flowvision 0.0.55

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

Please refer to install-oneflow for the detail of OneFlow installation.

• Then install the latest stable release of flowvision

pip install flowvision==0.0.55

• Or install the nightly release of flowvision

pip install -i https://test.pypi.org/simple/ flowvision==0.0.55

Documentation

You can find the API documentation on the website: https://flowvision.readthedocs.io/en/latest/index.html

Supported Model

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

Usage

Quick Start

• list supported model

from flowvision.models import ModelCreator
supported_model_table = ModelCreator.model_table()
print(supported_model_table)

• search supported model by wildcard

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

from flowvision.models 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
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.

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.

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.

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         │
╘════════════════════╧══════════════╛

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!