Franca: Nested Matryoshka Clustering for Scalable Visual Representation
  Learning
Authors: name: Shashanka Venkataramanan, name: Valentinos Pariza, name: Mohammadreza Salehi, name: Lukas Knobel, name: Spyros Gidaris, name: Elias Ramzi, name: Andrei Bursuc, name: Yuki M. Asano

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.