metadata
library_name: lucid
license: apache-2.0
tags:
- image-classification
- cvt
- lucid
datasets:
- imagenet-1k
pipeline_tag: image-classification
model-index:
- name: cvt-21
results:
- task:
type: image-classification
dataset:
name: ImageNet-1k
type: imagenet-1k
metrics:
- type: acc@1
value: 82.5
CvT-21
Wu et al., 2021 — CvT: Introducing Convolutions to Vision Transformers (arXiv:2103.15808)
Lucid port of transformers/microsoft/cvt-21,
converted to Lucid-native safetensors.
Available weights
| Tag | acc@1 | acc@5 | Params | GFLOPs | Size | Source |
|---|---|---|---|---|---|---|
IN1K (default) |
82.5 | — | 31.6M | — | 120.87 MB | transformers |
Usage
import lucid.models as models
from lucid.models.weights import CvT21Weights
# default tag
model = models.cvt_21_cls(pretrained=True)
# explicit tag (enum or string)
model = models.cvt_21_cls(weights=CvT21Weights.IN1K)
model = models.cvt_21_cls(pretrained="IN1K")
# preprocessing travels with the weights
weights = CvT21Weights.IN1K
preprocess = weights.transforms()
logits = model(preprocess(image)[None]).logits
Conversion
Converted from transformers/microsoft/cvt-21 via
python -m tools.convert_weights cvt_21 --tag IN1K.
Key mapping + numerical parity verified against the source.
License
apache-2.0 — inherited from the original weights.
Citation
@inproceedings{wu2021cvt,
title={CvT: Introducing Convolutions to Vision Transformers},
author={Wu, Haiping and Xiao, Bin and Codella, Noel and Liu, Mengchen and Dai, Xiyang and Yuan, Lu and Zhang, Lei},
booktitle={ICCV}, year={2021}
}