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---
tags:
- image-classification
- timm
library_name: timm
license: apache-2.0
datasets:
- imagenet-1k
---
# Model card for test_efficientnet.r160_in1k
A very small test EfficientNet 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: 0.1
- Activations (M): 0.6
- 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_efficientnet.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_efficientnet.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, 16, 80, 80])
# torch.Size([1, 24, 40, 40])
# torch.Size([1, 32, 20, 20])
# torch.Size([1, 48, 10, 10])
# torch.Size([1, 64, 5, 5])
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_efficientnet.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, 256, 5, 5) shaped tensor
output = model.forward_head(output, pre_logits=True)
# output is a (1, num_features) shaped tensor
```
## Model Comparison
### By Top-1
|model |img_size|top1 |top5 |param_count|
|--------------------------------|--------|------|------|-----------|
|test_convnext3.r160_in1k |192 |54.558|79.356|0.47 |
|test_convnext2.r160_in1k |192 |53.62 |78.636|0.48 |
|test_convnext2.r160_in1k |160 |53.51 |78.526|0.48 |
|test_convnext3.r160_in1k |160 |53.328|78.318|0.47 |
|test_convnext.r160_in1k |192 |48.532|74.944|0.27 |
|test_nfnet.r160_in1k |192 |48.298|73.446|0.38 |
|test_convnext.r160_in1k |160 |47.764|74.152|0.27 |
|test_nfnet.r160_in1k |160 |47.616|72.898|0.38 |
|test_efficientnet.r160_in1k |192 |47.164|71.706|0.36 |
|test_efficientnet_evos.r160_in1k|192 |46.924|71.53 |0.36 |
|test_byobnet.r160_in1k |192 |46.688|71.668|0.46 |
|test_efficientnet_evos.r160_in1k|160 |46.498|71.006|0.36 |
|test_efficientnet.r160_in1k |160 |46.454|71.014|0.36 |
|test_byobnet.r160_in1k |160 |45.852|70.996|0.46 |
|test_efficientnet_ln.r160_in1k |192 |44.538|69.974|0.36 |
|test_efficientnet_gn.r160_in1k |192 |44.448|69.75 |0.36 |
|test_efficientnet_ln.r160_in1k |160 |43.916|69.404|0.36 |
|test_efficientnet_gn.r160_in1k |160 |43.88 |69.162|0.36 |
|test_vit2.r160_in1k |192 |43.454|69.798|0.46 |
|test_resnet.r160_in1k |192 |42.376|68.744|0.47 |
|test_vit2.r160_in1k |160 |42.232|68.982|0.46 |
|test_vit.r160_in1k |192 |41.984|68.64 |0.37 |
|test_resnet.r160_in1k |160 |41.578|67.956|0.47 |
|test_vit.r160_in1k |160 |40.946|67.362|0.37 |
## 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}}
}
```