Cifar100 Python Packages

torchdistill

A coding-free framework built on PyTorch for reproducible deep learning studies. PyTorch Ecosystem. 🏆26 knowledge distillation methods presented at TPAMI, CVPR, ICLR, ECCV, NeurIPS, ICCV, AAAI, etc are implemented so far. 🎁 Trained models, training logs and configurations are available for ensuring the reproducibiliy and benchmark.

bsconv

Reference implementation for Blueprint Separable Convolutions (CVPR 2020)

adaptive-sparse-training

Adaptive Sparse Training (AST): 92.1% ImageNet-100 accuracy with 61% energy savings and zero degradation. Production-ready implementations for energy-efficient deep learning with ResNet50 and modern architectures.

nbdt

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

aognet

Code for CVPR 2019 paper: " Learning Deep Compositional Grammatical Architectures for Visual Recognition"