tensorpix/benchmarking-cv-models
Benchmark computer vision ML models in 3 minutes
This project helps machine learning teams evaluate the performance of different GPUs when training or inferencing computer vision models. You input the desired model architecture (like ResNet50 or UNet), specify GPU configurations, and get metrics like images per second and megapixels per second, which helps you choose the most cost-effective hardware for your deep learning workloads. It's designed for ML engineers or researchers making hardware purchasing decisions.
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Use this if you need to benchmark the training or inference speed of popular computer vision models on various GPU setups to inform hardware procurement or optimization.
Not ideal if you need to benchmark a complete MLOps pipeline including data loading, preprocessing, or model saving, as this focuses only on the pure training/inference loop.
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33
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3
Language
Python
License
MIT
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Last pushed
Jun 11, 2024
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