Instructions to use geekyrakshit/DeepLabV3-Plus with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use geekyrakshit/DeepLabV3-Plus with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://geekyrakshit/DeepLabV3-Plus") - Notebooks
- Google Colab
- Kaggle
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metrics:
- accuracy
- mean_iou
tags:
- segmentation
- keras
- tensorflow
- cityscapes
---
# DeepLabV3-Plus
Keras implementation of the DeepLabV3+ model as proposed by the paper [Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation](https://arxiv.org/abs/1802.02611)(ECCV 2018).
The models were trained on the fine-annotations set of the [Cityscapes dataset](cityscapes-dataset.com) for creating presets for [this PR](https://github.com/keras-team/keras-cv/pull/1831) on the `keras-cv` repository.
**Weights & Biases Dashboard:** https://wandb.ai/geekyrakshit/deeplabv3-keras-cv |