ruclip-vit-base-patch32-384

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: 150M
• Training Data Volume: 240 million text-image pairs
• Language: Russian
• Context Length: 77
• Transformer Layers: 12
• Transformer Width: 512
• Transformer Heads: 8
• Image Size: 384
• Vision Layers: 12
• Vision Width: 768
• Vision Patch Size: 32

Usage Github

pip install ruclip

clip, processor = ruclip.load("ruclip-vit-base-patch32-384", device="cuda")

Performance

We have evaluated the performance on the following datasets:

Dataset	Metric Name	Metric Result
Food101	acc	0.642
CIFAR10	acc	0.862
CIFAR100	acc	0.529
Birdsnap	acc	0.161
SUN397	acc	0.510
Stanford Cars	acc	0.572
DTD	acc	0.390
MNIST	acc	0.404
STL10	acc	0.946
PCam	acc	0.506
CLEVR	acc	0.188
Rendered SST2	acc	0.508
ImageNet	acc	0.451
FGVC Aircraft	mean-per-class	0.053
Oxford Pets	mean-per-class	0.587
Caltech101	mean-per-class	0.834
Flowers102	mean-per-class	0.449
HatefulMemes	roc-auc	0.537

Authors

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