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ai-forever/
ruclip-vit-large-patch14-224

Name: ruclip-vit-large-patch14-224
Availability: InStock

transformers

pytorch

endpoints_compatible

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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

ai-forever/ruclip-vit-large-patch14-224

ruclip-vit-large-patch14-224

Usage Github

Performance

Authors

ai-forever/
ruclip-vit-large-patch14-224