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Semisup_scarse

PyTorch implementation of Semi-Supervised Learning with Scarce Annotations https://arxiv.org/pdf/1905.08845.pdf

Categories > Machine Learning > Cifar10

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4 years ago

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Machine Learning > Cifar10

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Pytorch Cifar ⭐ 5,131

95.47% on CIFAR10 with PyTorch

most recent commit a year ago

Pytorch Playground ⭐ 2,366

Base pretrained models and datasets in pytorch (MNIST, SVHN, CIFAR10, CIFAR100, STL10, AlexNet, VGG16, VGG19, ResNet, Inception, SqueezeNet)

most recent commit 2 years ago

Knowledge Distillation Pytorch ⭐ 1,780

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

most recent commit a year ago

Pytorch Classification ⭐ 1,244

Classification with PyTorch.

most recent commit 3 years ago

Unsupervised Classification ⭐ 1,217

SCAN: Learning to Classify Images without Labels, incl. SimCLR. [ECCV 2020]

most recent commit a year ago

Torchdistill ⭐ 1,171

A coding-free framework built on PyTorch for reproducible deep learning studies. 🏆22 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.

dependent packages 2total releases 24most recent commit 6 months ago

pypi torchdistill} Downloads

Hlb Cifar10 ⭐ 1,112

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

most recent commit 8 months ago

One Pixel Attack Keras ⭐ 1,078

Keras implementation of "One pixel attack for fooling deep neural networks" using differential evolution on Cifar10 and ImageNet

most recent commit 4 years ago

Pytorch_image_classification ⭐ 979

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

most recent commit 3 years ago

Resnet In Tensorflow ⭐ 825

Re-implement Kaiming He's deep residual networks in tensorflow. Can be trained with cifar10.

most recent commit 5 years ago

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