File size: 3,186 Bytes
eaed757 ca31b21 f743f22 562d4c0 f743f22 04cac80 f743f22 ca31b21 562d4c0 d140d35 562d4c0 d140d35 562d4c0 f743f22 562d4c0 f743f22 d140d35 f743f22 d140d35 f743f22 562d4c0 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 |
---
library_name: keras-hub
---
## Model Overview
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.
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).
## Links
* [DenseNet Quickstart Notebook: coming soon]()
* [DenseNet API Documentation](https://keras.io/api/keras_hub/models/densnet/)
* [DenseNet Model Card](https://github.com/liuzhuang13/DenseNet)
## Installation
Keras and KerasHub can be installed with:
```
pip install -U -q keras-hub
pip install -U -q keras>=3
```
Jax, TensorFlow, and Torch come preinstalled in Kaggle Notebooks. For instructions on installing them in another environment see the [Keras Getting Started](https://keras.io/getting_started/) page.
## Presets
The following model checkpoints are provided by the Keras team. Weights have been ported from: https://huggingface.co/timm. Full code examples for each are available below.
| Preset name | Parameters | Description |
|-----------------------|------------|---------------|
| `densenet_121_imagenet` | 7037504 | DenseNet model with 121 layers. Trained on Imagenet 2012 classification task. |
| `densenet_169_imagenet` | 12642880 | DenseNet model with 169 layers. Trained on Imagenet 2012 classification task. |
| `densenet_201_imagenet` | 18321984 | DenseNet model with 201 layers. Trained on Imagenet 2012 classification task. |
## Example Usage
```python
input_data = np.ones(shape=(8, 224, 224, 3))
# Pretrained backbone
model = keras_hub.models.DenseNetBackbone.from_preset("densenet_201_imagenet")
model(input_data)
# Randomly initialized backbone with a custom config
model = keras_hub.models.DenseNetBackbone(
stackwise_num_repeats=[6, 12, 24, 16],
)
model(input_data)
# Use densenet for image classification task
model = keras_hub.models.ImageClassifier.from_preset("densenet_201_imagenet")
# User Timm presets directly from HuggingFace
model = keras_hub.models.ImageClassifier.from_preset('hf://timm/densenet121.tv_in1k')
```
## Example Usage with Hugging Face URI
```python
input_data = np.ones(shape=(8, 224, 224, 3))
# Pretrained backbone
model = keras_hub.models.DenseNetBackbone.from_preset("hf://keras/densenet_201_imagenet")
model(input_data)
# Randomly initialized backbone with a custom config
model = keras_hub.models.DenseNetBackbone(
stackwise_num_repeats=[6, 12, 24, 16],
)
model(input_data)
# Use densenet for image classification task
model = keras_hub.models.ImageClassifier.from_preset("hf://keras/densenet_201_imagenet")
# User Timm presets directly from HuggingFace
model = keras_hub.models.ImageClassifier.from_preset('hf://timm/densenet121.tv_in1k')
```
|