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update readme and clean benchmark results

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`‎README.md`

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`@@ -53,15 +53,15 @@ Small variants of simplenet consistently achieve high performance/accuracy:`
`53`	`53`
`54`	`54`	`\| model \| samples_per_sec \| param_count \| top1 \| top5 \|`
`55`	`55`	`\|:----------------------------------\| :--------------: \| :-----------: \| :--: \| :---: \|`
`56`		`-\|simplenetv1_small_m1_05 \| 3100.26 \| 1.51 \|61.122 \| 82.988 \|`
	`56`	`+\|simplenetv1_small_m1_05 \| 3100.26 \| 1.51 \|61.122 \| 82.988 \|`
`57`	`57`	`\|mobilenetv3_small_050 \| 3082.85 \| 1.59 \| 57.89 \| 80.194 \|`
`58`	`58`	`\|lcnet_050 \| 2713.02 \| 1.88 \| 63.1 \| 84.382 \|`
`59`		`-\|simplenetv1_small_m2_05 \| 2536.16 \| 1.51 \|61.67\|83.488 \|`
	`59`	`+\|simplenetv1_small_m2_05 \| 2536.16 \| 1.51 \|61.67\|83.488 \|`
`60`	`60`	`\|mobilenetv3_small_075 \| 1793.42 \| 2.04 \| 65.242 \| 85.438 \|`
`61`	`61`	`\|tf_mobilenetv3_small_075 \| 1689.53 \| 2.04 \| 65.714 \| 86.134 \|`
`62`		`-\|simplenetv1_small_m1_075 \| 1626.87 \| 3.29 \|67.784 \| 87.718 \|`
	`62`	`+\|simplenetv1_small_m1_075 \| 1626.87 \| 3.29 \|67.784 \| 87.718 \|`
`63`	`63`	`\|tf_mobilenetv3_small_minimal_100 \| 1316.91 \| 2.04 \| 62.908 \| 84.234 \|`
`64`		`-\|simplenetv1_small_m2_075 \| 1313.6 \| 3.29 \|68.506 \| 88.15 \|`
	`64`	`+\|simplenetv1_small_m2_075 \| 1313.6 \| 3.29 \|68.506 \| 88.15 \|`
`65`	`65`	`\|mobilenetv3_small_100 \| 1261.09 \| 2.54 \| 67.656 \| 87.634 \|`
`66`	`66`	`\|tf_mobilenetv3_small_100 \| 1213.03 \| 2.54 \| 67.924 \| 87.664 \|`
`67`	`67`	`\|mnasnet_small \| 1089.33 \| 2.03 \| 66.206 \| 86.508 \|`
`@@ -74,11 +74,11 @@ and this is a sample for larger models: simplenet variants outperform many newer`
`74`	`74`
`75`	`75`	`\| model \| samples_per_sec \| param_count \| top1 \| top5 \|`
`76`	`76`	`\|:----------------------------------\| :--------------: \| :-----------: \| :----: \| :----: \|`
`77`		`-\| simplenetv1_small_m1_075 \| 2893.91 \| 3.29\|67.784 \| 87.718 \|`
`78`		`-\| simplenetv1_small_m2_075 \| 2478.41 \| 3.29\|68.506 \| 88.15 \|`
	`77`	`+\| simplenetv1_small_m1_075 \| 2893.91 \| 3.29\|67.784 \| 87.718 \|`
	`78`	`+\| simplenetv1_small_m2_075 \| 2478.41 \| 3.29\|68.506 \| 88.15 \|`
`79`	`79`	`\| vit_tiny_r_s16_p8_224 \| 2337.23 \| 6.34 \| 71.792 \| 90.822 \|`
`80`		`-\| simplenetv1_5m_m1 \| 2105.06 \| 5.75\|71.548 \| 89.94 \|`
`81`		`-\| simplenetv1_5m_m2 \| 1754.25 \| 5.75\|72.03 \| 90.324 \|`
	`80`	`+\| simplenetv1_5m_m1 \| 2105.06 \| 5.75\|71.548 \| 89.94 \|`
	`81`	`+\| simplenetv1_5m_m2 \| 1754.25 \| 5.75\|72.03 \| 90.324 \|`
`82`	`82`	`\| resnet18 \| 1750.38 \| 11.69 \| 69.744 \| 89.082 \|`
`83`	`83`	`\| regnetx_006 \| 1620.25 \| 6.2 \| 73.86 \| 91.672 \|`
`84`	`84`	`\| mobilenetv3_large_100 \| 1491.86 \| 5.48 \| 75.766 \| 92.544 \|`
`@@ -89,11 +89,11 @@ and this is a sample for larger models: simplenet variants outperform many newer`
`89`	`89`	`\| tf_mobilenetv3_large_100 \| 1325.06 \| 5.48 \| 75.518 \| 92.604 \|`
`90`	`90`	`\| mnasnet_100 \| 1183.69 \| 4.38 \| 74.658 \| 92.112 \|`
`91`	`91`	`\| mobilenetv2_100 \| 1101.58 \| 3.5 \| 72.97 \| 91.02 \|`
`92`		`-\| simplenetv1_9m_m1 \| 1048.91 \| 9.51\|73.792 \| 91.486 \|`
	`92`	`+\| simplenetv1_9m_m1 \| 1048.91 \| 9.51\|73.792 \| 91.486 \|`
`93`	`93`	`\| resnet34 \| 1030.4 \| 21.8 \| 75.114 \| 92.284 \|`
`94`	`94`	`\| deit_tiny_patch16_224 \| 990.85 \| 5.72 \| 72.172 \| 91.114 \|`
`95`	`95`	`\| efficientnet_lite0 \| 977.76 \| 4.65 \| 75.476 \| 92.512 \|`
`96`		`-\| simplenetv1_9m_m2 \| 900.45 \| 9.51\|74.23 \| 91.748 \|`
	`96`	`+\| simplenetv1_9m_m2 \| 900.45 \| 9.51\|74.23 \| 91.748 \|`
`97`	`97`	`\| tf_efficientnet_lite0 \| 876.66 \| 4.65 \| 74.832 \| 92.174 \|`
`98`	`98`	`\| dla34 \| 834.35 \| 15.74 \| 74.62 \| 92.072 \|`
`99`	`99`	`\| mobilenetv2_110d \| 824.4 \| 4.52 \| 75.038 \| 92.184 \|`

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