Recipes

Each recipe in the SparseZoo is stored under the model created with it and has a specific stub that identifies it. The stubs are made up of the following structure:

DOMAIN/SUB_DOMAIN/ARCHITECTURE{-SUB_ARCHITECTURE}/FRAMEWORK/REPO/DATASET{-TRAINING_SCHEME}/SPARSE_NAME-SPARSE_CATEGORY-{SPARSE_TARGET}?recipe-type=RECIPE_TYPE

The properties within each model stub are defined as the following:

Model Property Definition Examples
DOMAIN The type of solution the model is architected and trained for cv, nlp
SUB_DOMAIN The sub type of solution the model is architected and trained for classification, segmentation
ARCHITECTURE The name of the guiding setup for the network's graph resnet_v1, mobilenet_v1
SUB_ARCHITECTURE (optional) The scaled version of the architecture such as width or depth 50, 101, 152
FRAMEWORK The machine learning framework the model was defined and trained in pytorch, tensorflow_v1
REPO The model repository the model and baseline weights originated from sparseml, torchvision
DATASET The dataset the model was trained on imagenet, cifar10
TRAINING_SCHEME (optional) A description on how the model was trained augmented, lower_lr
SPARSE_NAME An overview of what was done to sparsify the model base, pruned, quant (quantized), pruned_quant, arch (architecture modified)
SPARSE_CATEGORY Descriptor on the degree to which the model is sparsified as compared with the baseline metric none, conservative (100% baseline), moderate (>= 99% baseline), aggressive (< 99%)
SPARSE_TARGET (optional) Descriptor for the target environment the model was sparsified for disk, edge, deepsparse, gpu
RECIPE_TYPE A named descriptor for the recipe signifying what the recipe is for original, transfer_learn

Image Classification

Model Tag Validation Baseline Metric
cv/classification/efficientnet-b0/pytorch/sparseml/imagenet/arch-moderate?recipe_type=original 76.5% top1 accuracy
cv/classification/efficientnet-b4/pytorch/sparseml/imagenet/arch-moderate?recipe_type=original 82.1% top1 accuracy
cv/classification/inception_v3/pytorch/sparseml/imagenet/pruned-conservative?recipe_type=original 77.4% top1 accuracy
cv/classification/inception_v3/pytorch/sparseml/imagenet/pruned-moderate?recipe_type=original 76.6% top1 accuracy
cv/classification/mobilenet_v1-1.0/pytorch/sparseml/imagenet/base-none?recipe_type=original 70.9% top1 accuracy
cv/classification/mobilenet_v1-1.0/pytorch/sparseml/imagenet/pruned-conservative?recipe_type=original 70.9% top1 accuracy
cv/classification/mobilenet_v1-1.0/pytorch/sparseml/imagenet/pruned-moderate?recipe_type=original 70.1% top1 accuracy
cv/classification/mobilenet_v1-1.0/pytorch/sparseml/imagenet/pruned_quant-moderate?recipe_type=original 70.1% top1 accuracy
cv/classification/mobilenet_v1-1.0/pytorch/sparseml/imagenet/pruned_quant-moderate?recipe_type=original 70.1% top1 accuracy
cv/classification/resnet_v1-101/pytorch/sparseml/imagenet/pruned-moderate?recipe_type=original 76.6% top1 accuracy
cv/classification/resnet_v1-152/pytorch/sparseml/imagenet/pruned-moderate?recipe_type=original 77.5% top1 accuracy
cv/classification/resnet_v1-18/pytorch/sparseml/imagenet/pruned-conservative?recipe_type=original 69.8% top1 accuracy
cv/classification/resnet_v1-34/pytorch/sparseml/imagenet/pruned-conservative?recipe_type=original 73.3% top1 accuracy
cv/classification/resnet_v1-50/pytorch/sparseml/imagenet/pruned-conservative?recipe_type=original 76.1% top1 accuracy
cv/classification/resnet_v1-50/pytorch/sparseml/imagenet/pruned-moderate?recipe_type=original 75.3% top1 accuracy
cv/classification/resnet_v1-50/pytorch/sparseml/imagenet-augmented/pruned_quant-aggressive?recipe_type=original 76.1% top1 accuracy
cv/classification/resnet_v1-50/pytorch/sparseml/imagenette/pruned-conservative?recipe_type=original 99.9% top1 accuracy
cv/classification/resnet_v1-50/pytorch/torchvision/imagenette/pruned-conservative?recipe_type=original 99.9% top1 accuracy
cv/classification/vgg-11/pytorch/sparseml/imagenet/pruned-moderate?recipe_type=original 68.3% top1 accuracy
cv/classification/vgg-16/pytorch/sparseml/imagenet/pruned-conservative?recipe_type=original 71.6% top1 accuracy
cv/classification/vgg-16/pytorch/sparseml/imagenet/pruned-moderate?recipe_type=original 70.8% top1 accuracy
cv/classification/vgg-19/pytorch/sparseml/imagenet/pruned-moderate?recipe_type=original 71.7% top1 accuracy

Object Detection

Model Tag Validation Baseline Metric
cv/detection/ssd-resnet50_300/pytorch/sparseml/coco/pruned-moderate?recipe_type=original 41.8 [email protected]
cv/detection/ssd-resnet50_300/pytorch/sparseml/voc/pruned-moderate?recipe_type=original 51.5 [email protected]
cv/detection/yolo_v3-spp/pytorch/ultralytics/coco/pruned-aggressive?recipe_type=original 62.1 [email protected]