Update model card

README.md

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+---
+library_name: lucid
+license: apache-2.0
+tags:
+  - image-classification
+  - maxvit
+  - lucid
+datasets:
+  - imagenet-1k
+pipeline_tag: image-classification
+model-index:
+  - name: maxvit-tiny
+    results:
+      - task: { type: image-classification }
+        dataset: { name: ImageNet-1k, type: imagenet-1k }
+        metrics:
+          - { type: acc@1, value: 83.62 }
+          - { type: acc@5, value: 96.49 }
+---
+
+# MaxViT-Tiny
+
+> Tu et al., 2022 — *MaxViT: Multi-Axis Vision Transformer* (arXiv:2204.01697)
+
+[Lucid](https://github.com/ChanLumerico/lucid) port of `timm/maxvit_tiny_tf_224.in1k`,
+converted to Lucid-native safetensors.
+
+## Available weights
+
+| Tag | acc@1 | acc@5 | Params | GFLOPs | Size | Source |
+|---|---|---|---|---|---|---|
+| `IN1K` *(default)* | 83.62 | 96.49 | 30.9M | — | 118.18 MB | timm |
+
+## Usage
+
+```python
+import lucid.models as models
+from lucid.models.vision.resnet import MaxvitTinyWeights
+
+# default tag
+model = models.maxvit_tiny_cls(pretrained=True)
+
+# explicit tag (enum or string)
+model = models.maxvit_tiny_cls(weights=MaxvitTinyWeights.IN1K)
+model = models.maxvit_tiny_cls(pretrained="IN1K")
+
+# preprocessing travels with the weights
+weights = MaxvitTinyWeights.IN1K
+preprocess = weights.transforms()
+logits = model(preprocess(image)[None]).logits
+```
+
+## Conversion
+
+Converted from `timm/maxvit_tiny_tf_224.in1k` via
+`python -m tools.convert_weights maxvit_tiny --tag IN1K`.
+Key mapping + numerical parity verified against the source.
+
+## License
+
+`apache-2.0` — inherited from the original weights.
+
+## Citation
+
+```
+@inproceedings{tu2022maxvit,
+  title={MaxViT: Multi-Axis Vision Transformer},
+  author={Tu, Zhengzhong and Talebi, Hossein and Zhang, Han and Yang, Feng and Milanfar, Peyman and Bovik, Alan and Li, Yinxiao},
+  booktitle={ECCV}, year={2022}
+}
+```