This repository is <code>timm</code> (PyTorch Image Models), a high-quality library providing a vast collection of image classification model architectures (100+), pretrained weights, training utilities, and data pipelines. It is not an application but a reusable library/framework, designed for research and production use. The codebase is well-structured with clear separation of concerns: <code>timm/layers</code> for building blocks (activations, normalizations, attentions, pooling), <code>timm/models</code> for model definitions with a consistent registry pattern, <code>timm/data</code> for data loading and augmentation (including AutoAugment, RandAugment, Mixup, CutMix), <code>timm/optim</code> for 30+ optimizers, <code>timm/scheduler</code> for learning rate schedulers, and <code>timm/task</code> for training task abstractions. The library supports distributed training, mixed precision, ONNX export, Hugging Face Hub integration, and dynamic input sizes (NaFlex). It is actively maintained by Hugging Face and the community, with frequent releases and a large number of pretrained models.
This repository provides the official PyTorch implementation of MAR (Masked Autoregressive with Diffusion Loss), a research method for class-conditional image generation on ImageNet 256x256. The core contribution is an autoregressive model that operates on continuous-valued latent tokens (via a pre-trained VAE) and uses a diffusion loss instead of vector quantization. The codebase includes model definitions (MAR encoder-decoder transformer, DiffLoss MLP), training and evaluation scripts with PyTorch DDP, a VAE latent caching pipeline, and pre-trained checkpoints for base/large/huge variants. While primarily a research prototype, it contains reusable components such as the DiffLoss module, diffusion utilities, and a clean data caching pattern. The Gradio app and Colab notebook make the model accessible for interactive use.
This repository contains the reference implementation of AugMix, a data augmentation technique for improving robustness and uncertainty calibration in image classification models. It provides training scripts for CIFAR-10/100 and ImageNet using PyTorch, along with evaluation on their corrupted versions (CIFAR-C, ImageNet-C). The core augmentation logic is implemented in NumPy and PIL, and the training code applies a Jensen-Shannon Divergence consistency loss between augmented and clean images. The codebase is research-oriented, with scripts that are well-structured but heavily tied to the specific experimental setup of the paper. Dependencies are pinned but severely outdated (torch==1.2.0), making reproduction on modern systems challenging without adaptation.