oneflow vision codebase
Project description
vision
Datasets, Transforms and Models specific to Computer Vision
Installation
- install
oneflow0.5.0+cu102
and other requirements
python3 -m pip install -f https://release.oneflow.info oneflow==0.5.0+cu102
pip install rich
- install latest version of
flowvision
pip install flowvision==0.0.3
Supported Models
- AlexNet
- VGG
- DenseNet
- GoogleNet
- InceptionV3
- MnasNet
- MobileNetV2
- MobileNetV3
- ResNet
- ShuffleNetV2
- SqueezeNet
- Swin-Transformer
- CrossFormer
- ViT
- ConvMixer
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 did all our tests under the same setting, please check the model page here for more details.
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