Update model card

README.md

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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-b1
+    results:
+      - task: { type: image-classification }
+        dataset: { name: ImageNet-1K, type: imagenet-1k }
+        metrics:
+          - { type: acc@1, value: 78.642 }
+          - { type: acc@5, value: 94.186 }
+---
+
+# EfficientNet-B1
+
+> Tan & Le, 2019 — *EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks* (arXiv:1905.11946)
+
+[Lucid](https://github.com/ChanLumerico/lucid) port of `torchvision/EfficientNet_B1_Weights.IMAGENET1K_V1`,
+converted to Lucid-native safetensors.
+
+## Available weights
+
+| Tag | acc@1 | acc@5 | Params | GFLOPs | Size | Source |
+|---|---|---|---|---|---|---|
+| `IMAGENET1K_V1` *(default)* | 78.642 | 94.186 | 7.8M | 0.687 | 30.02 MB | torchvision |
+
+## Usage
+
+```python
+import lucid.models as models
+from lucid.models.vision.resnet import EfficientnetB1Weights
+
+# default tag
+model = models.efficientnet_b1_cls(pretrained=True)
+
+# explicit tag (enum or string)
+model = models.efficientnet_b1_cls(weights=EfficientnetB1Weights.IMAGENET1K_V1)
+model = models.efficientnet_b1_cls(pretrained="IMAGENET1K_V1")
+
+# preprocessing travels with the weights
+weights = EfficientnetB1Weights.IMAGENET1K_V1
+preprocess = weights.transforms()
+logits = model(preprocess(image)[None]).logits
+```
+
+## Conversion
+
+Converted from `torchvision/EfficientNet_B1_Weights.IMAGENET1K_V1` via
+`python -m tools.convert_weights efficientnet_b1 --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}
+}
+```