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模型仓库

performance tested on Ascend 910(8p) with graph mode
model name params(M) cards batch size resolution jit level graph compile ms/step img/s acc@top1 acc@top5 recipe weight
bit_resnet50 25.55 8 32 224x224 O2 146s 74.52 3413.33 76.81 93.17 yaml weights
cmt_small 26.09 8 128 224x224 O2 1268s 500.64 2048.01 83.24 96.41 yaml weights
coat_tiny 5.50 8 32 224x224 O2 543s 254.95 1003.92 79.67 94.88 yaml weights
convit_tiny 5.71 8 256 224x224 O2 133s 231.62 8827.59 73.66 91.72 yaml weights
convnext_tiny 28.59 8 16 224x224 O2 127s 66.79 1910.45 81.91 95.79 yaml weights
convnextv2_tiny 28.64 8 128 224x224 O2 237s 400.20 2560.00 82.43 95.98 yaml weights
crossvit_9 8.55 8 256 240x240 O2 206s 550.79 3719.30 73.56 91.79 yaml weights
densenet121 8.06 8 32 224x224 O2 191s 43.28 5914.97 75.64 92.84 yaml weights
dpn92 37.79 8 32 224x224 O2 293s 78.22 3272.82 79.46 94.49 yaml weights
dpn92 37.79 8 32 224x224 O2 293s 78.22 3272.82 79.46 94.49 yaml weights
efficientnet_b0 5.33 8 128 224x224 O2 203s 172.78 5926.61 76.89 93.16 yaml weights
ghostnet_050 2.60 8 128 224x224 O2 383s 211.13 4850.09 66.03 86.64 yaml weights
googlenet 6.99 8 32 224x224 O2 72s 21.40 11962.62 72.68 90.89 yaml weights
halonet_50t 22.79 8 64 256x256 O2 261s 421.66 6437.82 79.53 94.79 yaml weights
hrnet_w32 41.30 128 8 224x224 O2 1312s 279.10 3668.94 80.64 95.44 yaml weights
inception_v3 27.20 8 32 299x299 O2 120s 76.42 3349.91 79.11 94.40 yaml weights
inception_v4 42.74 8 32 299x299 O2 177s 76.19 3360.02 80.88 95.34 yaml weights
mixnet_s 4.17 8 128 224x224 O2 556s 252.49 4055.61 75.52 92.52 yaml weights
mnasnet_075 3.20 8 256 224x224 O2 140s 165.43 12379.86 71.81 90.53 yaml weights
mobilenet_v1_025 0.47 8 64 224x224 O2 89s 42.43 12066.93 53.87 77.66 yaml weights
mobilenet_v2_075 2.66 8 256 224x224 O2 164s 155.94 13133.26 69.98 89.32 yaml weights
mobilenet_v3_small_100 2.55 8 75 224x224 O2 145s 48.14 12463.65 68.10 87.86 yaml weights
mobilenet_v3_large_100 5.51 8 75 224x224 O2 271s 47.49 12634.24 75.23 92.31 yaml weights
mobilevit_xx_small 1.27 64 8 256x256 O2 301s 53.52 9566.52 68.91 88.91 yaml weights
nasnet_a_4x1056 5.33 8 256 224x224 O2 656s 330.89 6189.37 73.65 91.25 yaml weights
pit_ti 4.85 8 128 224x224 O2 192s 271.50 3771.64 72.96 91.33 yaml weights
poolformer_s12 11.92 8 128 224x224 O2 118s 220.13 4651.80 77.33 93.34 yaml weights
pvt_tiny 13.23 8 128 224x224 O2 192s 229.63 4459.35 74.81 92.18 yaml weights
pvt_v2_b0 3.67 8 128 224x224 O2 269s 269.38 3801.32 71.50 90.60 yaml weights
regnet_x_800mf 7.26 8 64 224x224 O2 99s 42.49 12049.89 76.04 92.97 yaml weights
repmlp_t224 38.30 8 128 224x224 O2 289s 578.23 1770.92 76.71 93.30 yaml weights
repvgg_a0 9.13 8 32 224x224 O2 50s
 20.58 12439.26 72.19 90.75 yaml weights
repvgg_a1 14.12 8 32 224x224 O2 29s 20.70 12367.15 74.19 91.89 yaml weights
res2net50 25.76 8 32 224x224 O2 119s 39.68 6451.61 79.35 94.64 yaml weights
resnest50 27.55 8 128 224x224 O2 83s 244.92 4552.73 80.81 95.16 yaml weights
resnet50 25.61 8 32 224x224 O2 43s 31.41 8150.27 76.69 93.50 yaml weights
resnetv2_50 25.60 8 32 224x224 O2 52s 32.66 7838.33 76.90 93.37 yaml weights
resnext50_32x4d 25.10 8 32 224x224 O2 49s 37.22 6878.02 78.53 94.10 yaml weights
rexnet_09 4.13 8 64 224x224 O2 462s 130.10 3935.43 77.06 93.41 yaml weights
seresnet18 11.80 8 64 224x224 O2 43s 44.40 11531.53 71.81 90.49 yaml weights
shufflenet_v1_g3_05 0.73 8 64 224x224 O2 169s 40.62 12604.63 57.05 79.73 yaml weights
shufflenet_v2_x0_5 1.37 8 64 224x224 O2 62s 41.87 12228.33 60.53 82.11 yaml weights
skresnet18 11.97 8 64 224x224 O2 60s 45.84 11169.28 73.09 91.20 yaml weights
squeezenet1_0 1.25 8 32 224x224 O2 45s 22.36 11449.02 58.67 80.61 yaml weights
swin_tiny 33.38 8 256 224x224 O2 226s 454.49 4506.15 80.82 94.80 yaml weights
swinv2_tiny_window8 28.78 8 128 256x256 O2 273s 317.19 3228.35 81.42 95.43 yaml weights
vgg13 133.04 8 32 224x224 O2 23s 55.20 4637.68 72.87 91.02 yaml weights
vgg19 143.66 8 32 224x224 O2 22s 67.42 3797.09 75.21 92.56 yaml weights
visformer_tiny 10.33 8 128 224x224 O2 137s 217.92 4698.97 78.28 94.15 yaml weights
volo_d1 27 8 128 224x224 O2 275s 270.79 3781.53 82.59 95.99 yaml weights
xception 22.91 8 32 299x299 O2 161s 96.78 2645.17 79.01 94.25 yaml weights
xcit_tiny_12_p16_224 7.00 8 128 224x224 O2 382s 252.98 4047.75 77.67 93.79 yaml weights
performance tested on Ascend 910*(8p) with graph mode
model name params(M) cards batch size resolution jit level graph compile ms/step img/s acc@top1 acc@top5 recipe weight
convit_tiny 5.71 8 256 224x224 O2 153s 226.51 9022.03 73.79 91.70 yaml weights
convnext_tiny 28.59 8 16 224x224 O2 137s 48.7 2612.24 81.28 95.61 yaml weights
convnextv2_tiny 28.64 8 128 224x224 O2 268s 257.2 3984.44 82.39 95.95 yaml weights
crossvit_9 8.55 8 256 240x240 O2 221s 514.36 3984.44 73.38 91.51 yaml weights
densenet121 8.06 8 32 224x224 O2 300s 47,34 5446.81 75.67 92.77 yaml weights
densenet121 8.06 8 32 224x224 O2 300s 47,34 5446.81 75.67 92.77 yaml weights
efficientnet_b0 5.33 8 128 224x224 O2 353s 172.64 5931.42 76.88 93.28 yaml weights
googlenet 6.99 8 32 224x224 O2 113s 23.5 10893.62 72.89 90.89 yaml weights
googlenet 6.99 8 32 224x224 O2 113s 23.5 10893.62 72.89 90.89 yaml weights
inception_v3 27.20 8 32 299x299 O2 172s 70.83 3614.29 79.25 94.47 yaml weights
inception_v4 42.74 8 32 299x299 O2 263s 80.97 3161.66 80.98 95.25 yaml weights
mixnet_s 4.17 8 128 224x224 O2 706s 228.03 4490.64 75.58 95.54 yaml weights
mnasnet_075 3.20 8 256 224x224 O2 144s 175.85 11646.29 71.77 90.52 yaml weights
mobilenet_v1_025 0.47 8 64 224x224 O2 195s 47.47 10785.76 54.05 77.74 yaml weights
mobilenet_v2_075 2.66 8 256 224x224 O2 233s 174.65 11726.31 69.73 89.35 yaml weights
mobilenet_v3_small_100 2.55 8 75 224x224 O2 184s 52.38 11454.75 68.07 87.77 yaml weights
mobilenet_v3_large_100 5.51 8 75 224x224 O2 354s 55.89 10735.37 75.59 92.57 yaml weights
mobilevit_xx_small 1.27 8 64 256x256 O2 437s 67.24 7614.52 67.11 87.85 yaml weights
nasnet_a_4x1056 5.33 8 256 224x224 O2 800s 364.35 5620.97 74.12 91.36 yaml weights
pit_ti 4.85 8 128 224x224 O2 212s 266.47 3842.83 73.26 91.57 yaml weights
poolformer_s12 11.92 8 128 224x224 O2 177s 211.81 4834.52 77.49 93.55 yaml weights
pvt_tiny 13.23 8 128 224x224 O2 212s 237.5 4311.58 74.88 92.12 yaml weights
pvt_v2_b0 3.67 8 128 224x224 O2 323s 255.76 4003.75 71.25 90.50 yaml weights
regnet_x_800mf 7.26 8 64 224x224 O2 228s 50.74 10090.66 76.11 93.00 yaml weights
repmlp_t224 38.30 8 128 224x224 O2 289s 578.23 1770.92 76.71 93.30 yaml weights
repvgg_a0 9.13 8 32 224x224 O2 76s 24.12 10613.60 72.29 90.78 yaml weights
repvgg_a1 14.12 8 32 224x224 O2 81s 28.29 9096.13 73.68 91.51 yaml weights
res2net50 25.76 8 32 224x224 O2 174s 39.6 6464.65 79.33 94.64 yaml weights
resnet50 25.61 8 32 224x224 O2 77s 31.9 8025.08 76.76 93.31 yaml weights
resnetv2_50 25.60 8 32 224x224 O2 120s 32.19 7781.16 77.03 93.29 yaml weights
resnext50_32x4d 25.10 8 32 224x224 O2 156s 44.61 5738.62 78.64 94.18 yaml weights
rexnet_09 4.13 8 64 224x224 O2 515s 115.61 3290.28 76.14 92.96 yaml weights
seresnet18 11.80 8 64 224x224 O2 90s 51.09 10021.53 72.05 90.59 yaml weights
shufflenet_v1_g3_05 0.73 8 64 224x224 O2 191s 47.77 10718.02 57.08 79.89 yaml weights
shufflenet_v2_x0_5 1.37 8 64 224x224 O2 100s 47.32 10819.95 60.65 82.26 yaml weights
skresnet18 11.97 8 64 224x224 O2 134s 49.83 10274.93 72.85 90.83 yaml weights
squeezenet1_0 1.25 8 32 224x224 O2 64s 23.48 10902.90 58.75 80.76 yaml weights
swin_tiny 33.38 8 256 224x224 O2 266s 466.6 4389.20 80.90 94.90 yaml weights
swinv2_tiny_window8 28.78 8 128 256x256 O2 385s 335.18 3055.07 81.38 95.46 yaml weights
vgg13 133.04 8 32 224x224 O2 41s 30.52 8387.94 72.81 91.02 yaml weights
vgg19 143.66 8 32 224x224 O2 53s 39.17 6535.61 75.24 92.55 yaml weights
visformer_tiny 10.33 8 128 224x224 O2 169s 201.14 5090.98 78.40 94.30 yaml weights
xcit_tiny_12_p16_224 7.00 8 128 224x224 O2 330s 229.25 4466.74 77.27 93.56 yaml weights

Notes

  • top-1 and top-5: Accuracy reported on the validation set of ImageNet-1K.