Retrain models for new data at edge devices
View and submit machine learning model evaluations
Calculate survival probability based on passenger details
Create demo spaces for models on Hugging Face
Compare audio representation models using benchmark results
Quantize a model for faster inference
Browse and evaluate language models
Explore and benchmark visual document retrieval models
Display leaderboard of language model evaluations
Display and submit LLM benchmarks
Explore and manage STM32 ML models with the STM32AI Model Zoo dashboard
Calculate memory usage for LLM models
Compare code model performance on benchmarks
EdgeTA is a model benchmarking tool designed to retrain models for new data at edge devices. It enables efficient adaptation of AI models to work effectively in resource-constrained environments like smartphones, IoT devices, or other edge computing platforms. With EdgeTA, users can optimize their models for real-time performance while maintaining accuracy.
What is EdgeTA used for?
EdgeTA is used to retrain AI models on edge devices, enabling them to adapt to new data while optimizing for resource efficiency.
Can EdgeTA work with any type of model?
Yes, EdgeTA is designed to be model-agnostic, supporting various deep learning frameworks and architectures.
How does EdgeTA ensure data privacy?
EdgeTA processes data locally on edge devices, minimizing the need for cloud-based data transmission and enhancing privacy.