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library_name: lucid
license: apache-2.0
tags:
- image-classification
- maxvit
- lucid
datasets:
- imagenet-1k
pipeline_tag: image-classification
model-index:
- name: maxvit-base
results:
- task: { type: image-classification }
dataset: { name: ImageNet-1k, type: imagenet-1k }
metrics:
- { type: acc@1, value: 84.95 }
- { type: acc@5, value: 97.04 }
---
# MaxViT-Base
> Tu et al., 2022 — *MaxViT: Multi-Axis Vision Transformer* (arXiv:2204.01697)
[Lucid](https://github.com/ChanLumerico/lucid) port of `timm/maxvit_base_tf_224.in1k`,
converted to Lucid-native safetensors.
## Available weights
| Tag | acc@1 | acc@5 | Params | GFLOPs | Size | Source |
|---|---|---|---|---|---|---|
| `IN1K` *(default)* | 84.95 | 97.04 | 119.5M | — | 456.43 MB | timm |
## Usage
```python
import lucid.models as models
from lucid.models.weights import MaxViTBaseWeights
# default tag
model = models.maxvit_base_cls(pretrained=True)
# explicit tag (enum or string)
model = models.maxvit_base_cls(weights=MaxViTBaseWeights.IN1K)
model = models.maxvit_base_cls(pretrained="IN1K")
# preprocessing travels with the weights
weights = MaxViTBaseWeights.IN1K
preprocess = weights.transforms()
logits = model(preprocess(image)[None]).logits
```
## Conversion
Converted from `timm/maxvit_base_tf_224.in1k` via
`python -m tools.convert_weights maxvit_base --tag IN1K`.
Key mapping + numerical parity verified against the source.
## License
`apache-2.0` — inherited from the original weights.
## Citation
```
@inproceedings{tu2022maxvit,
title={MaxViT: Multi-Axis Vision Transformer},
author={Tu, Zhengzhong and Talebi, Hossein and Zhang, Han and Yang, Feng and Milanfar, Peyman and Bovik, Alan and Li, Yinxiao},
booktitle={ECCV}, year={2022}
}
```
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