Metadata-Version: 2.1
Name: tf2-resnets
Version: 0.2.0
Summary: ResNet variations for TensorFlow.
Home-page: https://github.com/RichardXiao13/TensorFlow-ResNets
Author: Richard Xiao
Author-email: richard.xiao13@gmail.com
License: UNKNOWN
Description: # TensorFlow-ResNets
        
        [![PyPI version](https://badge.fury.io/py/tf2-resnets.svg)](https://badge.fury.io/py/tf2-resnets)  
        
        This repository contains TensorFlow Keras ResNet models.
        Below, you will find the supported variants of ResNet and what weights are supported.   
        
        The codebase takes inspiration from [TensorFlow ResNets](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/keras/applications/resnet.py) 
        and [PyTorch ResNets](https://github.com/pytorch/vision/blob/master/torchvision/models/resnet.py).  
        
        This repository is compatible with TF 2.2.0 even though grouped convolutions are only
        supported in TF Nightly.
        Grouped convolutions will be imported from `convolutions.py` which is the TF Nightly version of convolution layers.
        This allows the use of TPUs since they don't work with TF Nightly.  
        
        These models **will not work without a GPU or TPU** due to the use of grouped convolutions.  
        
        ## Installation
        
        ---
        
        To install, you can use `pip install tf2-resnets`.  
        To use a model, you can do
        ```python
        from tf2_resnets import models
        
        # Weights here are ImageNet.
        # They can also be 'ssl' (semi-supervised)
        # or 'swsl' (semi-weakly supervised)
        # for a selection of models.
        model = models.ResNeXt50(input_shape=(224, 224, 3), weights='imagenet')
        ```
        
        ## Models
        
        ---
        
        | Architecture      | Weights                | Top-1 Acc. | Top-5 Acc. |  
        | ----------------- | :--------------------: | :--------: | :--------: |
        | ResNeXt-50 32x4d  | ImageNet               | 77.6       | 93.7       |
        | ResNeXt-101 32x8d | ImageNet               | 79.3       | 94.5       |
        | Wide ResNet-50 2  | ImageNet               | 78.5       | 94.1       |
        | Wide ResNet-101 2 | ImageNet               | 78.8       | 94.3       |
        | ResNeSt-50        | ImageNet               | 81.0*      | N/A        |
        | ResNeSt-101       | ImageNet               | 82.8*      | N/A        |
        | ResNeSt-200       | ImageNet               | 83.8*      | N/A        |
        | ResNeSt-269       | ImageNet               | 84.5*      | N/A        |
        | ResNeXt-50 32x4d  | semi-supervised        | 80.3       | 95.4       |
        | ResNeXt-101 32x8d | semi-supervised        | 81.7       | 96.1       |
        | ResNeXt-50 32x4d  | semi-weakly supervised | 82.2       | 96.3       |
        | ResNeXt-101 32x8d | semi-weakly supervised | 84.3       | 97.2       |
        
        \* ResNeSt models' Top-1 Accuracies were reported using different crop sizes.
        Crop sizes in order - **224, 256, 320, and 416**.
        
        ## Preprocessing
        
        ---
        
        There are two different preprocessing functions which are meant to be used
        according to the task at hand. The first one, located in `imagenet_preprocessing.py`,
        is meant to be used for reproducing the ImageNet results. The second function
        is located inside `resnet.py`. This function is meant to be used for transfer learning.
        The first function includes an additional resize to 256 by 256 and a central crop to 224 by 224.
        
        ## Original Implementations
        
        ---
        
        The original implementions of these models are listed below.  
        * [ResNeSt](https://github.com/zhanghang1989/ResNeSt) (PyTorch)
        * [ResNeXt](https://github.com/facebookresearch/semi-supervised-ImageNet1K-models) (PyTorch)
        * [Wide ResNet](https://github.com/pytorch/vision) (PyTorch)
        
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
