Summary of Kerascv and Kerasnlp: Vision and Language Power-ups, by Matthew Watson et al.
KerasCV and KerasNLP: Vision and Language Power-Ups
by Matthew Watson, Divyashree Shivakumar Sreepathihalli, Francois Chollet, Martin Gorner, Kiranbir Sodhia, Ramesh Sampath, Tirth Patel, Haifeng Jin, Neel Kovelamudi, Gabriel Rasskin, Samaneh Saadat, Luke Wood, Chen Qian, Jonathan Bischof, Ian Stenbit, Abheesht Sharma, Anshuman Mishra
First submitted to arxiv on: 30 May 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG); Software Engineering (cs.SE)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A newly developed Keras domain package, comprising KerasCV for Computer Vision and KerasNLP for Natural Language Processing, is presented in this paper. These packages allow for seamless integration with popular deep learning frameworks such as JAX, TensorFlow, or PyTorch. The primary focus is on ease-of-use and performance, achieved through a modular design that provides building blocks for model creation and data preprocessing pipelines at the lower level of abstraction, and pre-trained task models for well-known architectures like Stable Diffusion, YOLOv8, GPT2, BERT, Mistral, CLIP, Gemma, T5, etc. at the higher level. Task models come equipped with built-in preprocessing, pre-trained weights, and can be fine-tuned on raw inputs. To facilitate efficient training, XLA compilation is supported for all models, along with compiled graph-based preprocessing via the TensorFlow data API. The libraries are fully open-source under the Apache 2.0 license and available on GitHub. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Keras domain packages, KerasCV and KerasNLP, make deep learning in Computer Vision and Natural Language Processing more accessible and efficient. These packages work with popular frameworks like JAX, TensorFlow, or PyTorch, allowing users to easily experiment and train models. The libraries provide building blocks for creating models and preprocessing data, as well as pre-trained task models that can be fine-tuned on raw inputs. This makes it easier for researchers and developers to develop and test their ideas. |
Keywords
» Artificial intelligence » Bert » Deep learning » Diffusion » Natural language processing » T5
