Label data for machine learning models
Analyze data to generate a comprehensive profile report
M-RewardBench Leaderboard
Finance chatbot using vectara-agentic
Check your progress in a Deep RL course
Compare classifier performance on datasets
Profile a dataset and publish the report on Hugging Face
Evaluate LLMs using Kazakh MC tasks
Transfer GitHub repositories to Hugging Face Spaces
Generate detailed data reports
VLMEvalKit Evaluation Results Collection
Analyze and compare datasets, upload reports to Hugging Face
Browse and filter LLM benchmark results
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.