This is the Linux app named PyCls whose latest release can be downloaded as Sweepcodeforstudyingmodelpopulationstatssourcecode.tar.gz. It can be run online in the free hosting provider OnWorks for workstations.
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PyCls
DESCRIPTION
pycls is a focused PyTorch codebase for image classification research that emphasizes reproducibility and strong, transparent baselines. It popularized families like RegNet and supports classic architectures (ResNet, ResNeXt) with clean implementations and consistent training recipes. The repository includes highly tuned schedules, augmentations, and regularization settings that make it straightforward to match reported accuracy without guesswork. Distributed training and mixed precision are first-class, enabling fast experiments on multi-GPU setups with simple, declarative configs. Model definitions are concise and modular, making it easy to prototype new blocks or swap backbones while keeping the rest of the pipeline unchanged. Pretrained weights and evaluation scripts cover common datasets, and the logging/metric stack is designed for quick comparison across runs. Practitioners use pycls both as a baseline factory and as a scaffold for new classification backbones.
Mga tampok
- Reference implementations of ResNet/ResNeXt/RegNet families
- Reproducible training recipes with tuned schedules and augmentations
- Distributed and mixed-precision training out of the box
- Declarative configuration system and clean data pipelines
- Pretrained checkpoints and standardized evaluation scripts
- Minimal, modular model code for rapid architectural iteration
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This is an application that can also be fetched from https://sourceforge.net/projects/pycls.mirror/. It has been hosted in OnWorks in order to be run online in an easiest way from one of our free Operative Systems.
