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ai-forever/
ruclip-vit-large-patch14-336

Name: ruclip-vit-large-patch14-336
Availability: InStock

transformers

pytorch

endpoints_compatible

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ruclip-vit-large-patch14-336

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: 336
Vision Layers: 24
Vision Width: 1024
Vision Patch Size: 14

Usage Github

pip install ruclip

clip, processor = ruclip.load("ruclip-vit-large-patch14-336", device="cuda")

Performance

We have evaluated the performance on the following datasets:

Dataset	Metric Name	Metric Result
Food101	acc	0.712
CIFAR10	acc	0.906
CIFAR100	acc	0.591
Birdsnap	acc	0.213
SUN397	acc	0.523
Stanford Cars	acc	0.659
DTD	acc	0.408
MNIST	acc	0.242
STL10	acc	0.956
PCam	acc	0.554
CLEVR	acc	0.142
Rendered SST2	acc	0.539
ImageNet	acc	0.488
FGVC Aircraft	mean-per-class	0.075
Oxford Pets	mean-per-class	0.546
Caltech101	mean-per-class	0.835
Flowers102	mean-per-class	0.517
HatefulMemes	roc-auc	0.519

Authors

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

ai-forever/ruclip-vit-large-patch14-336

ruclip-vit-large-patch14-336

Usage Github

Performance

Authors

ai-forever/
ruclip-vit-large-patch14-336