ruclip-vit-large-patch14-224

RuCLIP (Russian Contrastive Language–Image Pretraining) is a multimodal model for obtaining images and text similarities and rearranging captions and pictures. RuCLIP builds on a large body of work on zero-shot transfer, computer vision, natural language processing and multimodal learning.

Model was trained by Sber AI and SberDevices teams.

• Task: text ranking; image ranking; zero-shot image classification;
• Type: encoder
• Num Parameters: 430M
• Training Data Volume: 240 million text-image pairs
• Language: Russian
• Context Length: 77
• Transformer Layers: 12
• Transformer Width: 768
• Transformer Heads: 12
• Image Size: 224
• Vision Layers: 24
• Vision Width: 1024
• Vision Patch Size: 14

Usage Github

pip install ruclip

clip, processor = ruclip.load("ruclip-vit-large-patch14-224", device="cuda")

Performance

We have evaluated the performance on the following datasets:

Dataset	Metric Name	Metric Result
Food101	acc	0.597
CIFAR10	acc	0.878
CIFAR100	acc	0.511
Birdsnap	acc	0.172
SUN397	acc	0.484
Stanford Cars	acc	0.559
DTD	acc	0.370
MNIST	acc	0.337
STL10	acc	0.934
PCam	acc	0.520
CLEVR	acc	0.152
Rendered SST2	acc	0.529
ImageNet	acc	0.426
FGVC Aircraft	mean-per-class	0.046
Oxford Pets	mean-per-class	0.604
Caltech101	mean-per-class	0.777
Flowers102	mean-per-class	0.455
HatefulMemes	roc-auc	0.530

Authors

• Alex Shonenkov: Github, Kaggle GM
• Daniil Chesakov: Github
• Denis Dimitrov: Github
• Igor Pavlov: Github