KerasCV and KerasNLP: Vision and Language Power-Ups
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Abstract (arXiv)
We present the Keras domain packages KerasCV and KerasNLP, extensions of the Keras API for Computer Vision and Natural Language Processing workflows, capable of running on either JAX, TensorFlow, or PyTorch. These domain packages are designed to enable fast experimentation, with a focus on ease-of-use and performance. We adopt a modular, layered design: at the library's lowest level of abstraction, we provide building blocks for creating models and data preprocessing pipelines, and at the library's highest level of abstraction, we provide pretrained ``task" models for popular architectures such as Stable Diffusion, YOLOv8, GPT2, BERT, Mistral, CLIP, Gemma, T5, etc. Task models have built-in preprocessing, pretrained weights, and can be fine-tuned on raw inputs. To enable efficient training, we support XLA compilation for all models, and run all preprocessing via a compiled graph of TensorFlow operations using the tf.data API. The libraries are fully open-source (Apache 2.0 license) and available on GitHub.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 4 of its 8 references that are in the map. First posted on arXiv 30 May 2024.
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References · 4
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
- 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
- 2023Segment Anythingcited by 8,234
- 2021tf.data: A Machine Learning Data Processing Frameworkcited by 41
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- 2023ECoFLaP: Efficient Coarse-to-Fine Layer-Wise Pruning for Vision-Language Models
- 2026DataComp-VLM: Improved Open Datasets for Vision-Language Models
- 2024Pruning All-Rounder: Rethinking and Improving Inference Efficiency for Large Vision Language Models
- 2025Balanced Token Pruning: Accelerating Vision Language Models Beyond Local Optimization
- 2023ELIP: Efficient Discriminative Language-Image Pre-training with Fewer Vision Tokens
- 2025EffiVLM-BENCH: A Comprehensive Benchmark for Evaluating Training-Free Acceleration in Large Vision-Language Models
- 2026Underrepresented in Foundation Model Pretraining Data? A One-Shot Probe
- 2026Molmo2: Open Weights and Data for Vision-Language Models with Video Understanding and Grounding