Metadata-Version: 2.1
Name: torch_soft
Version: 0.1.1
Summary: A high-level deep learning library build on top of PyTorch for classification problems...
Home-page: https://github.com/millermuttu/torch_soft
Author: mallikarjun sajjan
Author-email: flyingmuttus1995@gmail.com
License: MIT
Description: <p align="center">
          <img src="images/logo.png" alt="tensornet" />
          <br />
          <br />
          <img src="https://img.shields.io/badge/version-0.1.1-blue.svg" alt="Version">
          <a href="https://github.com/shan18/TensorNet/blob/master/LICENSE"><img src="https://img.shields.io/apm/l/atomic-design-ui.svg?" alt="MIT License"></a>
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        </p>
        
        torch_soft is a high level implementatio of pytorch for some of claasification implementation with ease...
        
        ## features
        TensorNet currently supports the following features
        - Model architectures
          - ResNet18
          - A custom model called naiveresnet
        - Model utilities
          - Loss functions
            - Cross entropy loss
            - nll_loss
          - Optimizers
            - Stochastic Gradient Descent
          - Regularizers
            - L1 regularization
            - L2 regularization
          - LR Schedulers
            - Step LR
            - Reduce LR on Plateau
            - One Cycle Policy
          - LR Range Test
        - Model training and validation
        - Datasets (data is is returned via data loaders)
          - MNIST
          - CIFAR10
          - TinyImageNet
        - Data Augmentation
          - Resize
          - Padding
          - Random Crop
          - Horizontal Flip
          - Vertical Flip
          - Gaussian Blur
          - Random Rotation
          - CutOut
        - GradCAM and GradCAM++ (Gradient-weighted Class Activation Map)
        - Result Analysis Tools
          - Plotting changes in validation accuracy and loss during model training
          - Displaying correct and incorrect predictions of a trained model
          - Plotting images in a batch for visualization
          - Plotting gradcam outputs
        
        ## How to Use
        
        For examples on how to use torch_soft, refer to the [examples](https://github.com/millermuttu/torch_soft/tree/master/examples) directory.
        
        ## Dependencies
        
        torch_soft has the following third-party dependencies
        - torch
        - torchvision
        - torchsummary
        - tqdm
        - matplotlib
        - albumentations
        - opencv-python
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
