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ai-forever/
ruclip-vit-base-patch16-224

Name: ruclip-vit-base-patch16-224
Availability: InStock

transformers

pytorch

endpoints_compatible

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ruclip-vit-base-patch16-224

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: 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: 224
Vision Layers: 12
Vision Width: 768
Vision Patch Size: 16

Usage Github

pip install ruclip

clip, processor = ruclip.load("ruclip-vit-base-patch16-224", device="cuda")

Performance

We have evaluated the performance on the following datasets:

Dataset	Metric Name	Metric Result
Food101	acc	0.552
CIFAR10	acc	0.810
CIFAR100	acc	0.496
Birdsnap	acc	0.117
SUN397	acc	0.462
Stanford Cars	acc	0.487
DTD	acc	0.401
MNIST	acc	0.464
STL10	acc	0.932
PCam	acc	0.505
CLEVR	acc	0.128
Rendered SST2	acc	0.527
ImageNet	acc	0.401
FGVC Aircraft	mean-per-class	0.043
Oxford Pets	mean-per-class	0.595
Caltech101	mean-per-class	0.775
Flowers102	mean-per-class	0.388
HatefulMemes	roc-auc	0.516

Authors

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

ai-forever/ruclip-vit-base-patch16-224

ruclip-vit-base-patch16-224

Usage Github

Performance

Authors

ai-forever/
ruclip-vit-base-patch16-224