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---
tags:
- image-classification
- timm
library_name: timm
license: apache-2.0
datasets:
- imagenet-1k
- unknown-6m
---
# Model card for nextvit_small.bd_ssld_6m_in1k_384
A Next-ViT image classification model. Trained by paper authors on an unknown 6M sample dataset and ImageNet-1k using SSLD distillation.
## Model Details
- **Model Type:** Image classification / feature backbone
- **Model Stats:**
- Params (M): 31.8
- GMACs: 17.0
- Activations (M): 51.7
- Image size: 384 x 384
- **Pretrain Dataset:** Unknown-6M
- **Dataset:** ImageNet-1k
- **Papers:**
- Next-ViT: Next Generation Vision Transformer for Efficient Deployment in Realistic Industrial Scenarios: https://arxiv.org/abs/2207.05501
- **Original:** https://github.com/bytedance/Next-ViT
## 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('nextvit_small.bd_ssld_6m_in1k_384', 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(
'nextvit_small.bd_ssld_6m_in1k_384',
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, 96, 96, 96])
# torch.Size([1, 256, 48, 48])
# torch.Size([1, 512, 24, 24])
# torch.Size([1, 1024, 12, 12])
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(
'nextvit_small.bd_ssld_6m_in1k_384',
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, 1024, 12, 12) 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|
|---------------------------------|------|--------|------|--------|-----------|
|nextvit_large.bd_ssld_6m_in1k_384|86.542|13.458 |98.142|1.858 |57.87 |
|nextvit_base.bd_ssld_6m_in1k_384 |86.352|13.648 |98.04 |1.96 |44.82 |
|nextvit_small.bd_ssld_6m_in1k_384|85.964|14.036 |97.908|2.092 |31.76 |
|nextvit_large.bd_ssld_6m_in1k |85.48 |14.52 |97.696|2.304 |57.87 |
|nextvit_base.bd_ssld_6m_in1k |85.186|14.814 |97.59 |2.41 |44.82 |
|nextvit_large.bd_in1k_384 |84.924|15.076 |97.294|2.706 |57.87 |
|nextvit_small.bd_ssld_6m_in1k |84.862|15.138 |97.382|2.618 |31.76 |
|nextvit_base.bd_in1k_384 |84.706|15.294 |97.224|2.776 |44.82 |
|nextvit_small.bd_in1k_384 |84.022|15.978 |96.99 |3.01 |31.76 |
|nextvit_large.bd_in1k |83.626|16.374 |96.694|3.306 |57.87 |
|nextvit_base.bd_in1k |83.472|16.528 |96.656|3.344 |44.82 |
|nextvit_small.bd_in1k |82.61 |17.39 |96.226|3.774 |31.76 |
## Citation
```bibtex
@article{li2022next,
title={Next-ViT: Next Generation Vision Transformer for Efficient Deployment in Realistic Industrial Scenarios},
author={Li, Jiashi and Xia, Xin and Li, Wei and Li, Huixia and Wang, Xing and Xiao, Xuefeng and Wang, Rui and Zheng, Min and Pan, Xin},
journal={arXiv preprint arXiv:2207.05501},
year={2022}
}
```
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