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add more models to benchmark results

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Original file line number	Diff line number	Diff line change
`@@ -94,7 +94,17 @@ and this is a sample for larger models: simplenet variants outperform many newer`
`94`	`94`	`\| mobilenetv2_100 \| 295.68 \| 3.50 \| 72.970 \| 91.020 \|`
`95`	`95`	`\| densenet121 \| 293.94 \| 7.98 \| 75.584 \| 92.652 \|`
`96`	`96`	`\| mnasnet_100 \| 262.25 \| 4.38 \| 74.658 \| 92.112 \|`
`97`		`-`
	`97`	`+\| vgg11 \| 260.38 \| 132.86 \| 69.028 \| 88.626 \|`
	`98`	`+\| vgg11_bn \| 248.92 \| 132.87 \| 70.360 \| 89.802 \|`
	`99`	`+\| mobilenetv2_110d \| 230.80 \| 4.52 \| 75.038 \| 92.184 \|`
	`100`	`+\| efficientnet_lite0 \| 224.81 \| 4.65 \| 75.476 \| 92.512 \|`
	`101`	`+\| tf_efficientnet_lite0 \| 219.93 \| 4.65 \| 74.832 \| 92.174 \|`
	`102`	`+\| vgg13 \| 154.03 \| 133.05 \| 69.926 \| 89.246 \|`
	`103`	`+\| vgg13_bn \| 144.39 \| 133.05 \| 71.594 \| 90.376 \|`
	`104`	`+\| vgg16 \| 123.70 \| 138.36 \| 71.590 \| 90.382 \|`
	`105`	`+\| vgg16_bn \| 117.06 \| 138.37 \| 73.350 \| 91.504 \|`
	`106`	`+\| vgg19 \| 103.71 \| 143.67 \| 72.366 \| 90.870 \|`
	`107`	`+\| vgg19_bn \| 98.59 \| 143.68 \| 74.214 \| 91.848 \|`
`98`	`108`
`99`	`109`	Benchmark was done using a GTX1080 on Pytorch 1.11 with fp32, nhwc, batchsize of 256, input size = `224x224x3`.
`100`	`110`	`For all benchmark results [look here](https://github.com/Coderx7/SimpleNet_Pytorch/tree/master/ImageNet/training_scripts/imagenet_training/results)`

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