README.md

---
library_name: lucid
license: apache-2.0
tags:
  - image-classification
  - efficientformer
  - lucid
datasets:
  - imagenet-1k
pipeline_tag: image-classification
model-index:
  - name: efficientformer-l1
    results:
      - task: { type: image-classification }
        dataset: { name: ImageNet-1k, type: imagenet-1k }
        metrics:
          - { type: acc@1, value: 79.2 }
---

# EfficientFormer-L1

> Li et al., 2022 — *EfficientFormer: Vision Transformers at MobileNet Speed* (arXiv:2206.01191)

[Lucid](https://github.com/ChanLumerico/lucid) port of `timm/efficientformer_l1.snap_dist_in1k`,
converted to Lucid-native safetensors.

## Available weights

| Tag | acc@1 | acc@5 | Params | GFLOPs | Size | Source |
|---|---|---|---|---|---|---|
| `SNAP_DIST_IN1K` *(default)* | 79.2 | — | 12.3M | — | 47.03 MB | timm |

## Usage

```python
import lucid.models as models
from lucid.models.weights import EfficientFormerL1Weights

# default tag
model = models.efficientformer_l1_cls(pretrained=True)

# explicit tag (enum or string)
model = models.efficientformer_l1_cls(weights=EfficientFormerL1Weights.SNAP_DIST_IN1K)
model = models.efficientformer_l1_cls(pretrained="SNAP_DIST_IN1K")

# preprocessing travels with the weights
weights = EfficientFormerL1Weights.SNAP_DIST_IN1K
preprocess = weights.transforms()
logits = model(preprocess(image)[None]).logits
```

## Conversion

Converted from `timm/efficientformer_l1.snap_dist_in1k` via
`python -m tools.convert_weights efficientformer_l1 --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}
}
```