README.md

---
library_name: lucid
license: apache-2.0
tags:
  - image-classification
  - efficientnet
  - lucid
datasets:
  - imagenet-1k
pipeline_tag: image-classification
model-index:
  - name: efficientnet-b4
    results:
      - task: { type: image-classification }
        dataset: { name: ImageNet-1K, type: imagenet-1k }
        metrics:
          - { type: acc@1, value: 83.384 }
          - { type: acc@5, value: 96.594 }
---

# EfficientNet-B4

> Tan & Le, 2019 — *EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks* (arXiv:1905.11946)

[Lucid](https://github.com/ChanLumerico/lucid) port of `torchvision/EfficientNet_B4_Weights.IMAGENET1K_V1`,
converted to Lucid-native safetensors.

## Available weights

| Tag | acc@1 | acc@5 | Params | GFLOPs | Size | Source |
|---|---|---|---|---|---|---|
| `IMAGENET1K_V1` *(default)* | 83.384 | 96.594 | 19.3M | 4.394 | 74.33 MB | torchvision |

## Usage

```python
import lucid.models as models
from lucid.models.weights import EfficientNetB4Weights

# default tag
model = models.efficientnet_b4_cls(pretrained=True)

# explicit tag (enum or string)
model = models.efficientnet_b4_cls(weights=EfficientNetB4Weights.IMAGENET1K_V1)
model = models.efficientnet_b4_cls(pretrained="IMAGENET1K_V1")

# preprocessing travels with the weights
weights = EfficientNetB4Weights.IMAGENET1K_V1
preprocess = weights.transforms()
logits = model(preprocess(image)[None]).logits
```

## Conversion

Converted from `torchvision/EfficientNet_B4_Weights.IMAGENET1K_V1` via
`python -m tools.convert_weights efficientnet_b4 --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}
}
```