README.md

---
tags:
- image-classification
- timm
library_name: timm
license: apache-2.0
datasets:
- imagenet-1k
---
# Model card for test_vit.r160_in1k

A very small test Vision Transformer image classification model for testing and sanity checks. Trained on ImageNet-1k by Ross Wightman.

## Model Details
- **Model Type:** Image classification / feature backbone
- **Model Stats:**
  - Params (M): 0.4
  - GMACs: 0.0
  - Activations (M): 0.3
  - Image size: 160 x 160
- **Dataset:** ImageNet-1k
- **Papers:**
  - PyTorch Image Models: https://github.com/huggingface/pytorch-image-models
- **Original:** https://github.com/huggingface/pytorch-image-models

## 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('test_vit.r160_in1k', 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(
    'test_vit.r160_in1k',
    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, 64, 10, 10])
    #  torch.Size([1, 64, 10, 10])
    #  torch.Size([1, 64, 10, 10])

    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(
    'test_vit.r160_in1k',
    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, 101, 64) shaped tensor

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

## Model Comparison
### By Top-1

|model                       |top1  |top1_err|top5  |top5_err|param_count|img_size|crop_pct|
|----------------------------|------|--------|------|--------|-----------|--------|--------|
|test_efficientnet.r160_in1k |47.156|52.844  |71.726|28.274  |0.36       |192     |1.0     |
|test_byobnet.r160_in1k      |46.698|53.302  |71.674|28.326  |0.46       |192     |1.0     |
|test_efficientnet.r160_in1k |46.426|53.574  |70.928|29.072  |0.36       |160     |0.875   |
|test_byobnet.r160_in1k      |45.378|54.622  |70.572|29.428  |0.46       |160     |0.875   |
|test_vit.untrained.r160_in1k|42.0  |58.0    |68.664|31.336  |0.37       |192     |1.0     |
|test_vit.untrained.r160_in1k|40.822|59.178  |67.212|32.788  |0.37       |160     |0.875   |

## Citation
```bibtex
@misc{rw2019timm,
  author = {Ross Wightman},
  title = {PyTorch Image Models},
  year = {2019},
  publisher = {GitHub},
  journal = {GitHub repository},
  doi = {10.5281/zenodo.4414861},
  howpublished = {\url{https://github.com/huggingface/pytorch-image-models}}
}
```