metadata
library_name: lucid
license: apache-2.0
tags:
- image-classification
- efficientnet
- lucid
datasets:
- imagenet-1k
pipeline_tag: image-classification
model-index:
- name: efficientnet-b0
results:
- task:
type: image-classification
dataset:
name: ImageNet-1K
type: imagenet-1k
metrics:
- type: acc@1
value: 77.692
- type: acc@5
value: 93.532
EfficientNet-B0
Tan & Le, 2019 — EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks (arXiv:1905.11946)
Lucid port of torchvision/EfficientNet_B0_Weights.IMAGENET1K_V1,
converted to Lucid-native safetensors.
Available weights
| Tag | acc@1 | acc@5 | Params | GFLOPs | Size | Source |
|---|---|---|---|---|---|---|
IMAGENET1K_V1 (default) |
77.692 | 93.532 | 5.3M | 0.386 | 20.37 MB | torchvision |
Usage
import lucid.models as models
from lucid.models.weights import EfficientNetB0Weights
# default tag
model = models.efficientnet_b0_cls(pretrained=True)
# explicit tag (enum or string)
model = models.efficientnet_b0_cls(weights=EfficientNetB0Weights.IMAGENET1K_V1)
model = models.efficientnet_b0_cls(pretrained="IMAGENET1K_V1")
# preprocessing travels with the weights
weights = EfficientNetB0Weights.IMAGENET1K_V1
preprocess = weights.transforms()
logits = model(preprocess(image)[None]).logits
Conversion
Converted from torchvision/EfficientNet_B0_Weights.IMAGENET1K_V1 via
python -m tools.convert_weights efficientnet_b0 --tag IMAGENET1K_V1.
Key mapping + numerical parity verified against the source.
License
apache-2.0 — inherited from the original weights.
Citation
@inproceedings{tan2019efficientnet,
title={EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks},
author={Tan, Mingxing and Le, Quoc},
booktitle={ICML}, year={2019}
}