{"categories":[{"label":"Software Development","url":"https://skillfed.io/packages/category/software-development/7"},{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/3"}],"enrichment":{"capability":"KerasHub provides Keras 3 implementations of pretrained model architectures for text, image, and audio tasks, with checkpoints available on Kaggle Models and support for JAX, TensorFlow, and PyTorch backends.","skillfed_tags":["transfer-learning","multi-backend","pretrained-models"],"use_cases":["Load a pretrained ResNet or similar image classifier and fine-tune it on a custom dataset without rewriting for different backends.","Fine-tune a BERT or other text model on domain-specific tasks like sentiment analysis or named entity recognition.","Experiment with the same model architecture across JAX, TensorFlow, and PyTorch to compare performance and training efficiency.","Build production inference pipelines using pretrained audio models (e.g., Whisper) for speech recognition or classification.","Apply transfer learning and PEFT techniques to train large models on limited GPU memory."],"what_it_does":"KerasHub is a library of pretrained neural network models built on Keras 3, designed to work seamlessly across JAX, TensorFlow, and PyTorch from a single model definition. It provides ready-to-use implementations of popular architectures (ResNet, BERT, and others) paired with pretrained checkpoints hosted on Kaggle Models, enabling developers to quickly load and fine-tune models for text, image, and audio tasks without rewriting code for different backends.\n\nThe library extends the core Keras API through Layer and Model implementations, so if you know Keras you can use KerasHub immediately. Models support fine-tuning on GPUs and TPUs out of the box, with built-in PEFT techniques for single-accelerator training and support for model and data parallel training at scale. Installation always includes TensorFlow for the tf.data preprocessing API, though training itself can happen on any configured backend.","worth_installing":"Yes, if you need pretrained models across multiple backends. KerasHub is actively maintained, has low install friction, and Apache-2.0 licensing is permissive. However, the library is in pre-release (0.31.0) with no backwards compatibility guarantees, so APIs may break. The mandatory TensorFlow dependency for tf.data preprocessing adds overhead even when training on JAX or PyTorch. Best suited for research and experimentation; production use should account for API instability."},"id":"keras-hub","links":{"html":"https://skillfed.io/packages/keras-hub","md":"https://skillfed.io/packages/keras-hub.md","pypi":"https://pypi.org/project/keras-hub/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-08-08","license_spdx":"Apache-2.0","license_treatment":"permissive","name":"keras-hub","python_support":"supports_current","summary":"Pretrained models for Keras."},"popularity":{"monthly_downloads":629106,"position":5669,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.31.0"}