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library_name: lucid
license: apache-2.0
tags:
- image-classification
- efficientnet
- lucid
datasets:
- imagenet-1k
pipeline_tag: image-classification
model-index:
- name: efficientnet-b5
results:
- task: { type: image-classification }
dataset: { name: ImageNet-1K, type: imagenet-1k }
metrics:
- { type: acc@1, value: 83.444 }
- { type: acc@5, value: 96.628 }
---
# EfficientNet-B5
> Tan & Le, 2019 — *EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks* (arXiv:1905.11946)
[Lucid](https://github.com/ChanLumerico/lucid) port of `torchvision/EfficientNet_B5_Weights.IMAGENET1K_V1`,
converted to Lucid-native safetensors.
## Available weights
| Tag | acc@1 | acc@5 | Params | GFLOPs | Size | Source |
|---|---|---|---|---|---|---|
| `IMAGENET1K_V1` *(default)* | 83.444 | 96.628 | 30.4M | 10.266 | 116.67 MB | torchvision |
## Usage
```python
import lucid.models as models
from lucid.models.weights import EfficientNetB5Weights
# default tag
model = models.efficientnet_b5_cls(pretrained=True)
# explicit tag (enum or string)
model = models.efficientnet_b5_cls(weights=EfficientNetB5Weights.IMAGENET1K_V1)
model = models.efficientnet_b5_cls(pretrained="IMAGENET1K_V1")
# preprocessing travels with the weights
weights = EfficientNetB5Weights.IMAGENET1K_V1
preprocess = weights.transforms()
logits = model(preprocess(image)[None]).logits
```
## Conversion
Converted from `torchvision/EfficientNet_B5_Weights.IMAGENET1K_V1` via
`python -m tools.convert_weights efficientnet_b5 --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}
}
```
|