Getting Started¶

Installation¶

To install latest stable release of flowvision:

pip install flowvision==0.1.0

For an editable install

git clone https://github.com/Oneflow-Inc/vision.git
cd vision
pip install -e .

Usage¶

Create a model¶

In flowvision we support two ways to create a model.

Import the target model from flowvision.models, e.g., create alexnet from flowvision

from flowvision.models.alexnet import alexnet
model = alexnet()

# will download the pretrained model
model = alexnet(pretrained=True)

# customize model to fit different number of classes
model = alexnet(num_classes=100)

Or create model in an easier way by using ModelCreator, e.g., create alexnet model by ModelCreator

from flowvision.models import ModelCreator
alexnet = ModelCreator.create_model("alexnet")

# will download the pretrained model
alexnet = ModelCreator.create_model("alexnet", pretrained=True)

# customize model to fit different number of classes
alexnet = ModelCreator.create_model("alexnet", num_classes=100)

Tabulate all models with pretrained weights¶

ModelCreator.model_table() returns a tabular results of available models in flowvision. To check all of pretrained models, pass in pretrained=True in ModelCreator.model_table().

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

You can get the results like:

╒════════════════════════════════════════════╤══════════════╕
│ 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 supported model by Wildcard¶

It is easy to search for model architectures by using Wildcard as below:

from flowvision.models import ModelCreator
all_efficientnet_models = ModelCreator.model_table("**efficientnet**")
print(all_efficientnet_models)

You can get the results like:

╒════════════════════╤══════════════╕
│ Supported Models   │ Pretrained   │
╞════════════════════╪══════════════╡
│ efficientnet_b0    │ true         │
├────────────────────┼──────────────┤
│ efficientnet_b1    │ true         │
├────────────────────┼──────────────┤
│ efficientnet_b2    │ true         │
├────────────────────┼──────────────┤
│ efficientnet_b3    │ true         │
├────────────────────┼──────────────┤
│ efficientnet_b4    │ true         │
├────────────────────┼──────────────┤
│ efficientnet_b5    │ true         │
├────────────────────┼──────────────┤
│ efficientnet_b6    │ true         │
├────────────────────┼──────────────┤
│ efficientnet_b7    │ true         │
╘════════════════════╧══════════════╛

List all models supported in flowvision¶

ModelCreator.model_list has similar function as ModelCreator.model_table but return a list object, which gives the user a more flexible way to check the supported model in flowvision.

List all models with pretrained weights

from flowvision.models import ModelCreator
all_pretrained_models = ModelCreator.model_list(pretrained=True)
print(all_pretrained_models[:5])

You can get the results like:

['alexnet', 
 'convmixer_1024_20', 
 'convmixer_1536_20', 
 'convmixer_768_32_relu', 
 'crossformer_base_patch4_group7_224']

Support wildcard search

from flowvision.models import ModelCreator
all_efficientnet_models = ModelCreator.model_list("**efficientnet**")
print(all_efficientnet_models)

You can get the results like:

['efficientnet_b0', 
 'efficientnet_b1', 
 'efficientnet_b2', 
 'efficientnet_b3', 
 'efficientnet_b4', 
 'efficientnet_b5', 
 'efficientnet_b6', 
 'efficientnet_b7']