metadata
library_name: lucid
license: apache-2.0
tags:
- image-classification
- crossvit
- lucid
datasets:
- imagenet-1k
pipeline_tag: image-classification
model-index:
- name: crossvit-tiny
results:
- task:
type: image-classification
dataset:
name: ImageNet-1k
type: imagenet-1k
metrics:
- type: acc@1
value: 72.6
CrossViT-Ti
Chen et al., 2021 — CrossViT: Cross-Attention Multi-Scale Vision Transformer for Image Classification (arXiv:2103.14899)
Lucid port of timm/crossvit_tiny_240.in1k,
converted to Lucid-native safetensors.
Available weights
| Tag | acc@1 | acc@5 | Params | GFLOPs | Size | Source |
|---|---|---|---|---|---|---|
IN1K (default) |
72.6 | — | 7.0M | — | 26.79 MB | timm |
Usage
import lucid.models as models
from lucid.models.weights import CrossViTTinyWeights
# default tag
model = models.crossvit_tiny_cls(pretrained=True)
# explicit tag (enum or string)
model = models.crossvit_tiny_cls(weights=CrossViTTinyWeights.IN1K)
model = models.crossvit_tiny_cls(pretrained="IN1K")
# preprocessing travels with the weights
weights = CrossViTTinyWeights.IN1K
preprocess = weights.transforms()
logits = model(preprocess(image)[None]).logits
Conversion
Converted from timm/crossvit_tiny_240.in1k via
python -m tools.convert_weights crossvit_tiny --tag IN1K.
Key mapping + numerical parity verified against the source.
License
apache-2.0 — inherited from the original weights.
Citation
@inproceedings{chen2021crossvit,
title={CrossViT: Cross-Attention Multi-Scale Vision Transformer for Image Classification},
author={Chen, Chun-Fu (Richard) and Fan, Quanfu and Panda, Rameswar},
booktitle={ICCV}, year={2021}
}