rexnet_100.nav_in1k / README.md
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---
license: mit
library_name: timm
tags:
- image-classification
- timm
datasets:
- imagenet-1k
---
# Model card for rexnet_100.nav_in1k
A ReXNet image classification model. Pretrained on ImageNet-1k by paper authors.
## Model Details
- **Model Type:** Image classification / feature backbone
- **Model Stats:**
- Params (M): 4.8
- GMACs: 0.4
- Activations (M): 7.4
- Image size: 224 x 224
- **Papers:**
- Rethinking Channel Dimensions for Efficient Model Design: https://arxiv.org/abs/2007.00992
- **Original:** https://github.com/clovaai/rexnet
- **Dataset:** ImageNet-1k
## 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('rexnet_100.nav_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(
'rexnet_100.nav_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, 112, 112])
# torch.Size([1, 38, 56, 56])
# torch.Size([1, 61, 28, 28])
# torch.Size([1, 128, 14, 14])
# torch.Size([1, 185, 7, 7])
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(
'rexnet_100.nav_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, 1280, 7, 7) shaped tensor
output = model.forward_head(output, pre_logits=True)
# output is a (1, num_features) shaped tensor
```
## Model Comparison
Explore the dataset and runtime metrics of this model in timm [model results](https://github.com/huggingface/pytorch-image-models/tree/main/results)."
|model |top1 |top5 |param_count|img_size|crop_pct|
|-------------------------|------|------|-----------|--------|--------|
|rexnetr_300.sw_in12k_ft_in1k|84.53 |97.252|34.81 |288 |1.0 |
|rexnetr_200.sw_in12k_ft_in1k|83.164|96.648|16.52 |288 |1.0 |
|rexnet_300.nav_in1k |82.772|96.232|34.71 |224 |0.875 |
|rexnet_200.nav_in1k |81.652|95.668|16.37 |224 |0.875 |
|rexnet_150.nav_in1k |80.308|95.174|9.73 |224 |0.875 |
|rexnet_130.nav_in1k |79.478|94.68 |7.56 |224 |0.875 |
|rexnet_100.nav_in1k |77.832|93.886|4.8 |224 |0.875 |
## Citation
```bibtex
@misc{han2021rethinking,
title={Rethinking Channel Dimensions for Efficient Model Design},
author={Dongyoon Han and Sangdoo Yun and Byeongho Heo and YoungJoon Yoo},
year={2021},
eprint={2007.00992},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
```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}}
}
```