Metadata-Version: 2.1
Name: flowvision
Version: 0.0.56
Summary: oneflow vision codebase
Home-page: https://github.com/Oneflow-Inc/vision
Author: flow vision contributors
Author-email: rentianhe@oneflow.org
License: BSD
Description: # flowvision
        The flowvision package consists of popular datasets, model architectures, and common image transformations for computer vision.
        
        
        ## Installation
        First install OneFlow, please refer to [install-oneflow](https://github.com/Oneflow-Inc/oneflow#install-oneflow) for more details.
        
        Then install the latest stable release of `flowvision`
        ```bash
        pip install flowvision==0.0.56
        ```
        
        ## Documentation
        You can find the API documentation on the website: https://flowvision.readthedocs.io/en/latest/index.html
        
        ## Model Zoo
        All of the supported models can be found in our model summary page [here](MODEL_SUMMARY.md).
        
        We have conducted all the tests under the same setting, please refer to the model page [here](MODEL_ZOO.md) for more details.
        
        ## Quick Start
        <details>
        <summary> <b> Quick Start </b> </summary>
        
        - list supported model
        ```python
        from flowvision.models import ModelCreator
        supported_model_table = ModelCreator.model_table()
        print(supported_model_table)
        ```
        
        - search supported model by wildcard
        ```python
        from flowvision.models import ModelCreator
        pretrained_vit_model = ModelCreator.model_table("*vit*", pretrained=True)
        supported_vit_model = ModelCreator.model_table("*vit*", pretrained=False)
        supported_alexnet_model = ModelCreator.model_table('alexnet')
        
        # check the model table
        print(pretrained_vit_model)
        print(supported_vit_model)
        print(supported_alexnet_model)
        ```
        
        - create model use `ModelCreator`
        ```python
        from flowvision.models import ModelCreator
        model = ModelCreator.create_model('alexnet', pretrained=True)
        ```
        
        </details>
        
        <details>
        <summary> <b> ModelCreator </b> </summary>
        
        - Create model in a simple way
        ```python
        from flowvision.models import ModelCreator
        model = ModelCreator.create_model('alexnet', pretrained=True)
        ```
        the pretrained weight will be saved to `./checkpoints`
        
        - Supported model table
        ```python
        from flowvision.models import ModelCreator
        supported_model_table = ModelCreator.model_table()
        print(supported_model_table)
        ```
        ```
        ╒════════════════════════════════════════════╤══════════════╕
        │ Supported Models                           │ Pretrained   │
        ╞════════════════════════════════════════════╪══════════════╡
        │ alexnet                                    │ true         │
        ├────────────────────────────────────────────┼──────────────┤
        │ convmixer_1024_20                          │ true         │
        ├────────────────────────────────────────────┼──────────────┤
        │ convmixer_1536_20                          │ true         │
        ├────────────────────────────────────────────┼──────────────┤
        │ convmixer_768_32_relu                      │ true         │
        ├────────────────────────────────────────────┼──────────────┤
        │ shufflenet_v2_x0_5                         │ true         │
        ├────────────────────────────────────────────┼──────────────┤
        │ shufflenet_v2_x1_0                         │ true         │
        ├────────────────────────────────────────────┼──────────────┤
        │ shufflenet_v2_x1_5                         │ false        │
        ├────────────────────────────────────────────┼──────────────┤
        │ shufflenet_v2_x2_0                         │ false        │
        ├────────────────────────────────────────────┼──────────────┤
        │                    ...                     │     ...      │
        ├────────────────────────────────────────────┼──────────────┤
        │ wide_resnet101_2                           │ true         │
        ├────────────────────────────────────────────┼──────────────┤
        │ wide_resnet50_2                            │ true         │
        ╘════════════════════════════════════════════╧══════════════╛
        ```
        show all of the supported model in the table manner
        
        - Check the table of the models with pretrained weights.
        ```python
        from flowvision.models import ModelCreator
        pretrained_model_table = ModelCreator.model_table(pretrained=True)
        print(pretrained_model_table)
        ```
        ```
        ╒════════════════════════════════════════════╤══════════════╕
        │ Supported Models                           │ Pretrained   │
        ╞════════════════════════════════════════════╪══════════════╡
        │ alexnet                                    │ true         │
        ├────────────────────────────────────────────┼──────────────┤
        │ convmixer_1024_20                          │ true         │
        ├────────────────────────────────────────────┼──────────────┤
        │ convmixer_1536_20                          │ true         │
        ├────────────────────────────────────────────┼──────────────┤
        │ convmixer_768_32_relu                      │ true         │
        ├────────────────────────────────────────────┼──────────────┤
        │ crossformer_base_patch4_group7_224         │ true         │
        ├────────────────────────────────────────────┼──────────────┤
        │ crossformer_large_patch4_group7_224        │ true         │
        ├────────────────────────────────────────────┼──────────────┤
        │ crossformer_small_patch4_group7_224        │ true         │
        ├────────────────────────────────────────────┼──────────────┤
        │ crossformer_tiny_patch4_group7_224         │ true         │
        ├────────────────────────────────────────────┼──────────────┤
        │                    ...                     │     ...      │
        ├────────────────────────────────────────────┼──────────────┤
        │ wide_resnet101_2                           │ true         │
        ├────────────────────────────────────────────┼──────────────┤
        │ wide_resnet50_2                            │ true         │
        ╘════════════════════════════════════════════╧══════════════╛
        ```
        - Search for model by Wildcard.
        ```python
        from flowvision.models import ModelCreator
        supported_vit_model = ModelCreator.model_table('vit*')
        print(supported_vit_model)
        ```
        ```
        ╒════════════════════╤══════════════╕
        │ Supported Models   │ Pretrained   │
        ╞════════════════════╪══════════════╡
        │ vit_b_16_224       │ false        │
        ├────────────────────┼──────────────┤
        │ vit_b_16_384       │ true         │
        ├────────────────────┼──────────────┤
        │ vit_b_32_224       │ false        │
        ├────────────────────┼──────────────┤
        │ vit_b_32_384       │ true         │
        ├────────────────────┼──────────────┤
        │ vit_l_16_384       │ true         │
        ├────────────────────┼──────────────┤
        │ vit_l_32_384       │ true         │
        ╘════════════════════╧══════════════╛
        ```
        - Search for model with pretrained weights by Wildcard.
        ```python
        from flowvision.models import ModelCreator
        ModelCreator.model_table('vit*', pretrained=True)
        ```
        ```
        ╒════════════════════╤══════════════╕
        │ Supported Models   │ Pretrained   │
        ╞════════════════════╪══════════════╡
        │ vit_b_16_384       │ true         │
        ├────────────────────┼──────────────┤
        │ vit_b_32_384       │ true         │
        ├────────────────────┼──────────────┤
        │ vit_l_16_384       │ true         │
        ├────────────────────┼──────────────┤
        │ vit_l_32_384       │ true         │
        ╘════════════════════╧══════════════╛
        ```
        
        </details>
        
        ## Disclaimer on Datasets
        This is a utility library that downloads and prepares public datasets. We do not host or distribute these datasets, vouch for their quality or fairness, or claim that you have license to use the dataset. It is your responsibility to determine whether you have permission to use the dataset under the dataset's license.
        
        If you're a dataset owner and wish to update any part of it (description, citation, etc.), or do not want your dataset to be included in this library, please get in touch through a GitHub issue. Thanks for your contribution to the ML community!
        
Keywords: computer vision
Platform: any
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Intended Audience :: Developers
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
