Metadata-Version: 2.1
Name: gluoncv-torch
Version: 0.0.5
Summary: MXNet Gluon CV Toolkit
Home-page: https://github.com/dmlc/gluon-cv
Author: Gluon CV Toolkit Contributors
License: Apache-2.0
Description: [![PyPI](https://img.shields.io/pypi/v/gluoncv-torch.svg)](https://pypi.python.org/pypi/gluoncv-torch)
        [![PyPI Pre-release](https://img.shields.io/badge/pypi--prerelease-v0.0.4-ff69b4.svg)](https://pypi.org/project/gluoncv-torch/#history)
        [![Upload Python Package](https://github.com/zhanghang1989/gluoncv-torch/workflows/Upload%20Python%20Package/badge.svg)](https://github.com/zhanghang1989/gluoncv-torch/actions)
        [![Downloads](http://pepy.tech/badge/gluoncv-torch)](http://pepy.tech/project/gluoncv-torch)
        # PyTorch-Encoding
        # GluonCV-Torch
        
        Load [GluonCV](https://gluon-cv.mxnet.io/) Models in PyTorch.
        Simply `import gluoncvth` to getting better pretrained model than `torchvision`:
        
        ```python
        import gluoncvth as gcv
        model = gcv.models.resnet50(pretrained=True)
        ```
        
        **Installation**:
        
        ```bash
        pip install gluoncv-torch
        ```
        
        
        ## Available Models
        
        ### ImageNet
        
        ImageNet models single-crop error rates, comparing to the `torchvision` models:
        
        |                                 | torchvision     |               | gluoncvth     |             |
        |---------------------------------|-----------------|---------------|---------------|-------------|
        | Model                           | Top-1 error     | Top-5 error   | Top-1 error   | Top-5 error |  
        | [ResNet18](#resnet)             | 30.24           | 10.92         | 29.06         | 10.17       |
        | [ResNet34](#resnet)             | 26.70           | 8.58          | 25.35         | 7.92        |
        | [ResNet50](#resnet)             | 23.85           | 7.13          | 22.33         | 6.18        |
        | [ResNet101](#resnet)            | 22.63           | 6.44          | 20.80         | 5.39        |
        | [ResNet152](#resnet)            | 21.69           | 5.94          | 20.56         | 5.39        |
        | Inception v3                    | 22.55           | 6.44          | 21.33         | 5.61        |
        
        More models available at [GluonCV Image Classification ModelZoo](https://gluon-cv.mxnet.io/model_zoo/classification.html#imagenet)
        
        ### Semantic Segmentation
        
        Results on Pascal VOC dataset:
        
        | Model                   | Base Network  | mIoU       |
        |-------------------------|---------------|------------|
        | [FCN](#fcn)             | ResNet101     | 83.6       |
        | [PSPNet](#pspnet)       | ResNet101     | 85.1       |
        | [DeepLabV3](#deeplabv3) | ResNet101     | 86.2       |
        
        Results on ADE20K dataset:
        
        | Model                   | Base Network  | PixAcc    | mIoU       |
        |-------------------------|---------------|-----------|------------|
        | [FCN](#fcn)             | ResNet101     | 80.6      | 41.6       |
        | [PSPNet](#pspnet)       | ResNet101     | 80.8      | 42.9       |
        | [DeepLabV3](#deeplabv3) | ResNet101     | 81.1      | 44.1       |
        
        **Quick Demo**
        
        ```python
        import torch
        import gluoncvth
        
        # Get the model
        model = gluoncvth.models.get_deeplab_resnet101_ade(pretrained=True)
        model.eval()
        
        # Prepare the image
        url = 'https://github.com/zhanghang1989/image-data/blob/master/encoding/' + \
            'segmentation/ade20k/ADE_val_00001142.jpg?raw=true'
        filename = 'example.jpg'
        img = gluoncvth.utils.load_image(
            gluoncvth.utils.download(url, filename)).unsqueeze(0)
        
        # Make prediction
        output = model.evaluate(img)
        predict = torch.max(output, 1)[1].cpu().numpy() + 1
        
        # Get color pallete for visualization
        mask = gluoncvth.utils.get_mask_pallete(predict, 'ade20k')
        mask.save('output.png')
        ```
        
        ![](./image/demo_deeplab_ade.png)
        
        
        More models available at [GluonCV Semantic Segmentation ModelZoo](https://gluon-cv.mxnet.io/model_zoo/segmentation.html)
        
        ## API Reference
        
        ### ResNet
        
        - `gluoncvth.models.resnet18(pretrained=True)`
        - `gluoncvth.models.resnet34(pretrained=True)`
        - `gluoncvth.models.resnet50(pretrained=True)`
        - `gluoncvth.models.resnet101(pretrained=True)`
        - `gluoncvth.models.resnet152(pretrained=True)`
        
        ### FCN
        
        - `gluoncvth.models.get_fcn_resnet101_voc(pretrained=True)`
        - `gluoncvth.models.get_fcn_resnet101_ade(pretrained=True)`
        
        ### PSPNet
        
        - `gluoncvth.models.get_psp_resnet101_voc(pretrained=True)`
        - `gluoncvth.models.get_psp_resnet101_ade(pretrained=True)`
        
        ### DeepLabV3
        
        - `gluoncvth.models.get_deeplab_resnet101_voc(pretrained=True)`
        - `gluoncvth.models.get_deeplab_resnet101_ade(pretrained=True)`
        
        ### 
        
        ## Why [GluonCV](https://gluon-cv.mxnet.io/)?
        
        **1. State-of-the-art Implementations**
        
        **2. Pretrained Models and Tutorials**
        
        **3. Community Support**
        
        We expect this PyTorch inference API for GluonCV models will be beneficial to the entire computer vision comunity.
        
Platform: UNKNOWN
Description-Content-Type: text/markdown
