Run inference on neural network checkpoints and generate PSNR, SSIM, and LPIPS metrics.
npx skills add https://github.com/Jonah-1/DDSR --skill eval-jonah-1
Please help me install this Agent Skill. Skill: eval Source: https://github.com/Jonah-1/DDSR/tree/main/.claude/skills/eval Command: npx skills add https://github.com/Jonah-1/DDSR --skill eval-jonah-1
This Skill simplifies the process of evaluating trained neural network models by automating inference and metrics analysis, reducing manual effort.
Run evaluation on your trained model checkpoint and review the generated metrics and output images with a simple command.
High-intent search queries and answers about installing and using this skill.
Automating model evaluation requires running inference scripts on your trained neural network checkpoints to generate quantitative performance metrics. This Skill automates that inference process and parses the results into standardized metrics reporting.
Scene reconstruction quality benchmarking commonly uses PSNR, SSIM, and LPIPS scores to quantify performance. This Skill evaluates your model checkpoints and displays these specific metrics in a clear, formatted table for each scene and overall.
Validating trained deep learning model checkpoints involves running inference across multiple scenes using an evaluation script. You can run a single evaluation command to process the checkpoints and generate standardized quantitative results for each scene.
Benchmarking neural network inference results typically requires metric parsing tools to process checkpoints. This Skill includes the necessary scripts and references to automate the inference and metrics parsing, eliminating the need for custom reporting code.
The best way to report PSNR, SSIM, and LPIPS scores during model validation is to automate the inference and formatting process. This Skill generates a clear table displaying these scores for each scene and overall, streamlining deep learning benchmarking workflows.
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