---
library_name: keras-hub
license: apache-2.0
tags:
- image-segmentation
---
## Model Overview
DeepLabv3+ model is developed by Google for semantic segmentation. This guide demonstrates how to finetune and use DeepLabv3+ model for image semantic segmentaion with KerasCV. Its architecture that combines atrous convolutions, contextual information aggregation, and powerful backbones to achieve accurate and detailed semantic segmentation. The DeepLabv3+ model has been shown to achieve state-of-the-art results on a variety of image segmentation benchmarks. 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 [Apache 2 License](https://apache.org/licenses/LICENSE-2.0). Keras model code is released under the [Apache 2 License](https://github.com/keras-team/keras-hub/blob/master/LICENSE).

## Links

* [DeepLabV3Plus Quickstart Notebook](https://www.kaggle.com/code/prasadsachin/deeplabv3plus-quickstart)
* [DeepLabV3Plus Finetune Notebook](https://www.kaggle.com/code/prasadsachin/deeplabv3plus-finetune-notebook/)
* [DeepLabV3Plus API Documentation](https://keras.io/api/keras_hub/models/deeplab_v3/)


## Installation

Keras and KerasHub can be installed with:

```
pip install -U -q keras-hub
pip install -U -q keras
```

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. Full code examples for each are available below.

| Preset name                        | Parameters | Description                                                                                                                                                                                                                                                                                               |
|------------------------------------|------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| deeplab_v3_plus_resnet50_pascalvoc | 39.1M      | DeeplabV3Plus with a ResNet50 v2 backbone. Trained on PascalVOC 2012 Semantic segmentation task, which consists of 20 classes and one background class. This model achieves a final categorical accuracy of 89.34% and mIoU of 0.6391 on evaluation dataset. This preset is only comptabile with Keras 3. |

## Model card
https://arxiv.org/abs/1802.02611

## Example Usage
Load DeepLabv3+ presets a extension of DeepLabv3 by adding a simple yet
effective decoder module to refine the segmentation results especially
along object boundaries.

```python
images = np.ones(shape=(1, 96, 96, 3))
labels = np.zeros(shape=(1, 96, 96, 2))
segmenter = keras_hub.models.DeepLabV3ImageSegmenter.from_preset(
    "deeplab_v3_plus_resnet50_pascalvoc",
)
segmenter.predict(images)
```
Specify `num_classes` to load randomly initialized segmentation head.

```python
segmenter = keras_hub.models.DeepLabV3ImageSegmenter.from_preset(
    "deeplab_v3_plus_resnet50_pascalvoc",
    num_classes=2,
)
segmenter.preprocessor.image_size = (96, 96)
segmenter.fit(images, labels, epochs=3)
segmenter.predict(images)  # Trained 2 class segmentation.
```

## Example Usage with Hugging Face URI

Load DeepLabv3+ presets a extension of DeepLabv3 by adding a simple yet
effective decoder module to refine the segmentation results especially
along object boundaries.

```python
images = np.ones(shape=(1, 96, 96, 3))
labels = np.zeros(shape=(1, 96, 96, 2))
segmenter = keras_hub.models.DeepLabV3ImageSegmenter.from_preset(
    "hf://keras/deeplab_v3_plus_resnet50_pascalvoc",
)
segmenter.predict(images)
```
Specify `num_classes` to load randomly initialized segmentation head.

```python
segmenter = keras_hub.models.DeepLabV3ImageSegmenter.from_preset(
    "hf://keras/deeplab_v3_plus_resnet50_pascalvoc",
    num_classes=2,
)
segmenter.preprocessor.image_size = (96, 96)
segmenter.fit(images, labels, epochs=3)
segmenter.predict(images)  # Trained 2 class segmentation.
```