---
tags:
- timm
- image-feature-extraction
- transformers
library_name: timm
license: mit
datasets:
- laion-en
- laion-zh
- coyo
- grit
- coco
- textcaps
- objects365
- openimages
- all-seeing
- wukong-ocr
- laioncoco-ocr
- other-ocr
---
# Model card for vit_intern300m_patch14_448.ogvl_dist

An InternViT image feature model. Pretrained with distillation from [InternViT-6B](https://huggingface.co/OpenGVLab/InternViT-6B-448px-V1-5) by paper authors with a wide variety of image-text data. Model weights have been converted from original to `timm` vit from [OpenGVLab/InternViT-300M-448px](https://huggingface.co/OpenGVLab/InternViT-300M-448px). NOTE: this vit has no final norm before features / head.

## Model Details
- **Model Type:** Image classification / feature backbone
- **Model Stats:**
  - Params (M): 304.0
  - GMACs: 362.0
  - Activations (M): 656.4
  - Image size: 448 x 448
- **Papers:**
  - InternVL2: Better than the Best: https://internvl.github.io/blog/2024-07-02-InternVL-2.0/
  - InternVL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Tasks: https://arxiv.org/abs/2312.14238
- **Original:** https://github.com/OpenGVLab/InternVL
- **Dataset:**
  - LAION-en
  - LAION-zh
  - COYO
  - GRIT
  - COCO
  - TextCaps
  - Objects365
  - OpenImages
  - All-Seeing
  - Wukong-OCR
  - LaionCOCO-OCR
  - other-OCR

## Model Usage
### Image Classification
```python
from urllib.request import urlopen
from PIL import Image
import timm

img = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))

model = timm.create_model('vit_intern300m_patch14_448.ogvl_dist', pretrained=True)
model = model.eval()

# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)

output = model(transforms(img).unsqueeze(0))  # unsqueeze single image into batch of 1

top5_probabilities, top5_class_indices = torch.topk(output.softmax(dim=1) * 100, k=5)
```

### Feature Map Extraction
```python
from urllib.request import urlopen
from PIL import Image
import timm

img = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))

model = timm.create_model(
    'vit_intern300m_patch14_448.ogvl_dist',
    pretrained=True,
    features_only=True,
)
model = model.eval()

# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)

output = model(transforms(img).unsqueeze(0))  # unsqueeze single image into batch of 1

for o in output:
    # print shape of each feature map in output
    # e.g.:
    #  torch.Size([1, 1024, 32, 32])
    #  torch.Size([1, 1024, 32, 32])
    #  torch.Size([1, 1024, 32, 32])

    print(o.shape)
```

### Image Embeddings
```python
from urllib.request import urlopen
from PIL import Image
import timm

img = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))

model = timm.create_model(
    'vit_intern300m_patch14_448.ogvl_dist',
    pretrained=True,
    num_classes=0,  # remove classifier nn.Linear
)
model = model.eval()

# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)

output = model(transforms(img).unsqueeze(0))  # output is (batch_size, num_features) shaped tensor

# or equivalently (without needing to set num_classes=0)

output = model.forward_features(transforms(img).unsqueeze(0))
# output is unpooled, a (1, 1025, 1024) shaped tensor

output = model.forward_head(output, pre_logits=True)
# output is a (1, num_features) shaped tensor
```

## Citation
```bibtex
@article{chen2023internvl,
  title={InternVL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Tasks},
  author={Chen, Zhe and Wu, Jiannan and Wang, Wenhai and Su, Weijie and Chen, Guo and Xing, Sen and Zhong, Muyan and Zhang, Qinglong and Zhu, Xizhou and Lu, Lewei and Li, Bin and Luo, Ping and Lu, Tong and Qiao, Yu and Dai, Jifeng},
  journal={arXiv preprint arXiv:2312.14238},
  year={2023}
}
```
```bibtex
@article{chen2023internvl,
  title={InternVL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Tasks},
  author={Chen, Zhe and Wu, Jiannan and Wang, Wenhai and Su, Weijie and Chen, Guo and Xing, Sen and Zhong, Muyan and Zhang, Qinglong and Zhu, Xizhou and Lu, Lewei and Li, Bin and Luo, Ping and Lu, Tong and Qiao, Yu and Dai, Jifeng},
  journal={arXiv preprint arXiv:2312.14238},
  year={2023}
}
```