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`‎README.md`

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`@@ -18,6 +18,9 @@ The pytorch implementation is also very effieicent and the whole model takes onl`
`18`	`18`	`#### Update History:`
`19`	`19`
`20`	`20`	`<pre>`
	`21`	`+-- 2023 Apr 13:`
	`22`	`+ -- new weights for the removed paddings for 1x1 conv layers.`
	`23`	`+ -- some minor fixes`
`21`	`24`	`-- 2023 Feb 12:`
`22`	`25`	-- re-structured the repository, moving the old implementation into new directory named 'Cifar` and imagenet into its respective directory
`23`	`26`	`-- updated the old implementation to work with latest version of pytorch.`
`@@ -33,12 +36,12 @@ The original Caffe implementation can be found here : [Original Caffe implementa`
`33`	`36`
`34`	`37`	`#### ImageNet Result:`
`35`	`38`
`36`		`-\| Method \| \#Params \| ImageNet \| ImageNet-Real-Labels \|`
`37`		`-\| :--------------------------- \| :----------: \| :-----------: \|:-----------: \|`
`38`		`-\| SimpleNetV1_imagenet(36.33 MB) \| 9.5m \| 74.17/91.614 \|81.24/94.63 \|`
`39`		`-\| SimpleNetV1_imagenet(21.9 MB) \| 5.7m \| 71.936/90.3 \|79.12/93.68 \|`
`40`		`-\| SimpleNetV1_imagenet(12.58 MB)\| 3m \| 68.15/87.762 \| 75.66/91.80 \|`
`41`		`-\| SimpleNetV1_imagenet(5.78 MB) \| 1.5m \| 61.524/83.43 \|69.11/88.10 \|`
	`39`	`+\| Model \| \#Params \| ImageNet \| ImageNet-Real-Labels \|`
	`40`	`+\| :--------------------------- \| :----------: \| :-----------: \|:------------------: \|`
	`41`	`+\| simplenetv1_9m_m2(36.3 MB) \| 9.5m \| 74.23 / 91.748 \|81.22 / 94.756 \|`
	`42`	`+\| simplenetv1_5m_m2(22 MB) \| 5.7m \| 72.03 / 90.324 \|79.328/ 93.714 \|`
	`43`	`+\| simplenetv1_small_m2_075(12.6 MB)\| 3m \| 68.506/ 88.15 \| 76.283/ 92.02 \|`
	`44`	`+\| simplenetv1_small_m2_05(5.78 MB) \| 1.5m \| 61.67 / 83.488 \|69.31 / 88.195 \|`
`42`	`45`
`43`	`46`	`SimpleNet performs very decently, it outperforms VGGNet, variants of ResNet and MobileNets(1-3)`
`44`	`47`	`and its pretty fast as well! and its all using plain old CNN!.`
`@@ -48,65 +51,65 @@ Small variants of simplenet consistently achieve high performance/accuracy:`
`48`	`51`
`49`	`52`	`\| model \| samples_per_sec \| param_count \| top1 \| top5 \|`
`50`	`53`	`\|:----------------------------------\| :--------------: \| :-----------: \| :--: \| :---: \|`
`51`		`-\|mobilenetv3_small_050 \| 3035.37 \| 1.59 \| 57.89 \| 80.194 \|`
`52`		`-\|simplenetv1_small_m1_05 \| 2839.35 \| 1.51 \| 60.89\|82.978\|`
`53`		`-\|lcnet_050 \| 2683.57 \| 1.88 \| 63.1 \| 84.382 \|`
`54`		`-\|simplenetv1_small_m2_05 \| 2340.51 \| 1.51 \|61.524\|83.432\|`
`55`		`-\|mobilenetv3_small_075 \| 1781.14 \| 2.04 \| 65.242 \| 85.438 \|`
`56`		`-\|tf_mobilenetv3_small_075 \| 1674.31 \| 2.04 \| 65.714 \| 86.134 \|`
`57`		`-\|simplenetv1_small_m1_075 \| 1524.64 \| 3.29 \|67.764\|87.66 \|`
`58`		`-\|tf_mobilenetv3_small_minimal_100 \| 1308.27 \| 2.04 \| 62.908 \| 84.234 \|`
`59`		`-\|simplenetv1_small_m2_075 \| 1264.33 \| 3.29 \|68.15\|87.762\|`
`60`		`-\|mobilenetv3_small_100 \| 1263.23 \| 2.54 \| 67.656 \| 87.634 \|`
`61`		`-\|tf_mobilenetv3_small_100 \| 1220.08 \| 2.54 \| 67.924 \| 87.664 \|`
`62`		`-\|mnasnet_small \| 1085.15 \| 2.03 \| 66.206 \| 86.508 \|`
`63`		`-\|mobilenetv2_050 \| 848.38 \| 1.97 \| 65.942 \| 86.082 \|`
`64`		`-\|dla46_c \| 531.0 \| 1.3 \| 64.866 \| 86.294 \|`
`65`		`-\|dla46x_c \| 318.32 \| 1.07 \| 65.97 \| 86.98 \|`
`66`		`-\|dla60x_c \| 298.59 \| 1.32 \| 67.892 \| 88.426 \|`
	`54`	`+\|simplenetv1_small_m1_05 \| 3100.26 \| 1.51 \| 61.122 \| 82.988 \|`
	`55`	`+\|mobilenetv3_small_050 \| 3082.85 \| 1.59 \| 57.89\| 80.194 \|`
	`56`	`+\|lcnet_050 \| 2713.02 \| 1.88 \| 63.1 \| 84.382 \|`
	`57`	`+\|simplenetv1_small_m2_05 \| 2536.16 \| 1.51 \|61.67 \|83.488 \|`
	`58`	`+\|mobilenetv3_small_075 \| 1793.42 \| 2.04 \| 65.242 \| 85.438 \|`
	`59`	`+\|tf_mobilenetv3_small_075 \| 1689.53 \| 2.04 \| 65.714 \| 86.134 \|`
	`60`	`+\|simplenetv1_small_m1_075 \| 1626.87 \| 3.29 \|67.784 \|87.718 \|`
	`61`	`+\|tf_mobilenetv3_small_minimal_100 \| 1316.91 \| 2.04 \| 62.908 \| 84.234 \|`
	`62`	`+\|simplenetv1_small_m2_075 \| 1313.6 \| 3.29 \|68.506 \| 88.15 \|`
	`63`	`+\|mobilenetv3_small_100 \| 1261.09 \| 2.54 \| 67.656 \| 87.634 \|`
	`64`	`+\|tf_mobilenetv3_small_100 \| 1213.03 \| 2.54 \| 67.924 \| 87.664 \|`
	`65`	`+\|mnasnet_small \| 1089.33 \| 2.03 \| 66.206 \| 86.508 \|`
	`66`	`+\|mobilenetv2_050 \| 857.66 \| 1.97 \| 65.942 \| 86.082 \|`
	`67`	`+\|dla46_c \| 537.08 \| 1.3 \| 64.866 \| 86.294 \|`
	`68`	`+\|dla46x_c \| 323.03 \| 1.07 \| 65.97 \| 86.98 \|`
	`69`	`+\|dla60x_c \| 301.71 \| 1.32 \| 67.892 \| 88.426 \|`
`67`	`70`
`68`	`71`	`and this is a sample for larger models: simplenet variants outperform many newer architecures.`
`69`	`72`
`70`		`-\| model \| samples_per_sec \| param_count \| top1 \| top5 \|`
`71`		`-\|:----------------------------------\| :--------------: \| :-----------: \| :--: \| :---: \|`
`72`		`-\| vit_tiny_r_s16_p8_224 \| 1882.23 \| 6.34 \| 71.792 \| 90.822 \|`
`73`		`-\| simplenetv1_small_m1_075 \| 1516.74 \| 3.29 \| 67.764 \| 87.660 \|`
`74`		`-\| simplenetv1_small_m2_075 \| 1260.89 \| 3.29 \| 68.150 \| 87.762 \|`
`75`		`-\| simplenetv1_5m_m1 \| 1107.70 \| 5.75 \| 71.370 \| 90.100 \|`
`76`		`-\| deit_tiny_patch16_224 \| 991.41 \| 5.72 \| 72.172 \| 91.114 \|`
`77`		`-\| resnet18 \| 876.92 \| 11.69 \| 69.744 \| 89.082 \|`
`78`		`-\| simplenetv1_5m_m2 \| 835.17 \| 5.75 \| 71.936 \| 90.300 \|`
`79`		`-\| crossvit_9_240 \| 602.13 \| 8.55 \| 73.960 \| 91.968 \|`
`80`		`-\| vit_base_patch32_224_sam \| 571.37 \| 88.22 \| 73.694 \| 91.010 \|`
`81`		`-\| tinynet_b \| 530.15 \| 3.73 \| 74.976 \| 92.184 \|`
`82`		`-\| resnet26 \| 524.36 \| 16.00 \| 75.300 \| 92.578 \|`
`83`		`-\| tf_mobilenetv3_large_075 \| 505.13 \| 3.99 \| 73.436 \| 91.344 \|`
`84`		`-\| resnet34 \| 491.96 \| 21.80 \|75.114 \| 92.284 \|`
`85`		`-\| regnetx_006 \| 478.41 \| 6.20 \| 73.860 \| 91.672 \|`
`86`		`-\| dla34 \| 472.49 \| 15.74 \| 74.620 \| 92.072 \|`
`87`		`-\| simplenetv1_9m_m1 \| 459.21 \| 9.51 \| 73.376 \| 91.048 \|`
`88`		`-\| repvgg_b0 \| 455.36 \| 15.82 \| 75.160 \| 92.418 \|`
`89`		`-\| ghostnet_100 \| 407.03 \| 5.18 \| 73.974 \| 91.460 \|`
`90`		`-\| tf_mobilenetv3_large_minimal_100 \| 406.84 \| 3.92 \| 72.250 \| 90.630 \|`
`91`		`-\| mobilenetv3_large_100 \| 402.08 \| 5.48 \| 75.766 \| 92.544 \|`
`92`		`-\| simplenetv1_9m_m2 \| 389.94 \| 9.51 \| 74.170 \| 91.614 \|`
`93`		`-\| tf_mobilenetv3_large_100 \| 388.30 \| 5.48 \| 75.518 \| 92.604 \|`
`94`		`-\| mobilenetv2_100 \| 295.68 \| 3.50 \| 72.970 \| 91.020 \|`
`95`		`-\| densenet121 \| 293.94 \| 7.98 \| 75.584 \| 92.652 \|`
`96`		`-\| mnasnet_100 \| 262.25 \| 4.38 \| 74.658 \| 92.112 \|`
`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 \|`
`108`		`-`
`109`		-Benchmark was done using a GTX1080 on Pytorch 1.11 with fp32, nhwc, batchsize of 256, input size = `224x224x3`.
	`73`	`+\| model \| samples_per_sec \| param_count \| top1 \| top5 \|`
	`74`	`+\|:----------------------------------\| :--------------: \| :-----------: \| :----: \| :----: \|`
	`75`	`+\| simplenetv1_small_m1_075 \| 3036.82 \| 3.29 \| 67.784 \| 87.718 \|`
	`76`	`+\| simplenetv1_small_m2_075 \| 2589.4 \| 3.29 \| 68.506 \| 88.15 \|`
	`77`	`+\| vit_tiny_r_s16_p8_224 \| 2419.27 \| 6.34 \| 71.792 \| 90.822 \|`
	`78`	`+\| simplenetv1_5m_m1 \| 2204.86 \| 5.75 \| 71.548 \| 89.94 \|`
	`79`	`+\| resnet18 \| 1824.39 \| 11.69 \| 69.744 \| 89.082 \|`
	`80`	`+\| simplenetv1_5m_m2 \| 1821.86 \| 5.75 \| 72.03 \| 90.324 \|`
	`81`	`+\| regnetx_006 \| 1710.9 \| 6.2 \| 73.86 \| 91.672 \|`
	`82`	`+\| mobilenetv3_large_100 \| 1568.72 \| 5.48 \| 75.766 \| 92.544 \|`
	`83`	`+\| tf_mobilenetv3_large_minimal_100 \| 1564.4 \| 3.92 \| 72.25 \| 90.63 \|`
	`84`	`+\| tf_mobilenetv3_large_075 \| 1560.84 \| 3.99 \| 73.436 \| 91.344 \|`
	`85`	`+\| ghostnet_100 \| 1447.97 \| 5.18 \| 73.974 \| 91.46 \|`
	`86`	`+\| tinynet_b \| 1413.59 \| 3.73 \| 74.976 \| 92.184 \|`
	`87`	`+\| tf_mobilenetv3_large_100 \| 1396.66 \| 5.48 \| 75.518 \| 92.604 \|`
	`88`	`+\| mnasnet_100 \| 1238.11 \| 4.38 \| 74.658 \| 92.112 \|`
	`89`	`+\| mobilenetv2_100 \| 1158.3 \| 3.5 \| 72.97 \| 91.02 \|`
	`90`	`+\| simplenetv1_9m_m1 \| 1089.47 \| 9.51 \| 73.792 \| 91.486 \|`
	`91`	`+\| resnet34 \| 1071.63 \| 21.8 \| 75.114 \| 92.284 \|`
	`92`	`+\| efficientnet_lite0 \| 1020.15 \| 4.65 \| 75.476 \| 92.512 \|`
	`93`	`+\| deit_tiny_patch16_224 \| 1007.63 \| 5.72 \| 72.172 \| 91.114 \|`
	`94`	`+\| simplenetv1_9m_m2 \| 933.93 \| 9.51 \| 74.23 \| 91.748 \|`
	`95`	`+\| tf_efficientnet_lite0 \| 920.71 \| 4.65 \| 74.832 \| 92.174 \|`
	`96`	`+\| dla34 \| 871.88 \| 15.74 \| 74.62 \| 92.072 \|`
	`97`	`+\| mobilenetv2_110d \| 867.18 \| 4.52 \| 75.038 \| 92.184 \|`
	`98`	`+\| resnet26 \| 809.01 \| 16 \| 75.3 \| 92.578 \|`
	`99`	`+\| repvgg_b0 \| 788.45 \| 15.82 \| 75.16 \| 92.418 \|`
	`100`	`+\| crossvit_9_240 \| 618.98 \| 8.55 \| 73.96 \| 91.968 \|`
	`101`	`+\| vgg11 \| 602.05 \| 132.86 \| 69.028 \| 88.626 \|`
	`102`	`+\| vgg11_bn \| 522.94 \| 132.87 \| 70.36 \| 89.802 \|`
	`103`	`+\| vit_base_patch32_224_sam \| 514.09 \| 88.22 \| 73.694 \| 91.01 \|`
	`104`	`+\| densenet121 \| 460.65 \| 7.98 \| 75.584 \| 92.652 \|`
	`105`	`+\| vgg13 \| 373.71 \| 133.05 \| 69.926 \| 89.246 \|`
	`106`	`+\| vgg13_bn \| 326.26 \| 133.05 \| 71.594 \| 90.376 \|`
	`107`	`+\| vgg16 \| 312.99 \| 138.36 \| 71.59 \| 90.382 \|`
	`108`	`+\| vgg16_bn \| 276.35 \| 138.37 \| 73.35 \| 91.504 \|`
	`109`	`+\| vgg19 \| 269.28 \| 143.67 \| 72.366 \| 90.87 \|`
	`110`	`+\| vgg19_bn \| 239.6 \| 143.68 \| 74.214 \| 91.848 \|`
	`111`	`+`
	`112`	+Benchmark was done using a GTX1080 on Pytorch 1.11 with fp32, nchw, batchsize of 256, input size = `224x224x3`.
`110`	`113`	`For all benchmark results [look here](https://github.com/Coderx7/SimpleNet_Pytorch/tree/master/ImageNet/training_scripts/imagenet_training/results)`
`111`	`114`
`112`	`115`	`-- The models pretrained weights (pytorch, onnx, jit) can be found in [Release section](https://github.com/Coderx7/SimpleNet_Pytorch/releases)`

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