Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
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Pytorch Classification | 1,244 | 3 years ago | 30 | mit | Python | |||||
Classification with PyTorch. | ||||||||||
Unsupervised Classification | 1,217 | 10 months ago | 18 | other | Python | |||||
SCAN: Learning to Classify Images without Labels, incl. SimCLR. [ECCV 2020] | ||||||||||
Torchdistill | 1,171 | 2 | 4 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. | ||||||||||
One Pixel Attack Keras | 1,078 | 3 years ago | 4 | mit | Jupyter Notebook | |||||
Keras implementation of "One pixel attack for fooling deep neural networks" using differential evolution on Cifar10 and ImageNet | ||||||||||
Pytorch_image_classification | 979 | 2 years ago | 1 | mit | Python | |||||
PyTorch implementation of image classification models for CIFAR-10/CIFAR-100/MNIST/FashionMNIST/Kuzushiji-MNIST/ImageNet | ||||||||||
Neural Backed Decision Trees | 445 | 3 years ago | 8 | mit | Python | |||||
Making decision trees competitive with neural networks on CIFAR10, CIFAR100, TinyImagenet200, Imagenet | ||||||||||
Dawn Bench Entries | 235 | 4 years ago | 1 | Python | ||||||
DAWNBench: An End-to-End Deep Learning Benchmark and Competition | ||||||||||
Pyramidnet Pytorch | 215 | 4 years ago | mit | Python | ||||||
A PyTorch implementation for PyramidNets (Deep Pyramidal Residual Networks, https://arxiv.org/abs/1610.02915) | ||||||||||
Mobilenetv3 Pytorch | 197 | 5 years ago | 5 | mit | Python | |||||
Implementing Searching for MobileNetV3 paper using Pytorch | ||||||||||
Aognet | 130 | 5 years ago | 1 | January 10, 2019 | 5 | mit | Python | |||
Code for CVPR 2019 paper: " Learning Deep Compositional Grammatical Architectures for Visual Recognition" |