Summary of Autommlab: Automatically Generating Deployable Models From Language Instructions For Computer Vision Tasks, by Zekang Yang et al.
AutoMMLab: Automatically Generating Deployable Models from Language Instructions for Computer Vision Tasks
by Zekang Yang, Wang Zeng, Sheng Jin, Chen Qian, Ping Luo, Wentao Liu
First submitted to arxiv on: 23 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed AutoML system automates the entire end-to-end model production workflow for computer vision, enabling non-expert individuals to build task-specific models via a user-friendly language interface. The novel request-to-model task involves understanding natural language requests and executing the workflow to output production-ready models. To facilitate development and evaluation, a new experimental platform called AutoMMLab and a benchmark called LAMP are developed for studying key components in the end-to-end pipeline. A novel LLM-based HPO algorithm, HPO-LLaMA, is proposed to achieve significant improvement of HPO efficiency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes an automated machine learning (AutoML) system that helps non-expert users build computer vision models by understanding natural language requests and executing the workflow. This system includes a new experimental platform and benchmark for studying key components in the pipeline. The authors also introduce a novel algorithm to improve hyperparameter optimization efficiency. |
Keywords
* Artificial intelligence * Hyperparameter * Llama * Machine learning * Optimization
