Hugging Face Blog
Fine-Tune ViT for Image Classification with 🤗 Transformers
Read the original on Hugging Face Blog →The Flow has not summarised this story yet — read it at Hugging Face Blog.
Related stories
arXiv Machine Learning
Jul 8
A Constrained Optimization Perspective of Unrolled Transformers
arXiv:2601. 17257v2 Announce Type: replace Abstract: We introduce a constrained optimization framework for training transformers that behave like optimization descent algorithms.
arXiv Machine Learning
Jun 25
REViT: Roto-reflection Equivariant Convolutional Vision Transformer
arXiv:2606. 25318v1 Announce Type: cross Abstract: In this paper, we propose a discrete roto-reflection group equivariant vision transformer with convolutional attention.
Hugging Face Trending Papers
Jun 24
REViT: Roto-reflection Equivariant Convolutional Vision Transformer
In this paper, we propose a discrete roto-reflection group equivariant vision transformer with convolutional attention. Roto-reflection equivariant networks preserve the rotational, flip and positional symmetry in feature maps, making them useful for tasks where orientation of the inputs is relevant to the model outputs.
arXiv Machine Learning
Jun 4
Vision Transformer Finetuning Benefits from Non-Smooth Components
arXiv:2602. 06883v3 Announce Type: replace Abstract: The smoothness of the transformer architecture has been extensively studied in the context of generalization, training stability, and adversarial robustness.
arXiv Machine Learning
Jun 18
Transformer Geometry Observatory TGO-I: Spectral Geometry Observatory
arXiv:2606. 19249v1 Announce Type: cross Abstract: Despite the widespread adoption of Vision Transformers (ViTs) and their success across numerous computer vision applications, the fundamental understanding of their dimensional and representational geometry remains relatively underexplored.
arXiv AI
Jul 7
Towards Generalizable Deepfake Image Detection with Vision Transformers
arXiv:2604. 17376v2 Announce Type: replace-cross Abstract: In today's day and age, we face a challenge in detecting deepfake images because of the fast evolution of modern generative models and the poor generalization capability of existing methods.