DINOv2: State-of-the-art computer vision models with self ... - AI at Meta
Apr 17, 2023 · Meta AI has built DINOv2, a new method for training high-performance computer vision models. DINOv2 delivers strong performance and does not require fine-tuning. This makes it suitable for use as a backbone for many different computer vision tasks.
DINO and PAWS: Advancing the state of the art in computer vision
Apr 30, 2021 · Working with Inria researchers, we’ve developed a self-supervised image representation method, DINO, which produces remarkable results when trained with Vision Transformers. We are also detailing PAWS, a new method for 10x more efficient training.
DINOv2 by Meta AI
DINOv2 models are pretrained without supervision on a large, curated and diverse dataset of 142 million images. DINOv2 models demonstrate strong out-of-distribution performance and the produced features are usable without requiring any fine-tuning. State-of-the-art results and strong generalization on estimating depth from a single image.
Self-Supervised Vision Transformers with DINO - GitHub
Run DINO with ViT-small network on a single node with 8 GPUs for 100 epochs with the following command. Training time is 1.75 day and the resulting checkpoint should reach 69.3% on k-NN eval and 74.0% on linear eval.
Explore Meta AI’s self-supervised learning demo for images - AI at Meta
Apr 8, 2022 · Today, we are releasing the first-ever external demo based on Meta AI's self-supervised learning work. We focus on Vision Transformers pretrained with DINO, a method we released last year that has grown in popularity based on its capacity to understand the semantic layout of an image.
GitHub - facebookresearch/dinov2: PyTorch code and models for …
Oct 26, 2023 · DINOv2 models produce high-performance visual features that can be directly employed with classifiers as simple as linear layers on a variety of computer vision tasks; these visual features are robust and perform well across domains without any …
DINOv2 by Meta AI
This DINOv2 demo (the "Demo") allows users (18+) to upload or pre-select an image and display an estimated depth map, a segmentation map or retrieve and view images similar to the provided one. To use this tool, you can either upload your own image, take a photo, insert a URL, or choose from a selection of images provided by the Demo.
DINOv2: Self-supervised Learning Model Explained - Encord
Discover how Meta AI's DINOv2 works without labeled data, outperforming other models in tasks like segmentation, depth estimation, and instance retrieval. Meta AI's latest breakthrough, DINOv2, is transforming how we approach computer vision tasks.
SSL demos by Meta AI
See how DINO understands the different semantic parts of an image. Select an object or an area from a picture, and discover images similar to what you selected. DINO can identify similar concepts in 2 images. Select a part of an image A, and see the closest parts on another image B.
DINOv2: Self-Supervised Computer Vision Models by Meta AI
Meta AI has just released open-source DINOv2 models the first method that uses self-supervised learning to train computer vision models. The DINOv2 models achieve results that match or are even better than the standard approach and models in the field.