# Multimodal Visual Encoding
Openvision Vit Base Patch16 224
Apache-2.0
OpenVision is a fully open, cost-effective family of advanced visual encoders focused on multimodal learning.
Multimodal Fusion
O
UCSC-VLAA
79
0
Openvision Vit Huge Patch14 224
Apache-2.0
OpenVision is a fully open, cost-effective advanced vision encoder family focused on multimodal learning.
Multimodal Fusion
O
UCSC-VLAA
27
2
Openvision Vit Large Patch14 336
Apache-2.0
OpenVision is a fully open, cost-effective family of advanced visual encoders, specifically designed for multimodal learning.
Image Enhancement
Transformers

O
UCSC-VLAA
34
0
Openvision Vit Large Patch14 224
Apache-2.0
OpenVision is a fully open, cost-effective family of advanced vision encoders focused on multimodal learning.
Multimodal Fusion
O
UCSC-VLAA
308
4
Openvision Vit Base Patch8 224
Apache-2.0
OpenVision is a fully open, cost-effective family of advanced visual encoders focused on multimodal learning.
Image Classification
O
UCSC-VLAA
43
0
Openvision Vit Base Patch8 160
Apache-2.0
OpenVision-ViT-Tiny is a fully open, cost-effective advanced visual encoder, part of the OpenVision family, focusing on multimodal learning.
Image Classification
Transformers

O
UCSC-VLAA
26
0
Openvision Vit Small Patch8 224
Apache-2.0
OpenVision is a fully open, cost-effective advanced vision encoder family focused on multimodal learning.
O
UCSC-VLAA
25
0
Openvision Vit Tiny Patch8 384
Apache-2.0
OpenVision is a fully open, cost-effective advanced visual encoder family focused on multimodal learning.
Image Enhancement
Transformers

O
UCSC-VLAA
16
0
Openvision Vit Tiny Patch8 224
Apache-2.0
OpenVision is a fully open, cost-effective advanced vision encoder family focused on multimodal learning.
Multimodal Fusion
O
UCSC-VLAA
123
0
Openvision Vit Tiny Patch16 384
Apache-2.0
OpenVision is a fully open, cost-effective advanced vision encoder family focused on multimodal learning.
O
UCSC-VLAA
19
0
Openvision Vit Tiny Patch16 160
Apache-2.0
OpenVision is a fully open, cost-effective advanced visual encoder family focused on multimodal learning.
Multimodal Fusion
Transformers

O
UCSC-VLAA
30
0
Vit So400m Patch16 Siglip Gap 384.v2 Webli
Apache-2.0
A ViT image encoder based on SigLIP 2, utilizing global average pooling, with the attention pooling head removed, suitable for image feature extraction tasks.
Image Classification
Transformers

V
timm
19
0
Vit So400m Patch16 Siglip Gap 256.v2 Webli
Apache-2.0
ViT image encoder based on SigLIP 2, using global average pooling, with attention pooling head removed, suitable for image feature extraction tasks.
Text-to-Image
Transformers

V
timm
22
0
Vit So400m Patch16 Siglip 512.v2 Webli
Apache-2.0
A vision Transformer model based on SigLIP 2, designed for image feature extraction and suitable for multilingual vision-language tasks.
Text-to-Image
Transformers

V
timm
2,766
0
Vit So400m Patch16 Siglip 384.v2 Webli
Apache-2.0
Vision Transformer model based on SigLIP 2, designed for image feature extraction, pre-trained on the webli dataset
Text-to-Image
Transformers

V
timm
2,073
0
Vit So400m Patch16 Siglip 256.v2 Webli
Apache-2.0
SigLIP 2 ViT model, containing only the image encoder part for image feature extraction, trained on the WebLI dataset.
Text-to-Image
Transformers

V
timm
12.56k
0
Vit So400m Patch14 Siglip Gap 378.v2 Webli
Apache-2.0
Vision Transformer model based on SigLIP 2 architecture, pre-trained on WebLI dataset, with attention pooling head removed and global average pooling applied
Image Classification
Transformers

V
timm
20
0
Vit So400m Patch14 Siglip Gap 224.v2 Webli
Apache-2.0
A ViT image encoder based on SigLIP 2, employing global average pooling with the attention pooling head removed, suitable for image feature extraction tasks.
Image Classification
Transformers

V
timm
179
0
Vit So400m Patch14 Siglip 378.v2 Webli
Apache-2.0
Vision Transformer model based on SigLIP 2, designed for image feature extraction, trained on the webli dataset
Text-to-Image
Transformers

V
timm
30
0
Vit So400m Patch14 Siglip 224.v2 Webli
Apache-2.0
A Vision Transformer model based on SigLIP 2 architecture, designed for image feature extraction and pretrained on the webli dataset.
Image Classification
Transformers

V
timm
7,005
0
Vit Large Patch16 Siglip Gap 512.v2 Webli
Apache-2.0
A vision Transformer model based on SigLIP 2 architecture, designed for image feature extraction, using Global Average Pooling (GAP) instead of attention pooling head
Image Classification
Transformers

V
timm
29
0
Vit Large Patch16 Siglip Gap 384.v2 Webli
Apache-2.0
A vision Transformer model based on the SigLIP 2 architecture, featuring a Global Average Pooling (GAP) variant that removes the attention pooling head, suitable for image feature extraction tasks.
Text-to-Image
Transformers

V
timm
95
0
Vit Large Patch16 Siglip Gap 256.v2 Webli
Apache-2.0
A ViT image encoder based on SigLIP 2, employing global average pooling with the attention pooling head removed, specifically designed for image feature extraction.
Text-to-Image
Transformers

V
timm
95
0
Vit Large Patch16 Siglip 512.v2 Webli
Apache-2.0
ViT image encoder based on SigLIP 2, designed for timm, suitable for vision-language tasks
Image Classification
Transformers

V
timm
295
0
Vit Large Patch16 Siglip 384.v2 Webli
Apache-2.0
A vision Transformer model based on the SigLIP 2 architecture, designed for image feature extraction, pretrained on the webli dataset
Text-to-Image
Transformers

V
timm
4,265
0
Vit Large Patch16 Siglip 256.v2 Webli
Apache-2.0
Vision Transformer model based on SigLIP 2 architecture, designed for image feature extraction, trained on the webli dataset
Image Classification
Transformers

V
timm
525
0
Vit Giantopt Patch16 Siglip Gap 384.v2 Webli
Apache-2.0
A ViT image encoder based on SigLIP 2, utilizing global average pooling and removing the attention pooling head, suitable for image feature extraction tasks.
Image Classification
Transformers

V
timm
21
0
Vit Giantopt Patch16 Siglip Gap 256.v2 Webli
Apache-2.0
SigLIP 2 ViT image encoder, using global average pooling, with attention pooling head removed, designed specifically for timm
Image Classification
Transformers

V
timm
17
0
Vit Giantopt Patch16 Siglip 384.v2 Webli
Apache-2.0
ViT image encoder based on SigLIP 2, designed for timm, suitable for vision-language tasks
Image Classification
Transformers

V
timm
160
0
Vit Giantopt Patch16 Siglip 256.v2 Webli
Apache-2.0
Vision Transformer model based on SigLIP 2 technology, focused on image feature extraction
Text-to-Image
Transformers

V
timm
59
0
Vit Base Patch32 Siglip Gap 256.v2 Webli
Apache-2.0
A vision Transformer model based on SigLIP 2, using Global Average Pooling (GAP) instead of attention pooling head for image encoding
Text-to-Image
Transformers

V
timm
25
1
Vit Base Patch32 Siglip 256.v2 Webli
Apache-2.0
Vision Transformer model based on SigLIP 2 architecture, designed for image feature extraction
Text-to-Image
Transformers

V
timm
27
0
Vit Base Patch16 Siglip Gap 512.v2 Webli
Apache-2.0
A ViT image encoder based on SigLIP 2, using global average pooling with the attention pooling head removed, suitable for image feature extraction tasks.
Image Classification
Transformers

V
timm
105
0
Vit Base Patch16 Siglip Gap 384.v2 Webli
Apache-2.0
ViT image encoder based on SigLIP 2, using Global Average Pooling (GAP) instead of attention pooling head, suitable for image feature extraction tasks.
Image Classification
Transformers

V
timm
105
0
Vit Base Patch16 Siglip Gap 256.v2 Webli
Apache-2.0
A ViT image encoder based on SigLIP 2, employing global average pooling with the attention pooling head removed, suitable for image feature extraction.
Multimodal Fusion
Transformers

V
timm
114
1
Vit Base Patch16 Siglip Gap 224.v2 Webli
Apache-2.0
Vision Transformer model based on SigLIP 2, utilizing global average pooling for image features
Image Classification
Transformers

V
timm
303
0
Vit Base Patch16 Siglip 512.v2 Webli
Apache-2.0
Vision Transformer model based on SigLIP 2, designed for image feature extraction, pre-trained on the webli dataset
Text-to-Image
Transformers

V
timm
2,664
0
Vit Base Patch16 Siglip 384.v2 Webli
Apache-2.0
Vision Transformer model based on SigLIP 2, designed for image feature extraction, pre-trained on the webli dataset
Text-to-Image
Transformers

V
timm
330
0
Vit Base Patch16 Siglip 256.v2 Webli
Apache-2.0
A ViT image encoder based on SigLIP 2 for extracting image features, supporting multilingual vision-language tasks.
Text-to-Image
Transformers

V
timm
731
2
Vit Base Patch16 Siglip 224.v2 Webli
Apache-2.0
ViT model based on SigLIP 2, focused on image feature extraction, trained on the webli dataset
Text-to-Image
Transformers

V
timm
1,992
0
- 1
- 2
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