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Orbax Checkpointing in Keras
Introduction Orbax is the default checkpointing library recommended for JAX ecosystem users. It is a high-level checkpointing library which provides functionality for both checkpoint management and co...
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How to use Keras with NNX backend
Workflow 1: The Classic Keras Experience (model.fit) Create a Keras Model Create Dummy Data Compile and Fit Verify a change As you can see, your existing Keras code works out-of-the-box, giving you a ...
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Stable Diffusion 3 in KerasHub!
Overview Stable Diffusion 3 is a powerful, open-source latent diffusion model (LDM) designed to generate high-quality novel images based on text prompts. Released by Stability AI , it was pre-trained ...
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Stable Diffusion 3 in KerasHub!
Overview Stable Diffusion 3 is a powerful, open-source latent diffusion model (LDM) designed to generate high-quality novel images based on text prompts. Released by Stability AI , it was pre-trained ...
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Semantic Segmentation with KerasHub
Background Semantic segmentation is a type of computer vision task that involves assigning a class label such as "person", "bike", or "background" to each individual pixel of an image, effectively div...
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Semantic Segmentation with KerasHub
Background Semantic segmentation is a type of computer vision task that involves assigning a class label such as "person", "bike", or "background" to each individual pixel of an image, effectively div...
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Getting Started with KerasHub
Installation and Setup To begin, let's install keras-hub. The library is available on PyPI, so we can simply install it with pip. Keras 3 was built to work on top of TensorFlow, Jax, and Torch backend...
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Classification with KerasHub
Inference with a pretrained classifier Let's get started with the simplest KerasHub API: a pretrained classifier. In this example, we will construct a classifier that was pretrained on the ImageNet da...
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Segment Anything in KerasHub!
Overview The Segment Anything Model (SAM) produces high quality object masks from input prompts such as points or boxes, and it can be used to generate masks for all objects in an image. It has been t...
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Customizing what happens in
Introduction When you're doing supervised learning, you can use fit() and everything works smoothly. When you need to take control of every little detail, you can write your own training loop entirely...
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Uploading Models with KerasNLP
Load Model If you want to build a Causal LM based on a base model, simply call keras_nlp.models.CausalLM.from_preset and pass a built-in preset identifier. Fine-tune Model After loading the model, you...
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Uploading Models with KerasHub
Load Model If you want to build a Causal LM based on a base model, simply call keras_hub.models.CausalLM.from_preset and pass a built-in preset identifier. Fine-tune Model After loading the model, you...
📚 Read more at Keras Developer guides🔎 Find similar documents