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library_name: lucid
license: apache-2.0
tags:
- image-classification
- efficientformer
- lucid
datasets:
- imagenet-1k
pipeline_tag: image-classification
model-index:
- name: efficientformer-l3
results:
- task: { type: image-classification }
dataset: { name: ImageNet-1k, type: imagenet-1k }
metrics:
- { type: acc@1, value: 82.4 }
---
# EfficientFormer-L3
> Li et al., 2022 — *EfficientFormer: Vision Transformers at MobileNet Speed* (arXiv:2206.01191)
[Lucid](https://github.com/ChanLumerico/lucid) port of `timm/efficientformer_l3.snap_dist_in1k`,
converted to Lucid-native safetensors.
## Available weights
| Tag | acc@1 | acc@5 | Params | GFLOPs | Size | Source |
|---|---|---|---|---|---|---|
| `SNAP_DIST_IN1K` *(default)* | 82.4 | — | 31.4M | — | 120.07 MB | timm |
## Usage
```python
import lucid.models as models
from lucid.models.weights import EfficientFormerL3Weights
# default tag
model = models.efficientformer_l3_cls(pretrained=True)
# explicit tag (enum or string)
model = models.efficientformer_l3_cls(weights=EfficientFormerL3Weights.SNAP_DIST_IN1K)
model = models.efficientformer_l3_cls(pretrained="SNAP_DIST_IN1K")
# preprocessing travels with the weights
weights = EfficientFormerL3Weights.SNAP_DIST_IN1K
preprocess = weights.transforms()
logits = model(preprocess(image)[None]).logits
```
## Conversion
Converted from `timm/efficientformer_l3.snap_dist_in1k` via
`python -m tools.convert_weights efficientformer_l3 --tag SNAP_DIST_IN1K`.
Key mapping + numerical parity verified against the source.
## License
`apache-2.0` — inherited from the original weights.
## Citation
```
@article{li2022efficientformer,
title={EfficientFormer: Vision Transformers at MobileNet Speed},
author={Li, Yanyu and Yuan, Geng and Wen, Yang and Hu, Eric and Evangelidis, Georgios and Tulyakov, Sergey and Wang, Yanzhi and Ren, Jian},
journal={Advances in Neural Information Processing Systems},
volume={35}, year={2022}
}
```
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