Bss_distillation
Knowledge Distillation with Adversarial Samples Supporting Decision Boundary (AAAI 2019)
| Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
|---|---|---|---|---|---|---|---|---|---|---|
| Pytorch Image Models | 29,680 |  | 1 | 346 | 14 days ago | 55 | November 24, 2023 | 96 | apache-2.0 | Python |
| PyTorch image models, scripts, pretrained weights -- ResNet, ResNeXT, EfficientNet, NFNet, Vision Transformer (ViT), MobileNet-V3/V2, RegNet, DPN, CSPNet, Swin Transformer, MaxViT, CoAtNet, ConvNeXt, and more | ||||||||||
| Paddleclas | 5,161 | 3 months ago | 14 | December 05, 2022 | 283 | apache-2.0 | Python | |||
| A treasure chest for visual classification and recognition powered by PaddlePaddle | ||||||||||
| Pytorch Cifar100 | 3,687 | 5 months ago | 53 | Python | ||||||
| Practice on cifar100(ResNet, DenseNet, VGG, GoogleNet, InceptionV3, InceptionV4, Inception-ResNetv2, Xception, Resnet In Resnet, ResNext,ShuffleNet, ShuffleNetv2, MobileNet, MobileNetv2, SqueezeNet, NasNet, Residual Attention Network, SENet, WideResNet) | ||||||||||
| Mmpretrain | 3,114 |  | 10 | 2 months ago | 33 | April 06, 2023 | 215 | apache-2.0 | Python | |
| OpenMMLab Pre-training Toolbox and Benchmark | ||||||||||
| Deepdetect | 2,492 | 4 months ago | 92 | other | C++ | |||||
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| Sandbox for training deep learning networks | ||||||||||
| Efficientnet | 2,048 |  | 2 | 12 | 3 months ago | 9 | September 15, 2020 | 64 | apache-2.0 | Python |
| Implementation of EfficientNet model. Keras and TensorFlow Keras. | ||||||||||
| Tensorflow2.0 Examples | 1,692 | a year ago | 99 | mit | Jupyter Notebook | |||||
| 🙄 Difficult algorithm, Simple code. | ||||||||||
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| Official Implementation of 'Fast AutoAugment' in PyTorch. | ||||||||||
| Torchdistill | 1,171 |  | 2 | 3 months ago | 24 | November 06, 2023 | mit | Python | ||
| 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. | ||||||||||
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