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Update README.md with new model card content
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README.md
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library_name: keras-hub
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DenseNet is a convolution network which densely connects each layer to every other layer in a feed-forward fashion. The model was originally evaluated on four object recognition benchmark tasks (CIFAR-10, CIFAR-100, SVHN, and ImageNet). See the model card below for benchmarks, data sources, and intended use cases. This model is supported in both KerasCV and KerasHub. KerasCV will no longer be actively developed, so please try to use KerasHub.
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Weights are released under the [3-Clause BSD License](https://github.com/liuzhuang13/DenseNet/blob/master/LICENSE). Keras model code is released under the [Apache 2 License](https://github.com/keras-team/keras-hub/blob/master/LICENSE).
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| `densenet_169_imagenet` | 12642880 | DenseNet model with 169 layers. Trained on Imagenet 2012 classification task. |
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| `densenet_201_imagenet` | 18321984 | DenseNet model with 201 layers. Trained on Imagenet 2012 classification task. |
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```python
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input_data = np.ones(shape=(8, 224, 224, 3))
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library_name: keras-hub
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## Model Overview
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DenseNet is a convolution network which densely connects each layer to every other layer in a feed-forward fashion. The model was originally evaluated on four object recognition benchmark tasks (CIFAR-10, CIFAR-100, SVHN, and ImageNet). See the model card below for benchmarks, data sources, and intended use cases. This model is supported in both KerasCV and KerasHub. KerasCV will no longer be actively developed, so please try to use KerasHub.
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Weights are released under the [3-Clause BSD License](https://github.com/liuzhuang13/DenseNet/blob/master/LICENSE). Keras model code is released under the [Apache 2 License](https://github.com/keras-team/keras-hub/blob/master/LICENSE).
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| `densenet_169_imagenet` | 12642880 | DenseNet model with 169 layers. Trained on Imagenet 2012 classification task. |
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| `densenet_201_imagenet` | 18321984 | DenseNet model with 201 layers. Trained on Imagenet 2012 classification task. |
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## Example Usage
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```python
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input_data = np.ones(shape=(8, 224, 224, 3))
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