ruclip vit base patch32 384 RuCLIP ( Ru ssian C ontrastive L anguage– I mage P retraining) 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 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…
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