Sandbox for training deep learning networks
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Updated
May 7, 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.
PyTorch implementation of CNNs for CIFAR benchmark
TensorFlow implementation of GoogLeNet and Inception for image classification.
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
Multi-Scale Dense Networks for Resource Efficient Image Classification (ICLR 2018 Oral)
Unofficial PyTorch Reimplementation of RandAugment.
Implementation of the mixup training method
Quickly comparing your image classification models with the state-of-the-art models (such as DenseNet, ResNet, ...)
Pretrained GANs + VAEs + classifiers for MNIST/CIFAR in pytorch.
Open Set Recognition
[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.
Wide Residual Networks implemented in TensorLayer and TensorFlow.
Training Low-bits DNNs with Stochastic Quantization
Python toolkit for speech processing
Implementation of our Pattern Recognition paper "DMT: Dynamic Mutual Training for Semi-Supervised Learning"
CIFAR 10 image dataset
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