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Project: scratchai (GitHub Link)

• scratchai-master
  • .github
    • FUNDING.yml
  • .circleci
    • config.yml
  • LICENSE
  • scratchai
    • init.py
    • datasets
      • labels.py
      • __init__.py
      • README.md
    • learners
      • metrics.py
      • clflearner.py
      • trainops
        • train.py
      • __init__.py
      • quicktrain.py
      • learner.py
    • imgutils.py
    • attacks
      • attacks
        • deepfool.py
        • fast_gradient_method.py
        • semantic.py
        • pgd.py
        • noise.py
        • saliency_map_method.py
        • __init__.py
      • __init__.py
      • README.md
      • utils.py
    • pretrained
      • urls.py
      • __init__.py
      • README.md
    • one_call.py
    • _config.py
    • nets
      • clf
        • vgg.py
        • alexnet.py
        • lenet.py
        • resnet.py
        • googlenet.py
        • __init__.py
      • gans
        • cycle_gan.py
        • __init__.py
      • common.py
      • __init__.py
      • utils.py
      • style_transfer
        • image_transformation_net.py
        • __init__.py
      • seg
        • unet.py
        • __init__.py
        • enet.py
    • __init__.py
    • utils.py
    • DataLoader
      • DataLoader.py
      • color_code.py
      • SegLoader.py
      • get_datasets
        • get_fashion_mnist_data.sh
        • get_camvid.sh
        • camvid_code.txt
      • __init__.py
      • DatasetLoader.py
      • ImageLoader.py
  • CONTRIBUTING.md
  • setup.py
  • tutorials
    • README.md
  • README.md
  • tests
    • test_utils.py
    • test_loaders.py
    • test_nets.py
    • __init__.py
    • test_one_calls.py
    • test_metrics.py
    • test_attacks.py
    • test_learners.py
  • requirements.txt
  • .gitignore

scratchai

Builds

Documentation

Table of Contents:

1. Classification

Model	Paper	Implementation	Configurations
Lenet	http://yann.lecun.com/exdb/publis/pdf/lecun-01a.pdf	Implementation
Alexnet	https://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf	Implementation
VGG	https://arxiv.org/pdf/1409.1556.pdf	Implementation	VGG11, VGG11_BN, VGG13, VGG13_BN, VGG16_BN, VGG19, VGG19_BN, VGG_Dilated (For all the normal configurations)
Resnet	https://arxiv.org/abs/1512.03385	Implementation	Resnet18, Resnet34, Resnet50, Resnet101, Resnet150, Resnet_dilated (For all the previous resnets)
GoogLeNet	https://www.cs.unc.edu/~wliu/papers/GoogLeNet.pdf	Implementation
Resnext	https://arxiv.org/abs/1611.05431	NA

2. Segmentation

Model	Paper	Implementation
UNet	https://arxiv.org/abs/1505.04597	Implementation [Not checked]
ENet	https://arxiv.org/abs/1606.02147	Implementation [Not checked]

3. Generative Adversarial Networks

Model	Paper	Implementation
DCGAN	https://arxiv.org/abs/1511.06434	NA
CycleGAN	https://arxiv.org/abs/1703.10593	Implementation [Not checked]

4. Style Transfer

Model	Paper	Implementation
Image Transformation Network Justin et al.	Perceptual Losses Paper Supplementary Material	Implementation

5. Attacks

Attacks	Paper	Implementation
Noise	NA	Implementation
Semantic	https://arxiv.org/abs/1703.06857	Implementation
Saliency Map Method	https://arxiv.org/pdf/1511.07528.pdf	Ongoing
Fast Gradient Method	https://arxiv.org/abs/1412.6572	Implementation
Projected Gradient Descent	https://arxiv.org/pdf/1607.02533.pdf https://arxiv.org/pdf/1706.06083.pdf	Implementation
DeepFool	https://arxiv.org/abs/1511.04599 pdf	Implementation

Tutorials

Tutorials on how to get the most out of scratchai can be found here: https://github.com/iArunava/scratchai/tree/master/tutorials

These are ongoing list of tutorials and scratchai is looking for more and more contributions. If you are willing to contribute please take a look at the CONTRIBUTING.md / open a issue.

License

The code under this repository is distributed under MIT License. Feel free to use it in your own work with proper citations to this repository.