sagar100rathod/deep-ml
Library for training neural nets in pytorch
This is a framework for machine learning engineers and researchers to efficiently train deep learning models for computer vision. It takes your raw image datasets and a defined neural network architecture as input, then simplifies the process of training, evaluating, and tracking experiments. The output is a trained model ready for deployment or further analysis, along with performance metrics and visualizations of the training process.
Use this if you are a machine learning engineer or researcher regularly working on image classification, semantic segmentation, or image regression tasks and need to streamline your training and experimentation workflows, especially with multiple GPUs.
Not ideal if you are a beginner looking for a no-code solution or primarily work with non-image data types like text or time series, as this is focused on computer vision.
Stars
8
Forks
1
Language
Jupyter Notebook
License
MIT
Category
Last pushed
Mar 26, 2026
Commits (30d)
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