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Add sparsezoo table to models doc (#74)
Replacing hardcoded table in docs with an iframe and link to sparsezoo table.
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‎docs/source/models.md‎

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@@ -53,83 +53,16 @@ The contents of each model are made up of the following:
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### Image Classification
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| Model Tag | Validation Baseline Metric |
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| ------------------------------------------------------------------------------------------ | -------------------------- |
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| cv/classification/efficientnet-b0/pytorch/sparseml/imagenet/base-none | 77.3% top1 accuracy |
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| cv/classification/efficientnet-b0/pytorch/sparseml/imagenet/arch-moderate | 76.5% top1 accuracy |
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| cv/classification/efficientnet-b4/pytorch/sparseml/imagenet/base-none | 83.0% top1 accuracy |
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| cv/classification/efficientnet-b4/pytorch/sparseml/imagenet/arch-moderate | 82.1% top1 accuracy |
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| cv/classification/inception_v3/pytorch/sparseml/imagenet/base-none | 77.4% top1 accuracy |
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| cv/classification/inception_v3/pytorch/sparseml/imagenet/pruned-conservative | 77.4% top1 accuracy |
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| cv/classification/inception_v3/pytorch/sparseml/imagenet/pruned-moderate | 76.6% top1 accuracy |
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| cv/classification/mnistnet/pytorch/sparseml/mnist/base-none | 99.4% top1 accuracy |
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| cv/classification/mobilenet_v1-1.0/pytorch/sparseml/imagenet/base-none | 70.9% top1 accuracy |
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| cv/classification/mobilenet_v1-1.0/pytorch/sparseml/imagenet/pruned-conservative | 70.9% top1 accuracy |
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| cv/classification/mobilenet_v1-1.0/pytorch/sparseml/imagenet/pruned-moderate | 70.1% top1 accuracy |
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| cv/classification/mobilenet_v1-1.0/pytorch/sparseml/imagenet/pruned_quant-moderate | 70.1% top1 accuracy |
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| cv/classification/mobilenet_v2-1.0/pytorch/sparseml/imagenet/base-none | 71.9% top1 accuracy |
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| cv/classification/resnet_v1-101/keras/sparseml/imagenet/base-none | 77.4% top1 accuracy |
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| cv/classification/resnet_v1-101/keras/sparseml/imagenet/pruned-moderate | 76.6% top1 accuracy |
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| cv/classification/resnet_v1-101/pytorch/sparseml/imagenet/base-none | 77.4% top1 accuracy |
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| cv/classification/resnet_v1-101/pytorch/sparseml/imagenet/pruned-moderate | 76.6% top1 accuracy |
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| cv/classification/resnet_v1-101/pytorch/torchvision/imagenet/base-none | 76.6% top1 accuracy |
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| cv/classification/resnet_v1-101_2x/pytorch/sparseml/imagenet/base-none | 78.8% top1 accuracy |
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| cv/classification/resnet_v1-101_2x/pytorch/torchvision/imagenet/base-none | 78.8% top1 accuracy |
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| cv/classification/resnet_v1-152/keras/sparseml/imagenet/base-none | 78.3% top1 accuracy |
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| cv/classification/resnet_v1-152/keras/sparseml/imagenet/pruned-moderate | 77.5% top1 accuracy |
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| cv/classification/resnet_v1-152/pytorch/sparseml/imagenet/base-none | 78.3% top1 accuracy |
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| cv/classification/resnet_v1-152/pytorch/sparseml/imagenet/pruned-moderate | 77.5% top1 accuracy |
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| cv/classification/resnet_v1-152/pytorch/torchvision/imagenet/base-none | 77.5% top1 accuracy |
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| cv/classification/resnet_v1-18/pytorch/sparseml/imagenet/base-none | 69.8% top1 accuracy |
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| cv/classification/resnet_v1-18/pytorch/sparseml/imagenet/pruned-conservative | 69.8% top1 accuracy |
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| cv/classification/resnet_v1-18/pytorch/torchvision/imagenet/base-none | 69.8% top1 accuracy |
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| cv/classification/resnet_v1-20/keras/sparseml/cifar_10/base-none | 91.3% top1 accuracy |
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| cv/classification/resnet_v1-34/pytorch/sparseml/imagenet/base-none | 73.3% top1 accuracy |
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| cv/classification/resnet_v1-34/pytorch/sparseml/imagenet/pruned-conservative | 73.3% top1 accuracy |
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| cv/classification/resnet_v1-34/pytorch/torchvision/imagenet/base-none | 73.3% top1 accuracy |
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| cv/classification/resnet_v1-50/keras/sparseml/imagenet/base-none | 76.1% top1 accuracy |
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| cv/classification/resnet_v1-50/keras/sparseml/imagenet/pruned-conservative | 76.1% top1 accuracy |
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| cv/classification/resnet_v1-50/keras/sparseml/imagenet/pruned-moderate | 75.3% top1 accuracy |
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| cv/classification/resnet_v1-50/pytorch/sparseml/imagenet/base-none | 76.1% top1 accuracy |
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| cv/classification/resnet_v1-50/pytorch/sparseml/imagenet/pruned-conservative | 76.1% top1 accuracy |
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| cv/classification/resnet_v1-50/pytorch/sparseml/imagenet/pruned-moderate | 75.3% top1 accuracy |
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| cv/classification/resnet_v1-50/pytorch/sparseml/imagenet/pruned_quant-moderate | 75.4% top1 accuracy |
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| cv/classification/resnet_v1-50/pytorch/sparseml/imagenet-augmented/pruned_quant-aggressive | 76.1% top1 accuracy |
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| cv/classification/resnet_v1-50/pytorch/sparseml/imagenette/base-none | 99.9% top1 accuracy |
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| cv/classification/resnet_v1-50/pytorch/sparseml/imagenette/pruned-conservative | 99.9% top1 accuracy |
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| cv/classification/resnet_v1-50/pytorch/torchvision/imagenet/base-none | 99.9% top1 accuracy |
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| cv/classification/resnet_v1-50/pytorch/torchvision/imagenette/pruned-conservative | 99.9% top1 accuracy |
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| cv/classification/resnet_v1-50_2x/pytorch/sparseml/imagenet/base-none | 78.1% top1 accuracy |
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| cv/classification/resnet_v1-50_2x/pytorch/torchvision/imagenet/base-none | 78.1% top1 accuracy |
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| cv/classification/vgg-11/pytorch/sparseml/imagenet/base-none | 69.0% top1 accuracy |
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| cv/classification/vgg-11/pytorch/sparseml/imagenet/pruned-moderate | 68.3% top1 accuracy |
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| cv/classification/vgg-11/pytorch/torchvision/imagenet/base-none | 68.3% top1 accuracy |
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| cv/classification/vgg-11_bn/pytorch/sparseml/imagenet/base-none | 70.4% top1 accuracy |
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| cv/classification/vgg-11_bn/pytorch/torchvision/imagenet/base-none | 70.4% top1 accuracy |
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| cv/classification/vgg-13/pytorch/sparseml/imagenet/base-none | 69.9% top1 accuracy |
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| cv/classification/vgg-13/pytorch/torchvision/imagenet/base-none | 69.9% top1 accuracy |
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| cv/classification/vgg-13_bn/pytorch/sparseml/imagenet/base-none | 71.5% top1 accuracy |
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| cv/classification/vgg-13_bn/pytorch/torchvision/imagenet/base-none | 71.5% top1 accuracy |
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| cv/classification/vgg-16/pytorch/sparseml/imagenet/base-none | 71.6% top1 accuracy |
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| cv/classification/vgg-16/pytorch/sparseml/imagenet/pruned-conservative | 71.6% top1 accuracy |
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| cv/classification/vgg-16/pytorch/sparseml/imagenet/pruned-moderate | 70.8% top1 accuracy |
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| cv/classification/vgg-16/pytorch/torchvision/imagenet/base-none | 70.8% top1 accuracy |
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| cv/classification/vgg-16_bn/pytorch/sparseml/imagenet/base-none | 71.6% top1 accuracy |
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| cv/classification/vgg-16_bn/pytorch/torchvision/imagenet/base-none | 71.6% top1 accuracy |
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| cv/classification/vgg-19/pytorch/sparseml/imagenet/base-none | 72.4% top1 accuracy |
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| cv/classification/vgg-19/pytorch/sparseml/imagenet/pruned-moderate | 71.7% top1 accuracy |
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| cv/classification/vgg-19/pytorch/torchvision/imagenet/base-none | 71.7% top1 accuracy |
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| cv/classification/vgg-19_bn/pytorch/sparseml/imagenet/base-none | 74.2% top1 accuracy |
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| cv/classification/vgg-19_bn/pytorch/torchvision/imagenet/base-none | 74.2% top1 accuracy |
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<div>
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<iframe src="https://sparsezoo.neuralmagic.com/models/cv/classification" title="Image Classification Models" width="100%" height="500px"></iframe>
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</div>
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Image classification table not loading? View full table [here](https://sparsezoo.neuralmagic.com/models/cv/classification).
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### Object Detection
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| Model Tag | Validation Baseline Metric |
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| ------------------------------------------------------------------------------------------ | -------------------------- |
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| cv/detection/ssd-resnet50_300/pytorch/sparseml/coco/base-none | 42.7 [email protected] |
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| cv/detection/ssd-resnet50_300/pytorch/sparseml/coco/pruned-moderate | 41.8 [email protected] |
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| cv/detection/ssd-resnet50_300/pytorch/sparseml/voc/base-none | 52.2 [email protected] |
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| cv/detection/ssd-resnet50_300/pytorch/sparseml/voc/pruned-moderate | 51.5 [email protected] |
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| cv/detection/yolo_v3-spp/pytorch/ultralytics/coco/base-none | 64.2 [email protected] |
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| cv/detection/yolo_v3-spp/pytorch/ultralytics/coco/pruned-aggressive_97 | 62.4 [email protected] |
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| cv/detection/yolo_v3-spp/pytorch/ultralytics/coco/pruned_quant-aggressive_94 | 60.5 [email protected] |
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<div>
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<iframe src="https://sparsezoo.neuralmagic.com/models/cv/detection" title="Object Detect Models" width="100%" height="500px"></iframe>
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</div>
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Object detection table not loading? View full table [here](https://sparsezoo.neuralmagic.com/models/cv/detection).

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