Sandbox for training deep learning networks
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Updated
Aug 24, 2024 - Python
Sandbox for training deep learning networks
Proper implementation of ResNet-s for CIFAR10/100 in pytorch that matches description of the original paper.
The official implementation of [CVPR2022] Decoupled Knowledge Distillation https://arxiv.org/abs/2203.08679 and [ICCV2023] DOT: A Distillation-Oriented Trainer https://openaccess.thecvf.com/content/ICCV2023/papers/Zhao_DOT_A_Distillation-Oriented_Trainer_ICCV_2023_paper.pdf
PyTorch implementation of CNNs for CIFAR benchmark
Unofficial PyTorch Reimplementation of RandAugment.
Multi-Scale Dense Networks for Resource Efficient Image Classification (ICLR 2018 Oral)
Implementation of the mixup training method
Quickly comparing your image classification models with the state-of-the-art models (such as DenseNet, ResNet, ...)
TensorFlow implementation of GoogLeNet and Inception for image classification.
Pretrained GANs + VAEs + classifiers for MNIST/CIFAR in pytorch.
Open Set Recognition
Implementation of our Pattern Recognition paper "DMT: Dynamic Mutual Training for Semi-Supervised Learning"
[TIP 2022] Towards Better Accuracy-efficiency Trade-offs: Divide and Co-training. Plus, an image classification toolbox includes ResNet, Wide-ResNet, ResNeXt, ResNeSt, ResNeXSt, SENet, Shake-Shake, DenseNet, PyramidNet, and EfficientNet.
The official implementation of paper: "Inter-Instance Similarity Modeling for Contrastive Learning"
Training ImageNet / CIFAR models with sota strategies and fancy techniques such as ViT, KD, Rep, etc.
Training Low-bits DNNs with Stochastic Quantization
Python toolkit for speech processing
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