A PyTorch implementation for exploring deep and shallow knowledge distillation (KD) experiments with flexibility

Classification with PyTorch.

A coding-free framework built on PyTorch for reproducible deep learning studies. 🏆25 knowledge distillation methods presented at CVPR, ICLR, ECCV, NeurIPS, ICCV, etc are implemented so far. 🎁 Trained models, training logs and configurations are available for ensuring the reproducibiliy and benchmark.

PyTorch implementation of image classification models for CIFAR-10/CIFAR-100/MNIST/FashionMNIST/Kuzushiji-MNIST/ImageNet

Train to 94% on CIFAR-10 in <6.3 seconds on a single A100. Or ~95.79% in ~110 seconds (or less!)

Play deep learning with CIFAR datasets

Pretrained TorchVision models on CIFAR10 dataset (with weights)

Making decision trees competitive with neural networks on CIFAR10, CIFAR100, TinyImagenet200, Imagenet

A PyTorch implementation of SimCLR based on ICML 2020 paper "A Simple Framework for Contrastive Learning of Visual Representations"

[ICCV 2019] "AutoGAN: Neural Architecture Search for Generative Adversarial Networks" by Xinyu Gong, Shiyu Chang, Yifan Jiang and Zhangyang Wang

3.41% and 17.11% error on CIFAR-10 and CIFAR-100

Pretrained models on CIFAR10/100 in PyTorch

Naszilla is a Python library for neural architecture search (NAS)

Non-negative Positive-Unlabeled (nnPU) and unbiased Positive-Unlabeled (uPU) learning reproductive code on MNIST and CIFAR10

Bottleneck Transformers for Visual Recognition

DAWNBench: An End-to-End Deep Learning Benchmark and Competition

Speech commands recognition with PyTorch | Kaggle 10th place solution in TensorFlow Speech Recognition Challenge

pytorch implement of Lookahead Optimizer

ResNet-20/32/44/56/110 on CIFAR-10 with Caffe

Various CNN models for CIFAR10 with Chainer