metadata
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 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
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}
}