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] |