diff --git a/docs/source/models.md b/docs/source/models.md index 2266005a..087c2f18 100644 --- a/docs/source/models.md +++ b/docs/source/models.md @@ -53,83 +53,16 @@ The contents of each model are made up of the following: ### Image Classification -| Model Tag | Validation Baseline Metric | -| ------------------------------------------------------------------------------------------ | -------------------------- | -| cv/classification/efficientnet-b0/pytorch/sparseml/imagenet/base-none | 77.3% top1 accuracy | -| cv/classification/efficientnet-b0/pytorch/sparseml/imagenet/arch-moderate | 76.5% top1 accuracy | -| cv/classification/efficientnet-b4/pytorch/sparseml/imagenet/base-none | 83.0% top1 accuracy | -| cv/classification/efficientnet-b4/pytorch/sparseml/imagenet/arch-moderate | 82.1% top1 accuracy | -| cv/classification/inception_v3/pytorch/sparseml/imagenet/base-none | 77.4% top1 accuracy | -| cv/classification/inception_v3/pytorch/sparseml/imagenet/pruned-conservative | 77.4% top1 accuracy | -| cv/classification/inception_v3/pytorch/sparseml/imagenet/pruned-moderate | 76.6% top1 accuracy | -| cv/classification/mnistnet/pytorch/sparseml/mnist/base-none | 99.4% top1 accuracy | -| cv/classification/mobilenet_v1-1.0/pytorch/sparseml/imagenet/base-none | 70.9% top1 accuracy | -| cv/classification/mobilenet_v1-1.0/pytorch/sparseml/imagenet/pruned-conservative | 70.9% top1 accuracy | -| cv/classification/mobilenet_v1-1.0/pytorch/sparseml/imagenet/pruned-moderate | 70.1% top1 accuracy | -| cv/classification/mobilenet_v1-1.0/pytorch/sparseml/imagenet/pruned_quant-moderate | 70.1% top1 accuracy | -| cv/classification/mobilenet_v2-1.0/pytorch/sparseml/imagenet/base-none | 71.9% top1 accuracy | -| cv/classification/resnet_v1-101/keras/sparseml/imagenet/base-none | 77.4% top1 accuracy | -| cv/classification/resnet_v1-101/keras/sparseml/imagenet/pruned-moderate | 76.6% top1 accuracy | -| cv/classification/resnet_v1-101/pytorch/sparseml/imagenet/base-none | 77.4% top1 accuracy | -| cv/classification/resnet_v1-101/pytorch/sparseml/imagenet/pruned-moderate | 76.6% top1 accuracy | -| cv/classification/resnet_v1-101/pytorch/torchvision/imagenet/base-none | 76.6% top1 accuracy | -| cv/classification/resnet_v1-101_2x/pytorch/sparseml/imagenet/base-none | 78.8% top1 accuracy | -| cv/classification/resnet_v1-101_2x/pytorch/torchvision/imagenet/base-none | 78.8% top1 accuracy | -| cv/classification/resnet_v1-152/keras/sparseml/imagenet/base-none | 78.3% top1 accuracy | -| cv/classification/resnet_v1-152/keras/sparseml/imagenet/pruned-moderate | 77.5% top1 accuracy | -| cv/classification/resnet_v1-152/pytorch/sparseml/imagenet/base-none | 78.3% top1 accuracy | -| cv/classification/resnet_v1-152/pytorch/sparseml/imagenet/pruned-moderate | 77.5% top1 accuracy | -| cv/classification/resnet_v1-152/pytorch/torchvision/imagenet/base-none | 77.5% top1 accuracy | -| cv/classification/resnet_v1-18/pytorch/sparseml/imagenet/base-none | 69.8% top1 accuracy | -| cv/classification/resnet_v1-18/pytorch/sparseml/imagenet/pruned-conservative | 69.8% top1 accuracy | -| cv/classification/resnet_v1-18/pytorch/torchvision/imagenet/base-none | 69.8% top1 accuracy | -| cv/classification/resnet_v1-20/keras/sparseml/cifar_10/base-none | 91.3% top1 accuracy | -| cv/classification/resnet_v1-34/pytorch/sparseml/imagenet/base-none | 73.3% top1 accuracy | -| cv/classification/resnet_v1-34/pytorch/sparseml/imagenet/pruned-conservative | 73.3% top1 accuracy | -| cv/classification/resnet_v1-34/pytorch/torchvision/imagenet/base-none | 73.3% top1 accuracy | -| cv/classification/resnet_v1-50/keras/sparseml/imagenet/base-none | 76.1% top1 accuracy | -| cv/classification/resnet_v1-50/keras/sparseml/imagenet/pruned-conservative | 76.1% top1 accuracy | -| cv/classification/resnet_v1-50/keras/sparseml/imagenet/pruned-moderate | 75.3% top1 accuracy | -| cv/classification/resnet_v1-50/pytorch/sparseml/imagenet/base-none | 76.1% top1 accuracy | -| cv/classification/resnet_v1-50/pytorch/sparseml/imagenet/pruned-conservative | 76.1% top1 accuracy | -| cv/classification/resnet_v1-50/pytorch/sparseml/imagenet/pruned-moderate | 75.3% top1 accuracy | -| cv/classification/resnet_v1-50/pytorch/sparseml/imagenet/pruned_quant-moderate | 75.4% top1 accuracy | -| cv/classification/resnet_v1-50/pytorch/sparseml/imagenet-augmented/pruned_quant-aggressive | 76.1% top1 accuracy | -| cv/classification/resnet_v1-50/pytorch/sparseml/imagenette/base-none | 99.9% top1 accuracy | -| cv/classification/resnet_v1-50/pytorch/sparseml/imagenette/pruned-conservative | 99.9% top1 accuracy | -| cv/classification/resnet_v1-50/pytorch/torchvision/imagenet/base-none | 99.9% top1 accuracy | -| cv/classification/resnet_v1-50/pytorch/torchvision/imagenette/pruned-conservative | 99.9% top1 accuracy | -| cv/classification/resnet_v1-50_2x/pytorch/sparseml/imagenet/base-none | 78.1% top1 accuracy | -| cv/classification/resnet_v1-50_2x/pytorch/torchvision/imagenet/base-none | 78.1% top1 accuracy | -| cv/classification/vgg-11/pytorch/sparseml/imagenet/base-none | 69.0% top1 accuracy | -| cv/classification/vgg-11/pytorch/sparseml/imagenet/pruned-moderate | 68.3% top1 accuracy | -| cv/classification/vgg-11/pytorch/torchvision/imagenet/base-none | 68.3% top1 accuracy | -| cv/classification/vgg-11_bn/pytorch/sparseml/imagenet/base-none | 70.4% top1 accuracy | -| cv/classification/vgg-11_bn/pytorch/torchvision/imagenet/base-none | 70.4% top1 accuracy | -| cv/classification/vgg-13/pytorch/sparseml/imagenet/base-none | 69.9% top1 accuracy | -| cv/classification/vgg-13/pytorch/torchvision/imagenet/base-none | 69.9% top1 accuracy | -| cv/classification/vgg-13_bn/pytorch/sparseml/imagenet/base-none | 71.5% top1 accuracy | -| cv/classification/vgg-13_bn/pytorch/torchvision/imagenet/base-none | 71.5% top1 accuracy | -| cv/classification/vgg-16/pytorch/sparseml/imagenet/base-none | 71.6% top1 accuracy | -| cv/classification/vgg-16/pytorch/sparseml/imagenet/pruned-conservative | 71.6% top1 accuracy | -| cv/classification/vgg-16/pytorch/sparseml/imagenet/pruned-moderate | 70.8% top1 accuracy | -| cv/classification/vgg-16/pytorch/torchvision/imagenet/base-none | 70.8% top1 accuracy | -| cv/classification/vgg-16_bn/pytorch/sparseml/imagenet/base-none | 71.6% top1 accuracy | -| cv/classification/vgg-16_bn/pytorch/torchvision/imagenet/base-none | 71.6% top1 accuracy | -| cv/classification/vgg-19/pytorch/sparseml/imagenet/base-none | 72.4% top1 accuracy | -| cv/classification/vgg-19/pytorch/sparseml/imagenet/pruned-moderate | 71.7% top1 accuracy | -| cv/classification/vgg-19/pytorch/torchvision/imagenet/base-none | 71.7% top1 accuracy | -| cv/classification/vgg-19_bn/pytorch/sparseml/imagenet/base-none | 74.2% top1 accuracy | -| cv/classification/vgg-19_bn/pytorch/torchvision/imagenet/base-none | 74.2% top1 accuracy | +
+ +
+ +Image classification table not loading? View full table [here](https://sparsezoo.neuralmagic.com/models/cv/classification). ### Object Detection -| Model Tag | Validation Baseline Metric | -| ------------------------------------------------------------------------------------------ | -------------------------- | -| cv/detection/ssd-resnet50_300/pytorch/sparseml/coco/base-none | 42.7 mAP@0.5 | -| cv/detection/ssd-resnet50_300/pytorch/sparseml/coco/pruned-moderate | 41.8 mAP@0.5 | -| cv/detection/ssd-resnet50_300/pytorch/sparseml/voc/base-none | 52.2 mAP@0.5 | -| cv/detection/ssd-resnet50_300/pytorch/sparseml/voc/pruned-moderate | 51.5 mAP@0.5 | -| cv/detection/yolo_v3-spp/pytorch/ultralytics/coco/base-none | 64.2 mAP@0.5 | -| cv/detection/yolo_v3-spp/pytorch/ultralytics/coco/pruned-aggressive_97 | 62.4 mAP@0.5 | -| cv/detection/yolo_v3-spp/pytorch/ultralytics/coco/pruned_quant-aggressive_94 | 60.5 mAP@0.5 | +
+ +
+ +Object detection table not loading? View full table [here](https://sparsezoo.neuralmagic.com/models/cv/detection). \ No newline at end of file