metadata
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 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
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}
}