Navid Moazzezby Navid Moazzez·Updated Sept 30, 2026·1 min read
Dinov3

new: [2026-06-12]:fire: Metadata-guided training code and reference recipes (FMoW satellite imagery, HPA-WholeHR fluorescence imagery) are now released on the FINO branch, accompanying Who Needs Labels? Adapting Vision Foundation Models With the Metadata You Already Have (Gardès et al., 2026).

[2026-03-10] The Canopy Height Maps v2 (CHMv2) model and inference code are now available (more details on downloading the model weights and using the code here). The model weights are also available in Hugging Face Hub and supported by the Hugging Face Transformers library. Building on our original high-resolution canopy height maps released in 2024, CHMv2 delivers substantial improvements in accuracy, detail, and global consistency by leveraging DINOv3.

[2025-11-20] Distillation code and configurations for ConvNeXt backbones are now released!

Dinov3 at a glance

Stars11k
Forks966
LanguageJupyter Notebook
LicenseOther
Last update2026-07-15
Contributors14

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