sparsezoo.models.classification package
Submodules
sparsezoo.models.classification.efficientnet module
- EfficientNet models:
-
sparsezoo.models.classification.efficientnet.efficientnet_b0(framework: str = 'pytorch', repo: str = 'sparseml', dataset: str = 'imagenet', training_scheme: Optional[str] = None, sparse_name: str = 'base', sparse_category: str = 'none', sparse_target: Optional[str] = None, override_folder_name: Optional[str] = None, override_parent_path: Optional[str] = None, force_token_refresh: bool = False) → sparsezoo.objects.model.Model[source] Convenience function for getting an efficientnet b0 model
- Parameters
framework – The framework the model the object belongs to was trained on; e.g. pytorch, tensorflow
repo – the source repo for the model the object belongs to; e.g. sparseml, torchvision
dataset – The dataset the model the object belongs to was trained on; e.g. imagenet, cifar10
training_scheme – The training scheme used on the model the object belongs to if any; e.g. augmented
sparse_name – The name describing the sparsification of the model the object belongs to, e.g. base, pruned, pruned_quant
sparse_category – The degree of sparsification of the model the object belongs to; e.g. none, conservative (~100% baseline metric), moderate (>=99% baseline metric), aggressive (<99% baseline metric)
sparse_target – The deployment target of sparsification of the model the object belongs to; e.g. edge, deepsparse, deepsparse_throughput, gpu
override_folder_name – Override for the name of the folder to save this file under
override_parent_path – Path to override the default save path for where to save the parent folder for this file under
force_token_refresh – True to refresh the auth token, False otherwise
- Returns
The created model
-
sparsezoo.models.classification.efficientnet.efficientnet_b4(framework: str = 'pytorch', repo: str = 'sparseml', dataset: str = 'imagenet', training_scheme: Optional[str] = None, sparse_name: str = 'base', sparse_category: str = 'none', sparse_target: Optional[str] = None, override_folder_name: Optional[str] = None, override_parent_path: Optional[str] = None, force_token_refresh: bool = False) → sparsezoo.objects.model.Model[source] Convenience function for getting an efficientnet b0 model
- Parameters
framework – The framework the model the object belongs to was trained on; e.g. pytorch, tensorflow
repo – the source repo for the model the object belongs to; e.g. sparseml, torchvision
dataset – The dataset the model the object belongs to was trained on; e.g. imagenet, cifar10
training_scheme – The training scheme used on the model the object belongs to if any; e.g. augmented
sparse_name – The name describing the sparsification of the model the object belongs to, e.g. base, pruned, pruned_quant
sparse_category – The degree of sparsification of the model the object belongs to; e.g. none, conservative (~100% baseline metric), moderate (>=99% baseline metric), aggressive (<99% baseline metric)
sparse_target – The deployment target of sparsification of the model the object belongs to; e.g. edge, deepsparse, deepsparse_throughput, gpu
override_folder_name – Override for the name of the folder to save this file under
override_parent_path – Path to override the default save path for where to save the parent folder for this file under
force_token_refresh – True to refresh the auth token, False otherwise
- Returns
The created model
sparsezoo.models.classification.inception module
- Inception models:
-
sparsezoo.models.classification.inception.inception_v3(framework: str = 'pytorch', repo: str = 'sparseml', dataset: str = 'imagenet', training_scheme: Optional[str] = None, sparse_name: str = 'base', sparse_category: str = 'none', sparse_target: Optional[str] = None, override_folder_name: Optional[str] = None, override_parent_path: Optional[str] = None, force_token_refresh: bool = False) → sparsezoo.objects.model.Model[source] Convenience function for getting an inception_v3 model
- Parameters
framework – The framework the model the object belongs to was trained on; e.g. pytorch, tensorflow
repo – the source repo for the model the object belongs to; e.g. sparseml, torchvision
dataset – The dataset the model the object belongs to was trained on; e.g. imagenet, cifar10
training_scheme – The training scheme used on the model the object belongs to if any; e.g. augmented
sparse_name – The name describing the sparsification of the model the object belongs to, e.g. base, pruned, pruned_quant
sparse_category – The degree of sparsification of the model the object belongs to; e.g. none, conservative (~100% baseline metric), moderate (>=99% baseline metric), aggressive (<99% baseline metric)
sparse_target – The deployment target of sparsification of the model the object belongs to; e.g. edge, deepsparse, deepsparse_throughput, gpu
override_folder_name – Override for the name of the folder to save this file under
override_parent_path – Path to override the default save path for where to save the parent folder for this file under
force_token_refresh – True to refresh the auth token, False otherwise
- Returns
The created model
sparsezoo.models.classification.mobilenet module
- MobileNet models:
https://arxiv.org/abs/1704.04861 https://arxiv.org/abs/1801.04381
-
sparsezoo.models.classification.mobilenet.mobilenet_v1(framework: str = 'pytorch', repo: str = 'sparseml', dataset: str = 'imagenet', training_scheme: Optional[str] = None, sparse_name: str = 'base', sparse_category: str = 'none', sparse_target: Optional[str] = None, override_folder_name: Optional[str] = None, override_parent_path: Optional[str] = None, force_token_refresh: bool = False) → sparsezoo.objects.model.Model[source] Convenience function for getting a mobilenet_v1 model
- Parameters
framework – The framework the model the object belongs to was trained on; e.g. pytorch, tensorflow
repo – the source repo for the model the object belongs to; e.g. sparseml, torchvision
dataset – The dataset the model the object belongs to was trained on; e.g. imagenet, cifar10
training_scheme – The training scheme used on the model the object belongs to if any; e.g. augmented
sparse_name – The name describing the sparsification of the model the object belongs to, e.g. base, pruned, pruned_quant
sparse_category – The degree of sparsification of the model the object belongs to; e.g. none, conservative (~100% baseline metric), moderate (>=99% baseline metric), aggressive (<99% baseline metric)
sparse_target – The deployment target of sparsification of the model the object belongs to; e.g. edge, deepsparse, deepsparse_throughput, gpu
override_folder_name – Override for the name of the folder to save this file under
override_parent_path – Path to override the default save path for where to save the parent folder for this file under
force_token_refresh – True to refresh the auth token, False otherwise
- Returns
The created model
-
sparsezoo.models.classification.mobilenet.mobilenet_v2(framework: str = 'pytorch', repo: str = 'sparseml', dataset: str = 'imagenet', training_scheme: Optional[str] = None, sparse_name: str = 'base', sparse_category: str = 'none', sparse_target: Optional[str] = None, override_folder_name: Optional[str] = None, override_parent_path: Optional[str] = None, force_token_refresh: bool = False) → sparsezoo.objects.model.Model[source] Convenience function for getting a mobilenet_v2 model
- Parameters
framework – The framework the model the object belongs to was trained on; e.g. pytorch, tensorflow
repo – the source repo for the model the object belongs to; e.g. sparseml, torchvision
dataset – The dataset the model the object belongs to was trained on; e.g. imagenet, cifar10
training_scheme – The training scheme used on the model the object belongs to if any; e.g. augmented
sparse_name – The name describing the sparsification of the model the object belongs to, e.g. base, pruned, pruned_quant
sparse_category – The degree of sparsification of the model the object belongs to; e.g. none, conservative (~100% baseline metric), moderate (>=99% baseline metric), aggressive (<99% baseline metric)
sparse_target – The deployment target of sparsification of the model the object belongs to; e.g. edge, deepsparse, deepsparse_throughput, gpu
override_folder_name – Override for the name of the folder to save this file under
override_parent_path – Path to override the default save path for where to save the parent folder for this file under
force_token_refresh – True to refresh the auth token, False otherwise
- Returns
The created model
sparsezoo.models.classification.resnet module
- ResNet models:
-
sparsezoo.models.classification.resnet.resnet_101(framework: str = 'pytorch', repo: str = 'sparseml', dataset: str = 'imagenet', training_scheme: Optional[str] = None, sparse_name: str = 'base', sparse_category: str = 'none', sparse_target: Optional[str] = None, override_folder_name: Optional[str] = None, override_parent_path: Optional[str] = None, force_token_refresh: bool = False) → sparsezoo.objects.model.Model[source] Convenience function for getting a resnet 101 model
- Parameters
framework – The framework the model the object belongs to was trained on; e.g. pytorch, tensorflow
repo – the source repo for the model the object belongs to; e.g. sparseml, torchvision
dataset – The dataset the model the object belongs to was trained on; e.g. imagenet, cifar10
training_scheme – The training scheme used on the model the object belongs to if any; e.g. augmented
sparse_name – The name describing the sparsification of the model the object belongs to, e.g. base, pruned, pruned_quant
sparse_category – The degree of sparsification of the model the object belongs to; e.g. none, conservative (~100% baseline metric), moderate (>=99% baseline metric), aggressive (<99% baseline metric)
sparse_target – The deployment target of sparsification of the model the object belongs to; e.g. edge, deepsparse, deepsparse_throughput, gpu
override_folder_name – Override for the name of the folder to save this file under
override_parent_path – Path to override the default save path for where to save the parent folder for this file under
force_token_refresh – True to refresh the auth token, False otherwise
- Returns
The created model
-
sparsezoo.models.classification.resnet.resnet_101_2x(framework: str = 'pytorch', repo: str = 'sparseml', dataset: str = 'imagenet', training_scheme: Optional[str] = None, sparse_name: str = 'base', sparse_category: str = 'none', sparse_target: Optional[str] = None, override_folder_name: Optional[str] = None, override_parent_path: Optional[str] = None, force_token_refresh: bool = False) → sparsezoo.objects.model.Model[source] Convenience function for getting a resnet 101 2x width model
- Parameters
framework – The framework the model the object belongs to was trained on; e.g. pytorch, tensorflow
repo – the source repo for the model the object belongs to; e.g. sparseml, torchvision
dataset – The dataset the model the object belongs to was trained on; e.g. imagenet, cifar10
training_scheme – The training scheme used on the model the object belongs to if any; e.g. augmented
sparse_name – The name describing the sparsification of the model the object belongs to, e.g. base, pruned, pruned_quant
sparse_category – The degree of sparsification of the model the object belongs to; e.g. none, conservative (~100% baseline metric), moderate (>=99% baseline metric), aggressive (<99% baseline metric)
sparse_target – The deployment target of sparsification of the model the object belongs to; e.g. edge, deepsparse, deepsparse_throughput, gpu
override_folder_name – Override for the name of the folder to save this file under
override_parent_path – Path to override the default save path for where to save the parent folder for this file under
force_token_refresh – True to refresh the auth token, False otherwise
- Returns
The created model
-
sparsezoo.models.classification.resnet.resnet_152(framework: str = 'pytorch', repo: str = 'sparseml', dataset: str = 'imagenet', training_scheme: Optional[str] = None, sparse_name: str = 'base', sparse_category: str = 'none', sparse_target: Optional[str] = None, override_folder_name: Optional[str] = None, override_parent_path: Optional[str] = None, force_token_refresh: bool = False) → sparsezoo.objects.model.Model[source] Convenience function for getting a resnet 152 model
- Parameters
framework – The framework the model the object belongs to was trained on; e.g. pytorch, tensorflow
repo – the source repo for the model the object belongs to; e.g. sparseml, torchvision
dataset – The dataset the model the object belongs to was trained on; e.g. imagenet, cifar10
training_scheme – The training scheme used on the model the object belongs to if any; e.g. augmented
sparse_name – The name describing the sparsification of the model the object belongs to, e.g. base, pruned, pruned_quant
sparse_category – The degree of sparsification of the model the object belongs to; e.g. none, conservative (~100% baseline metric), moderate (>=99% baseline metric), aggressive (<99% baseline metric)
sparse_target – The deployment target of sparsification of the model the object belongs to; e.g. edge, deepsparse, deepsparse_throughput, gpu
override_folder_name – Override for the name of the folder to save this file under
override_parent_path – Path to override the default save path for where to save the parent folder for this file under
force_token_refresh – True to refresh the auth token, False otherwise
- Returns
The created model
-
sparsezoo.models.classification.resnet.resnet_18(framework: str = 'pytorch', repo: str = 'sparseml', dataset: str = 'imagenet', training_scheme: Optional[str] = None, sparse_name: str = 'base', sparse_category: str = 'none', sparse_target: Optional[str] = None, override_folder_name: Optional[str] = None, override_parent_path: Optional[str] = None, force_token_refresh: bool = False) → sparsezoo.objects.model.Model[source] Convenience function for getting a resnet 18 model
- Parameters
framework – The framework the model the object belongs to was trained on; e.g. pytorch, tensorflow
repo – the source repo for the model the object belongs to; e.g. sparseml, torchvision
dataset – The dataset the model the object belongs to was trained on; e.g. imagenet, cifar10
training_scheme – The training scheme used on the model the object belongs to if any; e.g. augmented
sparse_name – The name describing the sparsification of the model the object belongs to, e.g. base, pruned, pruned_quant
sparse_category – The degree of sparsification of the model the object belongs to; e.g. none, conservative (~100% baseline metric), moderate (>=99% baseline metric), aggressive (<99% baseline metric)
sparse_target – The deployment target of sparsification of the model the object belongs to; e.g. edge, deepsparse, deepsparse_throughput, gpu
override_folder_name – Override for the name of the folder to save this file under
override_parent_path – Path to override the default save path for where to save the parent folder for this file under
force_token_refresh – True to refresh the auth token, False otherwise
- Returns
The created model
-
sparsezoo.models.classification.resnet.resnet_34(framework: str = 'pytorch', repo: str = 'sparseml', dataset: str = 'imagenet', training_scheme: Optional[str] = None, sparse_name: str = 'base', sparse_category: str = 'none', sparse_target: Optional[str] = None, override_folder_name: Optional[str] = None, override_parent_path: Optional[str] = None, force_token_refresh: bool = False) → sparsezoo.objects.model.Model[source] Convenience function for getting a resnet 34 model
- Parameters
framework – The framework the model the object belongs to was trained on; e.g. pytorch, tensorflow
repo – the source repo for the model the object belongs to; e.g. sparseml, torchvision
dataset – The dataset the model the object belongs to was trained on; e.g. imagenet, cifar10
training_scheme – The training scheme used on the model the object belongs to if any; e.g. augmented
sparse_name – The name describing the sparsification of the model the object belongs to, e.g. base, pruned, pruned_quant
sparse_category – The degree of sparsification of the model the object belongs to; e.g. none, conservative (~100% baseline metric), moderate (>=99% baseline metric), aggressive (<99% baseline metric)
sparse_target – The deployment target of sparsification of the model the object belongs to; e.g. edge, deepsparse, deepsparse_throughput, gpu
override_folder_name – Override for the name of the folder to save this file under
override_parent_path – Path to override the default save path for where to save the parent folder for this file under
force_token_refresh – True to refresh the auth token, False otherwise
- Returns
The created model
-
sparsezoo.models.classification.resnet.resnet_50(framework: str = 'pytorch', repo: str = 'sparseml', dataset: str = 'imagenet', training_scheme: Optional[str] = None, sparse_name: str = 'base', sparse_category: str = 'none', sparse_target: Optional[str] = None, override_folder_name: Optional[str] = None, override_parent_path: Optional[str] = None, force_token_refresh: bool = False) → sparsezoo.objects.model.Model[source] Convenience function for getting a resnet 50 model
- Parameters
framework – The framework the model the object belongs to was trained on; e.g. pytorch, tensorflow
repo – the source repo for the model the object belongs to; e.g. sparseml, torchvision
dataset – The dataset the model the object belongs to was trained on; e.g. imagenet, cifar10
training_scheme – The training scheme used on the model the object belongs to if any; e.g. augmented
sparse_name – The name describing the sparsification of the model the object belongs to, e.g. base, pruned, pruned_quant
sparse_category – The degree of sparsification of the model the object belongs to; e.g. none, conservative (~100% baseline metric), moderate (>=99% baseline metric), aggressive (<99% baseline metric)
sparse_target – The deployment target of sparsification of the model the object belongs to; e.g. edge, deepsparse, deepsparse_throughput, gpu
override_folder_name – Override for the name of the folder to save this file under
override_parent_path – Path to override the default save path for where to save the parent folder for this file under
force_token_refresh – True to refresh the auth token, False otherwise
- Returns
The created model
-
sparsezoo.models.classification.resnet.resnet_50_2x(framework: str = 'pytorch', repo: str = 'sparseml', dataset: str = 'imagenet', training_scheme: Optional[str] = None, sparse_name: str = 'base', sparse_category: str = 'none', sparse_target: Optional[str] = None, override_folder_name: Optional[str] = None, override_parent_path: Optional[str] = None, force_token_refresh: bool = False) → sparsezoo.objects.model.Model[source] Convenience function for getting a resnet 50 2x width model
- Parameters
framework – The framework the model the object belongs to was trained on; e.g. pytorch, tensorflow
repo – the source repo for the model the object belongs to; e.g. sparseml, torchvision
dataset – The dataset the model the object belongs to was trained on; e.g. imagenet, cifar10
training_scheme – The training scheme used on the model the object belongs to if any; e.g. augmented
sparse_name – The name describing the sparsification of the model the object belongs to, e.g. base, pruned, pruned_quant
sparse_category – The degree of sparsification of the model the object belongs to; e.g. none, conservative (~100% baseline metric), moderate (>=99% baseline metric), aggressive (<99% baseline metric)
sparse_target – The deployment target of sparsification of the model the object belongs to; e.g. edge, deepsparse, deepsparse_throughput, gpu
override_folder_name – Override for the name of the folder to save this file under
override_parent_path – Path to override the default save path for where to save the parent folder for this file under
force_token_refresh – True to refresh the auth token, False otherwise
- Returns
The created model
sparsezoo.models.classification.vgg module
- VGG models:
-
sparsezoo.models.classification.vgg.vgg_11(framework: str = 'pytorch', repo: str = 'sparseml', dataset: str = 'imagenet', training_scheme: Optional[str] = None, sparse_name: str = 'base', sparse_category: str = 'none', sparse_target: Optional[str] = None, override_folder_name: Optional[str] = None, override_parent_path: Optional[str] = None, force_token_refresh: bool = False) → sparsezoo.objects.model.Model[source] Convenience function for getting a vgg 11 model
- Parameters
framework – The framework the model the object belongs to was trained on; e.g. pytorch, tensorflow
repo – the source repo for the model the object belongs to; e.g. sparseml, torchvision
dataset – The dataset the model the object belongs to was trained on; e.g. imagenet, cifar10
training_scheme – The training scheme used on the model the object belongs to if any; e.g. augmented
sparse_name – The name describing the sparsification of the model the object belongs to, e.g. base, pruned, pruned_quant
sparse_category – The degree of sparsification of the model the object belongs to; e.g. none, conservative (~100% baseline metric), moderate (>=99% baseline metric), aggressive (<99% baseline metric)
sparse_target – The deployment target of sparsification of the model the object belongs to; e.g. edge, deepsparse, deepsparse_throughput, gpu
override_folder_name – Override for the name of the folder to save this file under
override_parent_path – Path to override the default save path for where to save the parent folder for this file under
force_token_refresh – True to refresh the auth token, False otherwise
- Returns
The created model
-
sparsezoo.models.classification.vgg.vgg_11bn(framework: str = 'pytorch', repo: str = 'sparseml', dataset: str = 'imagenet', training_scheme: Optional[str] = None, sparse_name: str = 'base', sparse_category: str = 'none', sparse_target: Optional[str] = None, override_folder_name: Optional[str] = None, override_parent_path: Optional[str] = None, force_token_refresh: bool = False) → sparsezoo.objects.model.Model[source] Convenience function for getting a batch normalized vgg 11 model
- Parameters
framework – The framework the model the object belongs to was trained on; e.g. pytorch, tensorflow
repo – the source repo for the model the object belongs to; e.g. sparseml, torchvision
dataset – The dataset the model the object belongs to was trained on; e.g. imagenet, cifar10
training_scheme – The training scheme used on the model the object belongs to if any; e.g. augmented
sparse_name – The name describing the sparsification of the model the object belongs to, e.g. base, pruned, pruned_quant
sparse_category – The degree of sparsification of the model the object belongs to; e.g. none, conservative (~100% baseline metric), moderate (>=99% baseline metric), aggressive (<99% baseline metric)
sparse_target – The deployment target of sparsification of the model the object belongs to; e.g. edge, deepsparse, deepsparse_throughput, gpu
override_folder_name – Override for the name of the folder to save this file under
override_parent_path – Path to override the default save path for where to save the parent folder for this file under
force_token_refresh – True to refresh the auth token, False otherwise
- Returns
The created model
-
sparsezoo.models.classification.vgg.vgg_13(framework: str = 'pytorch', repo: str = 'sparseml', dataset: str = 'imagenet', training_scheme: Optional[str] = None, sparse_name: str = 'base', sparse_category: str = 'none', sparse_target: Optional[str] = None, override_folder_name: Optional[str] = None, override_parent_path: Optional[str] = None, force_token_refresh: bool = False) → sparsezoo.objects.model.Model[source] Convenience function for getting a vgg 13 model
- Parameters
framework – The framework the model the object belongs to was trained on; e.g. pytorch, tensorflow
repo – the source repo for the model the object belongs to; e.g. sparseml, torchvision
dataset – The dataset the model the object belongs to was trained on; e.g. imagenet, cifar10
training_scheme – The training scheme used on the model the object belongs to if any; e.g. augmented
sparse_name – The name describing the sparsification of the model the object belongs to, e.g. base, pruned, pruned_quant
sparse_category – The degree of sparsification of the model the object belongs to; e.g. none, conservative (~100% baseline metric), moderate (>=99% baseline metric), aggressive (<99% baseline metric)
sparse_target – The deployment target of sparsification of the model the object belongs to; e.g. edge, deepsparse, deepsparse_throughput, gpu
override_folder_name – Override for the name of the folder to save this file under
override_parent_path – Path to override the default save path for where to save the parent folder for this file under
force_token_refresh – True to refresh the auth token, False otherwise
- Returns
The created model
-
sparsezoo.models.classification.vgg.vgg_13bn(framework: str = 'pytorch', repo: str = 'sparseml', dataset: str = 'imagenet', training_scheme: Optional[str] = None, sparse_name: str = 'base', sparse_category: str = 'none', sparse_target: Optional[str] = None, override_folder_name: Optional[str] = None, override_parent_path: Optional[str] = None, force_token_refresh: bool = False) → sparsezoo.objects.model.Model[source] Convenience function for getting a batch normalized vgg 13 model
- Parameters
framework – The framework the model the object belongs to was trained on; e.g. pytorch, tensorflow
repo – the source repo for the model the object belongs to; e.g. sparseml, torchvision
dataset – The dataset the model the object belongs to was trained on; e.g. imagenet, cifar10
training_scheme – The training scheme used on the model the object belongs to if any; e.g. augmented
sparse_name – The name describing the sparsification of the model the object belongs to, e.g. base, pruned, pruned_quant
sparse_category – The degree of sparsification of the model the object belongs to; e.g. none, conservative (~100% baseline metric), moderate (>=99% baseline metric), aggressive (<99% baseline metric)
sparse_target – The deployment target of sparsification of the model the object belongs to; e.g. edge, deepsparse, deepsparse_throughput, gpu
override_folder_name – Override for the name of the folder to save this file under
override_parent_path – Path to override the default save path for where to save the parent folder for this file under
force_token_refresh – True to refresh the auth token, False otherwise
- Returns
The created model
-
sparsezoo.models.classification.vgg.vgg_16(framework: str = 'pytorch', repo: str = 'sparseml', dataset: str = 'imagenet', training_scheme: Optional[str] = None, sparse_name: str = 'base', sparse_category: str = 'none', sparse_target: Optional[str] = None, override_folder_name: Optional[str] = None, override_parent_path: Optional[str] = None, force_token_refresh: bool = False) → sparsezoo.objects.model.Model[source] Convenience function for getting a vgg 16 model
- Parameters
framework – The framework the model the object belongs to was trained on; e.g. pytorch, tensorflow
repo – the source repo for the model the object belongs to; e.g. sparseml, torchvision
dataset – The dataset the model the object belongs to was trained on; e.g. imagenet, cifar10
training_scheme – The training scheme used on the model the object belongs to if any; e.g. augmented
sparse_name – The name describing the sparsification of the model the object belongs to, e.g. base, pruned, pruned_quant
sparse_category – The degree of sparsification of the model the object belongs to; e.g. none, conservative (~100% baseline metric), moderate (>=99% baseline metric), aggressive (<99% baseline metric)
sparse_target – The deployment target of sparsification of the model the object belongs to; e.g. edge, deepsparse, deepsparse_throughput, gpu
override_folder_name – Override for the name of the folder to save this file under
override_parent_path – Path to override the default save path for where to save the parent folder for this file under
force_token_refresh – True to refresh the auth token, False otherwise
- Returns
The created model
-
sparsezoo.models.classification.vgg.vgg_16bn(framework: str = 'pytorch', repo: str = 'sparseml', dataset: str = 'imagenet', training_scheme: Optional[str] = None, sparse_name: str = 'base', sparse_category: str = 'none', sparse_target: Optional[str] = None, override_folder_name: Optional[str] = None, override_parent_path: Optional[str] = None, force_token_refresh: bool = False) → sparsezoo.objects.model.Model[source] Convenience function for getting a batch normalized vgg 16 model
- Parameters
framework – The framework the model the object belongs to was trained on; e.g. pytorch, tensorflow
repo – the source repo for the model the object belongs to; e.g. sparseml, torchvision
dataset – The dataset the model the object belongs to was trained on; e.g. imagenet, cifar10
training_scheme – The training scheme used on the model the object belongs to if any; e.g. augmented
sparse_name – The name describing the sparsification of the model the object belongs to, e.g. base, pruned, pruned_quant
sparse_category – The degree of sparsification of the model the object belongs to; e.g. none, conservative (~100% baseline metric), moderate (>=99% baseline metric), aggressive (<99% baseline metric)
sparse_target – The deployment target of sparsification of the model the object belongs to; e.g. edge, deepsparse, deepsparse_throughput, gpu
override_folder_name – Override for the name of the folder to save this file under
override_parent_path – Path to override the default save path for where to save the parent folder for this file under
force_token_refresh – True to refresh the auth token, False otherwise
- Returns
The created model
-
sparsezoo.models.classification.vgg.vgg_19(framework: str = 'pytorch', repo: str = 'sparseml', dataset: str = 'imagenet', training_scheme: Optional[str] = None, sparse_name: str = 'base', sparse_category: str = 'none', sparse_target: Optional[str] = None, override_folder_name: Optional[str] = None, override_parent_path: Optional[str] = None, force_token_refresh: bool = False) → sparsezoo.objects.model.Model[source] Convenience function for getting a vgg 19 model
- Parameters
framework – The framework the model the object belongs to was trained on; e.g. pytorch, tensorflow
repo – the source repo for the model the object belongs to; e.g. sparseml, torchvision
dataset – The dataset the model the object belongs to was trained on; e.g. imagenet, cifar10
training_scheme – The training scheme used on the model the object belongs to if any; e.g. augmented
sparse_name – The name describing the sparsification of the model the object belongs to, e.g. base, pruned, pruned_quant
sparse_category – The degree of sparsification of the model the object belongs to; e.g. none, conservative (~100% baseline metric), moderate (>=99% baseline metric), aggressive (<99% baseline metric)
sparse_target – The deployment target of sparsification of the model the object belongs to; e.g. edge, deepsparse, deepsparse_throughput, gpu
override_folder_name – Override for the name of the folder to save this file under
override_parent_path – Path to override the default save path for where to save the parent folder for this file under
force_token_refresh – True to refresh the auth token, False otherwise
- Returns
The created model
-
sparsezoo.models.classification.vgg.vgg_19bn(framework: str = 'pytorch', repo: str = 'sparseml', dataset: str = 'imagenet', training_scheme: Optional[str] = None, sparse_name: str = 'base', sparse_category: str = 'none', sparse_target: Optional[str] = None, override_folder_name: Optional[str] = None, override_parent_path: Optional[str] = None, force_token_refresh: bool = False) → sparsezoo.objects.model.Model[source] Convenience function for getting a batch normalized vgg 19 model
- Parameters
framework – The framework the model the object belongs to was trained on; e.g. pytorch, tensorflow
repo – the source repo for the model the object belongs to; e.g. sparseml, torchvision
dataset – The dataset the model the object belongs to was trained on; e.g. imagenet, cifar10
training_scheme – The training scheme used on the model the object belongs to if any; e.g. augmented
sparse_name – The name describing the sparsification of the model the object belongs to, e.g. base, pruned, pruned_quant
sparse_category – The degree of sparsification of the model the object belongs to; e.g. none, conservative (~100% baseline metric), moderate (>=99% baseline metric), aggressive (<99% baseline metric)
sparse_target – The deployment target of sparsification of the model the object belongs to; e.g. edge, deepsparse, deepsparse_throughput, gpu
override_folder_name – Override for the name of the folder to save this file under
override_parent_path – Path to override the default save path for where to save the parent folder for this file under
force_token_refresh – True to refresh the auth token, False otherwise
- Returns
The created model
Module contents
Image classification models