Franca: Nested Matryoshka Clustering for Scalable Visual Representation
  Learning
We present Franca (pronounced Fran-ka): free one; the first fully open-source
(data, code, weights) vision foundation model that matches and in many cases
surpasses the performance of state-of-the-art proprietary models, e.g., DINOv2,
CLIP, SigLIPv2, etc. Our approach is grounded in a transparent training
pipeline inspired by Web-SSL and uses publicly available data: ImageNet-21K and
a subset of ReLAION-2B. Beyond model release, we tackle critical limitations in
SSL clustering methods. While modern models rely on assigning image features to
large codebooks via clustering algorithms like Sinkhorn-Knopp, they fail to
account for the inherent ambiguity in clustering semantics. To address this, we
introduce a parameter-efficient, multi-head clustering projector based on
nested Matryoshka representations. This design progressively refines features
into increasingly fine-grained clusters without increasing the model size,
enabling both performance and memory efficiency. Additionally, we propose a
novel positional disentanglement strategy that explicitly removes positional
biases from dense representations, thereby improving the encoding of semantic
content. This leads to consistent gains on several downstream benchmarks,
demonstrating the utility of cleaner feature spaces. Our contributions
establish a new standard for transparent, high-performance vision models and
open a path toward more reproducible and generalizable foundation models for
the broader AI community. The code and model checkpoints are available at
https://github.com/valeoai/Franca.
687de5df2e8db0930be6f14b
Shashanka Venkataramanan
admin_assigned
2025-07-21T15:57:23.321Z
651ec83782dbe82d3f500eaa
/avatars/4143b9102ff589e0d1b2325aea75766e.svg
Shashanka Venkataramanan 
user
shawshankv
687de5df2e8db0930be6f14c
Valentinos Pariza
admin_assigned
2025-07-21T15:57:29.437Z
63f79b1bfd6949669625f54e
/avatars/04f7a3a9e71a656ea64aed1bb01db5b1.svg
Valentinos Pariza
user
valentinospariza
687de5df2e8db0930be6f14d
Mohammadreza Salehi
687de5df2e8db0930be6f14e
Lukas Knobel
admin_assigned
2025-07-21T15:57:39.747Z
62f4cafb31ee3f3670f597a4
/avatars/9a214de17a8e6ab8ceb2b2ee8727368b.svg
Lukas Knobel
user
Lukas431
687de5df2e8db0930be6f14f
Spyros Gidaris
admin_assigned
2025-07-21T15:57:46.036Z
678cd947d13c19c917644792
https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/010zyg-JoIjaqVn9lSDRT.png
Spyros Gidaris
user
sgidaris
687de5df2e8db0930be6f150
Elias Ramzi
admin_assigned
2025-07-21T15:57:52.435Z
651ebd4e45d808ba81f33613
/avatars/56cadecaeabd0d0fde7018866bc07d32.svg
Elias Ramzi
user
Elias-Ramzi
687de5df2e8db0930be6f151
Andrei Bursuc
687de5df2e8db0930be6f152
Yuki M. Asano
Franca: Nested Matryoshka Clustering for Scalable Visual Representation
  Learning
We present Franca (pronounced Fran-ka): free one; the first fully open-source
(data, code, weights) vision foundation model that matches and in many cases
surpasses the performance of state-of-the-art proprietary models, e.g., DINOv2,
CLIP, SigLIPv2, etc. Our approach is grounded in a transparent training
pipeline inspired by Web-SSL and uses publicly available data: ImageNet-21K and
a subset of ReLAION-2B. Beyond model release, we tackle critical limitations in
SSL clustering methods. While modern models rely on assigning image features to
large codebooks via clustering algorithms like Sinkhorn-Knopp, they fail to
account for the inherent ambiguity in clustering semantics. To address this, we
introduce a parameter-efficient, multi-head clustering projector based on
nested Matryoshka representations. This design progressively refines features
into increasingly fine-grained clusters without increasing the model size,
enabling both performance and memory efficiency. Additionally, we propose a
novel positional disentanglement strategy that explicitly removes positional
biases from dense representations, thereby improving the encoding of semantic
content. This leads to consistent gains on several downstream benchmarks,
demonstrating the utility of cleaner feature spaces. Our contributions
establish a new standard for transparent, high-performance vision models and
open a path toward more reproducible and generalizable foundation models for
the broader AI community. The code and model checkpoints are available at
https://github.com/valeoai/Franca.
Franca, an open-source vision foundation model, achieves high performance using a transparent training pipeline and novel clustering and disentanglement techniques.
Web-SSL ImageNet-21K ReLAION-2B Sinkhorn-Knopp parameter-efficient multi-head clustering projector nested Matryoshka representations positional disentanglement strategy semantic content downstream benchmarks