Label data for machine learning models
Cluster data points using KMeans
Browse and submit evaluation results for AI benchmarks
Predict linear relationships between numbers
Calculate and explore ecological data with ECOLOGITS
Explore tradeoffs between privacy and fairness in machine learning models
Profile a dataset and publish the report on Hugging Face
Generate plots for GP and PFN posterior approximations
Monitor application health
Need to analyze data? Let a Llama-3.1 agent do it for you!
Generate a data profile report
Calculate VRAM requirements for running large language models
Generate benchmark plots for text generation models
Mikeyandfriends-PixelWave FLUX.1-dev 03 is a data visualization tool designed to assist in labeling data for machine learning models. It provides an intuitive interface to help users organize, visualize, and annotate datasets, making it easier to prepare data for training machine learning models. This development version (03) includes experimental features aimed at improving efficiency and user experience.
Example: For a dataset of images, you can visualize the images, add labels, and export the labeled dataset for training a computer vision model.
What datasets is Mikeyandfriends-PixelWave FLUX.1-dev 03 compatible with?
The tool supports a wide range of datasets, including images, text, and numerical data. It is particularly optimized for image-based datasets.
Can I customize the labeling tools?
Yes, customizable labeling tools are available, allowing you to create workflows tailored to your specific dataset or project requirements.
What file formats does the tool support for exporting labeled data?
Mikeyandfriends-PixelWave FLUX.1-dev 03 supports common formats such as CSV, JSON, and COCO for seamless integration with machine learning pipelines.