flowvision 0.0.53

oneflow vision codebase

vision

Datasets, Transforms and Models specific to Computer Vision

Installation

• First install the nightly version of OneFlow

python3 -m pip install oneflow -f https://staging.oneflow.info/branch/master/cu102

• Then install the latest stable release of flowvision

pip install flowvision==0.0.53

• Or install the nightly release of flowvision

pip install -i https://test.pypi.org/simple/ flowvision==0.0.53

Supported Model

All of the supported models can be found in our model summary page here.

Usage

Quick Start

• list supported model

from flowvision import ModelCreator
ModelCreator.model_table()

• search supported model by wildcard

from flowvision import ModelCreator
ModelCreator.model_table("*vit*", pretrained=True)
ModelCreator.model_table("*vit*", pretrained=False)
ModelCreator.model_table('alexnet')

• create model use ModelCreator

from flowvision import ModelCreator
model = ModelCreator.create_model('alexnet', pretrained=True)

ModelCreator

• Create model in a simple way

from flowvision.models import ModelCreator
model = ModelCreator.create_model('alexnet', pretrained=True)

the pretrained weight will be saved to ./checkpoints

• Supported model table

from flowvision.models import ModelCreator
ModelCreator.model_table()

           Models            
┏━━━━━━━━━━━━━━┳━━━━━━━━━━━━┓
┃ Name         ┃ Pretrained ┃
┡━━━━━━━━━━━━━━╇━━━━━━━━━━━━┩
│ alexnet      │ true       │
│ 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       │
└──────────────┴────────────┘

show all of the supported model in the table manner

• List models with pretrained weights

from flowvision.models import ModelCreator
ModelCreator.model_table(pretrained=True)

           Models            
┏━━━━━━━━━━━━━━┳━━━━━━━━━━━━┓
┃ Name         ┃ Pretrained ┃
┡━━━━━━━━━━━━━━╇━━━━━━━━━━━━┩
│ alexnet      │ true       │
│ vit_b_16_384 │ true       │
│ vit_b_32_384 │ true       │
│ vit_l_16_384 │ true       │
│ vit_l_32_384 │ true       │
└──────────────┴────────────┘

• Search for model by Wildcard

from flowvision.models import ModelCreator
ModelCreator.model_table('vit*')

           Models            
┏━━━━━━━━━━━━━━┳━━━━━━━━━━━━┓
┃ Name         ┃ 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

from flowvision.models import ModelCreator
ModelCreator.model_table('vit*', pretrained=True)

           Models            
┏━━━━━━━━━━━━━━┳━━━━━━━━━━━━┓
┃ Name         ┃ Pretrained ┃
┡━━━━━━━━━━━━━━╇━━━━━━━━━━━━┩
│ vit_b_16_384 │ true       │
│ vit_b_32_384 │ true       │
│ vit_l_16_384 │ true       │
│ vit_l_32_384 │ true       │
└──────────────┴────────────┘

Model Zoo

We have conducted all the tests under the same setting, please refer to the model page here for more 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!