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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 ...
📚 Read more at Keras Developer guides🔎 Find similar documents
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...
📚 Read more at Keras Developer guides🔎 Find similar documents
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 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...
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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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Segment Anything in KerasCV!
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...
📚 Read more at Keras Developer guides🔎 Find similar documents
Distributed training with Keras 3
Introduction The Keras distribution API is a new interface designed to facilitate distributed deep learning across a variety of backends like JAX, TensorFlow and PyTorch. This powerful API introduces ...
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