Audience
Developers looking for a solution to upgrade their machine learning pipeline with active learning
About Lightly
Lightly selects the subset of your data with the biggest impact on model accuracy, allowing you to improve your model iteratively by using the best data for retraining. Get the most out of your data by reducing data redundancy, and bias, and focusing on edge cases. Lightly's algorithms can process lots of data within less than 24 hours. Connect Lightly to your existing cloud buckets and process new data automatically. Use our API to automate the whole data selection process. Use state-of-the-art active learning algorithms. Lightly combines active- and self-supervised learning algorithms for data selection. Use a combination of model predictions, embeddings, and metadata to reach your desired data distribution. Improve your model by better understanding your data distribution, bias, and edge cases. Manage data curation runs and keep track of new data for labeling and model training. Easy installation via a Docker image and cloud storage integration, no data leaves your infrastructure.
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"Powerful and Efficient Data Labeling Tool with Smart Automation" Posted 2024-08-17
Pros: Labeling big datasets takes a lot less time thanks to Lightly's automation capabilities like pre-labeling and active learning. It's a great option for optimizing workflow efficiency and simplifying data preparation because of its intuitive interface and smooth integration with machine learning frameworks.
Cons: Some of the most sophisticated features have a steep learning curve. It would be easier for new users to become acquainted with the tool if there were more video tutorials—especially ones with GIFs—in the manual.
Overall: All things considered, Lightly has been a great help with our data labeling procedure. It has freed us up to concentrate more on developing the model instead of getting mired down in the laborious details of preparing the data. Our project turnaround times have significantly improved thanks to the platform, which is dependable.
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