EdgeBoard支持的模型
更新时间:2022-06-23
目前,EdgeBoard主要支持视觉类模型,包含分类、检测、分割以及特定场景模型。
分类模型
模型为PaddleClas release/static的ModelZoo,github地址:https://github.com/PaddlePaddle/PaddleClas/tree/release/static
以下数据的测试环境:
模型训练环境:PaddlePaddle = 1.8.5、PaddleClas = release/static
模型部署环境:EdgeBoard 软核1.8.1、系统Ubuntu18.04
ResNet and Vd series
ResNet model | Top-1 Acc by PaddleClas | Top-5 Acc by PaddleClas | Top-1 Acc by 3A | Top-5 Acc by 3A | time by 3A(ms) | Top-1 Acc by 3B | Top-5 Acc by 3B | time by 3B(ms) | Top-1 Acc by 5D | Top-5 Acc by 5D | time by 5D(ms) | Top-1 Acc by 9D | Top-5 Acc by 9D | time by 9D(ms) |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
ResNet18 | 0.7098 | 0.8992 | 0.7096 | 0.8991 | 14.71548588 | 0.733 | 0.914 | 8.95363345 | 0.7097 | 0.8988 | 8.95363345 | 0.7097 | 0.8988 | 6.40590735 |
ResNet18_vd | 0.7226 | 0.908 | 0.723 | 0.908 | 16.02208967 | 0.7223 | 0.9081 | 9.83267931 | 0.7223 | 0.9081 | 17.8796712 | 0.7221 | 0.908 | 7.09548706 |
ResNet34 | 0.7457 | 0.9214 | 0.7441 | 0.9211 | 26.98735892 | 0.7441 | 0.921 | 15.6827087 | 0.7441 | 0.921 | 15.6827087 | 0.7437 | 0.9211 | 10.6187022 |
ResNet34_vd | 0.7598 | 0.9298 | 0.7586 | 0.9303 | 28.36009001 | 0.7582 | 0.93 | 16.5780194 | 0.7582 | 0.93 | 16.5780194 | 0.7587 | 0.9298 | 11.3229563 |
ResNet50 | 0.765 | 0.93 | 0.7637 | 0.9294 | 34.05404204 | 0.7641 | 0.9294 | 21.732892 | 0.7641 | 0.9294 | 21.732892 | 0.7639 | 0.9296 | 16.6979658 |
ResNet50_vc | 0.7835 | 0.9403 | 0.7827 | 0.9398 | 35.21767119 | 0.7903 | 0.9446 | 22.9020555 | 0.7903 | 0.9446 | 22.9020555 | 0.7823 | 0.9398 | 17.2306128 |
ResNet50_vd | 0.7912 | 0.9444 | 0.7904 | 0.9446 | 35.71911547 | 0.7741 | 0.9365 | 37.4155384 | 0.7741 | 0.9365 | 37.4155384 | 0.7903 | 0.9444 | 17.6102096 |
ResNet101 | 0.7756 | 0.9364 | 0.7742 | 0.9366 | 59.52945458 | 0.8013 | 0.9493 | 38.5317895 | 0.8013 | 0.9493 | 38.5317895 | 0.7742 | 0.9361 | 27.3622326 |
ResNet101_vd | 0.8017 | 0.9497 | 0.8014 | 0.9495 | 61.17122603 | 0.7823 | 0.939 | 53.0845316 | 0.7823 | 0.939 | 53.0845316 | 0.8014 | 0.9491 | 28.3110359 |
ResNet152 | 0.7826 | 0.9396 | 0.7827 | 0.9393 | 85.05916077 | 0.8041 | 0.9523 | 54.2026246 | 0.8041 | 0.9523 | 54.2026246 | 0.782 | 0.939 | 37.6998016 |
ResNet152_vd | 0.8059 | 0.953 | 0.8041 | 0.9523 | 86.59876956 | 0.8075 | 0.9534 | 68.9850622 | 0.8075 | 0.9534 | 68.9850622 | 0.8043 | 0.9522 | 38.5713139 |
ResNet200_vd | 0.8093 | 0.9533 | 0.807 | 0.9537 | 110.5979351 | 0.8069 | 0.9538 | 49.9698168 | 0.8069 | 0.9538 | 49.9698168 | 0.8069 | 0.9538 | 49.9698168 |
Mobile series
Model | Top-1 Acc by PaddleClas | Top-5 Acc by PaddleClas | Top-1 Acc by 3A | Top-5 Acc by 3B | time by 3B(ms) | Top-1 Acc by 3B | Top-5 Acc by 3B | time by 3B(ms) | Top1 Acc by 5C | Top-5 Acc by 5C | time by 5C(ms) | Top-1 Acc by 5D | Top-5 Acc by 5D | time by 5D(ms) | Top-1 Acc by 9D | Top-5 Acc by 9D | time by 9D(ms) |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
MobileNetV1_x0_25 | 0.5143 | 0.7546 | 0.5025 | 0.7443 | 3.68112385 | 0.5025 | 0.7443 | 3.47995238 | 0.5025 | 0.7443 | 4.05508755 | 0.5025 | 0.7443 | 4.05508755 | 0.5025 | 0.7443 | 3.47995238 |
MobileNetV1_x0_5 | 0.6352 | 0.8473 | 0.6325 | 0.8469 | 4.97116533 | 0.6325 | 0.8469 | 4.35903775 | 0.6325 | 0.8469 | 5.05141552 | 0.6325 | 0.8469 | 5.05141552 | 0.6325 | 0.8469 | 4.35903774 |
MobileNetV1_x0_75 | 0.6881 | 0.8823 | 0.6838 | 0.8803 | 7.60554075 | 0.6843 | 0.8802 | 6.29608946 | 0.6843 | 0.8802 | 7.24652382 | 0.6843 | 0.8802 | 7.24652382 | 0.6843 | 0.8802 | 6.29608946 |
MobileNetV1 | 0.7099 | 0.8968 | 0.7079 | 0.8956 | 9.34210897 | 0.708 | 0.8954 | 7.22738171 | 0.708 | 0.8954 | 8.4207836 | 0.708 | 0.8954 | 8.4207836 | 0.708 | 0.8954 | 7.05951087 |
MobileNetV2_x0_25 | 0.5321 | 0.7652 | 0.5273 | 0.7616 | 7.74662709 | 0.5269 | 0.7616 | 7.09189643 | 0.5273 | 0.7617 | 8.24141499 | 0.5273 | 0.7617 | 8.24141499 | 0.5269 | 0.7616 | 6.96900913 |
MobileNetV2_x0_5 | 0.6503 | 0.8572 | 0.6453 | 0.8541 | 7.4191766 | 0.5135 | 0.6808 | 6.86391964 | 0.6451 | 0.8539 | 8.13312565 | 0.6451 | 0.8539 | 8.13312565 | 0.5936 | 0.7877 | 6.75820331 |
MobileNetV2_x0_75 | 0.6983 | 0.8901 | 0.6959 | 0.8879 | 12.7507112 | 0.6959 | 0.8878 | 11.5829853 | 0.696 | 0.8878 | 13.0029739 | 0.696 | 0.8878 | 13.0029739 | 0.6959 | 0.8878 | 11.3544805 |
MobileNetV2 | 0.7215 | 0.9065 | 0.7186 | 0.9052 | 11.0632651 | 0.7188 | 0.9051 | 9.86122213 | 0.7188 | 0.9052 | 11.3809789 | 0.7188 | 0.9052 | 11.3809789 | 0.7188 | 0.9051 | 9.66140689 |
MobileNetV2_x1_5 | 0.7412 | 0.9167 | 0.7385 | 0.9155 | 17.1437208 | 0.7345 | 0.9133 | 14.5713013 | 0.7345 | 0.9133 | 16.5544266 | 0.7345 | 0.9133 | 16.5544266 | 0.8014 | 0.9491 | 14.2934955 |
MobileNetV2_x2_0 | 0.7523 | 0.9258 | 0.7386 | 0.9154 | 22.0435947 | 0.3747 | 0.6042 | 17.6118443 | 0.7384 | 0.9153 | 20.1993121 | 0.7384 | 0.9153 | 20.1993121 | 0.7826 | 0.9397 | 17.1324282 |
GhostNet_x1_0 | 0.7402 | 0.9165 | 0.4417 | 0.677 | 34.1087561 | 0.5221 | 0.7592 | 30.9889381 | 0.3239 | 0.5406 | 33.5847206 | 0.3239 | 0.5406 | 33.5847206 | 0.5221 | 0.7592 | 30.9889381 |
SEResNeXt and Res2Net series
Model | Top-1 Acc by PaddleClas | Top-5 Acc by PaddleClas | Top-1 Acc by 5D | Top-5 Acc by 5D | time by 5D(ms) | Top-1 Acc by 9D | Top-5 Acc by 9D | time by 9D(ms) |
---|---|---|---|---|---|---|---|---|
Res2Net50_26w_4s | 0.7933 | 0.9457 | 0.7923 | 0.945 | 46.9715829 | 0.7923 | 0.945 | 41.3515309 |
Res2Net50_vd_26w_4s | 0.7975 | 0.9491 | 0.797 | 0.9484 | 48.1105378 | 0.7969 | 0.9485 | 42.0706068 |
Res2Net50_14w_8s | 0.7946 | 0.947 | 0.7875 | 0.9414 | 64.7512679 | 0.7873 | 0.9412 | 57.5119234 |
Res2Net101_vd_26w_4s | 0.8064 | 0.9522 | 0.7938 | 0.9393 | 74.3632303 | 0.7933 | 0.9394 | 62.2968839 |
Res2Net200_vd_26w_4s | 0.8121 | 0.9571 | 0.7967 | 0.9418 | 132.182714 | 0.7967 | 0.9413 | 108.708479 |
ResNeXt50_32x4d | 0.7775 | 0.9382 | 0.7757 | 0.937 | 27.6730268 | 0.7675 | 0.9281 | 22.2482438 |
ResNeXt50_vd_32x4d | 0.7956 | 0.9462 | 0.7757 | 0.937 | 27.6730268 | 0.74 | 0.96 | 23.0841588 |
ResNeXt50_64x4d | 0.7843 | 0.9413 | 0.7836 | 0.9403 | 44.1621114 | 0.7843 | 0.9413 | 52.8567324 |
ResNeXt50_vd_64x4d | 0.8012 | 0.9486 | 0.8002 | 0.9483 | 45.2178389 | 0.8012 | 0.9486 | 54.1873319 |
ResNeXt101_32x4d | 0.7865 | 0.9419 | 0.7854 | 0.9415 | 44.4215912 | 0.7865 | 0.9419 | 33.8506747 |
ResNeXt101_vd_32x4d | 0.8033 | 0.9512 | 0.8013 | 0.9501 | 45.5457859 | 0.8033 | 0.9512 | 34.8075458 |
ResNeXt101_64x4d | 0.7835 | 0.9452 | 0.7925 | 0.9452 | 72.0490677 | 0.792 | 0.9449 | 82.8039959 |
ResNeXt101_vd_64x4d | 0.8078 | 0.952 | 0.8027 | 0.9472 | 73.287074 | 0.8078 | 0.952 | 81.9382522 |
ResNeXt152_32x4d | 0.7898 | 0.9433 | 0.7888 | 0.943 | 62.0476544 | 0.7889 | 0.9432 | 48.0175154 |
ResNeXt152_vd_32x4d | 0.8072 | 0.952 | 0.8063 | 0.9519 | 63.1244978 | 0.8072 | 0.952 | 47.7385326 |
ResNeXt152_64x4d | 0.7951 | 0.9471 | 0.7937 | 0.946 | 101.227566 | 0.7951 | 0.9471 | 113.319645 |
ResNeXt152_vd_64x4d | 0.8108 | 0.9534 | 0.8102 | 0.9532 | 102.414722 | 0.8107 | 0.9533 | 113.768651 |
SE_ResNet18_vd | 0.7333 | 0.9138 | 0.733 | 0.9142 | 12.8133037 | 0.7334 | 0.913 | 9.29966265 |
SE_ResNet34_vd | 0.7651 | 0.932 | 0.7641 | 0.9313 | 22.3687792 | 0.7651 | 0.932 | 15.6554105 |
SE_ResNet50_vd | 0.7952 | 0.9475 | 0.7946 | 0.9473 | 34.68155 | 0.7952 | 0.9475 | 27.4156064 |
SE_ResNeXt50_32x4d | 0.7844 | 0.9396 | 0.7832 | 0.9396 | 39.3880242 | 0.7844 | 0.9396 | 32.3298221 |
SE_ResNeXt50_vd_32x4d | 0.8024 | 0.9489 | 0.8024 | 0.9489 | 42.8019707 | 0.7922 | 0.9386 | 34.4062971 |
SE_ResNeXt101_32x4d | 0.7912 | 0.942 | 0.7924 | 0.9438 | 66.9515808 | 0.7834 | 0.942 | 53.2186031 |
SENet154_vd | 0.814 | 0.9548 | 0.8127 | 0.9542 | 138.551309 | 0.814 | 0.9548 | 143.699729 |
DPN and DenseNet series
Model | Top-1 Acc by PaddleClas | Top-5 Acc by PaddleClas | Top-1 Acc by 3A | Top-5 Acc by 3A | time by 3A(ms) | Top-1 Acc by 3B | Top-5 Acc by 3B | time by 3B(ms) | Top-1 Acc by 5D | Top-5 Acc by 5D | time by 5D(ms) | Top-1 Acc by 9D | Top-5 Acc by 9D | time by 9D(ms) |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
DenseNet121 | 0.7566 | 0.9258 | 0.7558 | 0.9258 | 53.6027625 | 0.7558 | 0.9258 | 53.6027625 | 0.7558 | 0.9259 | 44.3492407 | 0.7566 | 0.9258 | 39/3477994 |
DenseNet161 | 0.7857 | 0.9414 | 0.7844 | 0.9406 | 115.46785 | 0.7844 | 0.9406 | 115.46785 | 0.7845 | 0.9411 | 91.7863596 | 0.7857 | 0.9414 | 80.0224323 |
DenseNet169 | 0.7681 | 0.9331 | 0.768 | 0.9328 | 68.6313306 | 0.768 | 0.9328 | 68.6313306 | 0.7675 | 0.9328 | 57.4114082 | 0.7681 | 0.9331 | 50.6865443 |
DenseNet201 | 0.7763 | 0.9366 | 0.7755 | 0.9364 | 90.441223 | 0.7755 | 0.9364 | 90.441223 | 0.7757 | 0.9366 | 75.0129829 | 0.7763 | 0.9366 | 66.8523895 |
DenseNet264 | 0.7796 | 0.9385 | 0.7793 | 0.9388 | 129.401795 | 0.7793 | 0.9388 | 129.401795 | 0.779 | 0.9389 | 105.48438 | 0.7796 | 0.9385 | 93.7701779 |
DPN68 | 0.7678 | 0.9343 | 0.724 | 0.9092 | 55.3380497 | 0.724 | 0.9092 | 55.3380497 | 0.7243 | 0.9093 | 44.8887849 | 0.7678 | 0.9343 | 40.6067176 |
DPN92 | 0.7985 | 0.948 | 0.7979 | 0.9478 | 132.163013 | 0.7979 | 0.9478 | 132.163013 | 0.7983 | 0.9478 | 96.3646919 | 0.7985 | 0.948 | 85.7311024 |
DPN98 | 0.8059 | 0.951 | 0.803 | 0.9496 | 166.167311 | 0.803 | 0.9496 | 166.167311 | 0.8035 | 0.9501 | 116.329713 | 0.8059 | 0.951 | 121.238888 |
DPN107 | 0.8089 | 0.9532 | 0.7019 | 0.8887 | 254.276414 | 0.7019 | 0.8887 | 254.276414 | 0.7015 | 0.8878 | 174.463986 | 0.8089 | 0.9532 | 181.168191 |
DPN131 | 0.807 | 0.9514 | 0.8034 | 0.9489 | 223.894086 | 0.8034 | 0.9489 | 223.894086 | 0.8036 | 0.949 | 157.157379 | 0.807 | 0.9514 | 163.428369 |
HRNet series
Model | Top-1 Acc by PaddleClas | Top-5 Acc by PaddleClas | Top-1 Acc by 3A | Top-5 Acc by 3A | time by 3A(ms) | Top-1 Acc by 3B | Top-5 Acc by 3B | time by 3B(ms) | Top-1 Acc by 5D | Top-5 Acc by 5D | time by 5D(ms) | Top-1 Acc by 9D | Top-5 Acc by 9D | time by 9D(ms) |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
HRNet_W18_C | 0.7692 | 0.9339 | 0.5661 | 0.8185 | 60.9819089 | 0.5661 | 0.8185 | 60.9819089 | 0.5659 | 0.8186 | 55.4794437 | 0.7873 | 0.9412 | 39.3477994 |
HRNet_W30_C | 0.7804 | 0.9402 | 0.657 | 0.8775 | 83.3555966 | 0.657 | 0.8775 | 83.3555966 | 0.6571 | 0.8782 | 65.7723425 | 0.7969 | 0.9485 | 44.0211977 |
HRNet_W32_C | 0.7828 | 0.9424 | 0.7821 | 0.9425 | 82.9289218 | 0.7821 | 0.9425 | 82.9289218 | 0.7821 | 0.9423 | 64.4770416 | 0.7923 | 0.945 | 50.0433493 |
HRNet_W40_C | 0.7877 | 0.9447 | 0.7839 | 0.9428 | 111.186797 | 0.7839 | 0.9428 | 111.186797 | 0.7835 | 0.9432 | 80.2612852 | 0.7933 | 0.9394 | 60.8345537 |
HRNet_W44_C | 0.79 | 0.9451 | 0.766 | 0.9349 | 126.927966 | 0.766 | 0.9349 | 126.927966 | 0.7663 | 0.9349 | 88.4218833 | 0.7967 | 0.9413 | 65.3390154 |
HRNet_W48_C | 0.7895 | 0.9442 | 0.7892 | 0.9443 | 135.097451 | 0.7892 | 0.9443 | 135.097451 | 0.7894 | 0.9441 | 90.5619071 | 0.7936 | 0.9677 | 65.9286927 |
HRNet_W64_C | 0.793 | 0.9461 | 0.7926 | 0.9462 | 206.795211 | 0.7926 | 0.9462 | 206.795211 | 0.7929 | 0.946 | 122.473623 | 0.7959 | 0.9478 | 83.8883701 |
Inception series
Model | Top-1 Acc by PaddleClas | Top-5 Acc by PaddleClas | Top-1 Acc by 3A | Top-5 Acc by 3A | time by 3A(ms) | Top-1 Acc by 3B | Top-5 Acc by 3B | time by 3B(ms) | Top-1 Acc by 5D | Top-5 Acc by 5D | time by 5D(ms) | Top-1 Acc by 9D | Top-5 Acc by 9D | time by 9D(ms) |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Xception41_deeplab | 0.7955 | 0.9438 | 0.7593 | 0.9214 | 68.4773261 | 0.7593 | 0.9214 | 68.4773261 | 0.7593 | 0.9215 | 47.2026896 | 0.7923 | 0.945 | 38.5318629 |
Xception65_deeplab | 0.8032 | 0.9449 | 0.7735 | 0.929 | 103.461153 | 0.7735 | 0.929 | 103.461153 | 0.7743 | 0.9287 | 69.1011319 | 0.7967 | 0.9413 | 55.9068845 |
InceptionV3 | 0.7914 | 0.9459 | 0.7348 | 0.9108 | 27.4721269 | 0.7348 | 0.9108 | 27.4721269 | 0.735 | 0.9104 | 21.0058066 | 0.6959 | 0.8878 | 16.2281211 |
InceptionV4 | 0.8077 | 0.9526 | 0.7591 | 0.9218 | 54.4613488 | 0.7591 | 0.9218 | 54.4613488 | 0.7591 | 0.9214 | 38.6780565 | 0.7188 | 0.9051 | 29.8487604 |
EfficientNet and ResNeXt101_wsl series
Model | Top-1 Acc by PaddleClas | Top-5 Acc by PaddleClas | Top-1 Acc by 3A | Top-5 Acc by 3A | time by 3A(ms) | Top-1 Acc by 3B | Top-5 Acc by 3B | time by 3B(ms) | Top-1 Acc by 5D | Top-5 Acc by 5D | time by 5D(ms) | Top-1 Acc by 9D | Top-5 Acc by 9D | time by 9D(ms) |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
ResNeXt101_32x8d_wsl | 0.8255 | 0.9674 | 0.8223 | 0.9659 | 133.606047 | 0.8223 | 0.9659 | 133.606047 | 0.8225 | 0.9656 | 67.4824983 | 0.82 | 0.95 | 47.6001574 |
ResNeXt101_32x16d_wsl | 0.8424 | 0.9726 | 0.8385 | 0.9712 | 83.7354768 | 0.8385 | 0.9712 | 83.7354768 | 0.8385 | 0.9712 | 151.637222 | 0.8312 | 0.9714 | 82.075878 |
ResNeSt50_fast_1s1x64d | 0.8035 | 0.9528 | 0.8022 | 0.9521 | 40.8778803 | 0.8022 | 0.9521 | 40.8778803 | 0.802 | 0.952 | 28.9425293 | 0.7973 | 0.9612 | 21.9779988 |
检测模型
模型为paddledetection release/0.5的ModelZoo,github地址:https://github.com/PaddlePaddle/PaddleDetection/blob/release/0.5/docs/MODEL_ZOO_cn.md
以下数据的测试环境:
模型训练环境:PaddlePaddle = 1.8.5、PaddleDetection = release/0.5
模型部署环境:EdgeBoard 软核1.8.1、系统Ubuntu
Yolov3 on pascal voc
骨架网络 | 输入尺寸 | mAP by paddledetection | mAP on FZ9D |
---|---|---|---|
DarkNet53 | 608 | 83.5 | 81.6 |
DarkNet53 | 416 | 83.6 | 80.9 |
DarkNet53 | 320 | 82.2 | 81.2 |
DarkNet53 Diou-Loss | 608 | 83.5 | 83.3 |
MobileNet-V1 | 608 | 76.2 | 75.6 |
MobileNet-V1 | 416 | 76.7 | 74.3 |
MobileNet-V1 | 320 | 75.3 | 73.1 |
ResNet34 | 608 | 82.6 | 80.2 |
ResNet34 | 416 | 81.9 | 81.1 |
ResNet34 | 320 | 80.1 | 78.2 |
Yolov3 on COCO
骨架网络 | 输入尺寸 | 加入deformable卷积 | mAP by paddledetection | mAP by FZ9D | mAP by FZ5C | FZ5C time cost |
---|---|---|---|---|---|---|
DarkNet53 | 608 | 否 | 38.9 | 38.7 | 38.5 | 248.9 |
DarkNet53 | 416 | 否 | 37.5 | 37.2 | 34.5 | 83 |
DarkNet53 | 320 | 否 | 34.8 | 33.5 | 34.5 | 82.95 |
MobileNet-V1 | 608 | 否 | 29.3 | 28.7 | 28.9 | 155 |
MobileNet-V1 | 416 | 否 | 29.3 | 28.8 | 26.7 | 50 |
MobileNet-V1 | 320 | 否 | 27.1 | 26.9 | 27.6 | 50 |
ResNet34 | 608 | 否 | 36.2 | 36 | 35.86 | 177.4 |
ResNet34 | 416 | 否 | 34.3 | 33.9 | 31 | 59.3 |
ResNet34 | 320 | 否 | 31.4 | 30.5 | 31.1 | 59 |
SSD on pascal voc
骨架网络 | 输入尺寸 | mAP by paddledetection | mAP on EdgeBoard |
---|---|---|---|
MobileNet v1 | 300 | 73.2 | 暂无数据 |
其他支持的模型
分类 | 模型 | 支持平台 |
---|---|---|
分割模型 | deeplabv3P | FZ3、FZ5、FZ9 |
关键点 | HRnet | FZ3、FZ5、FZ9 |
特定场景 | 人体检测 | 场景化软硬一体设备 |
特定场景 | 人体关键点检测 | 场景化软硬一体设备 |
特定场景 | 人脸识别 | 场景化软硬一体设备 |
特定场景 | 安全帽佩戴合规检测 | 场景化软硬一体设备 |
特定场景 | 烟火检测 | 场景化软硬一体设备 |
特定场景 | 车辆检测 | 场景化软硬一体设备 |
特定场景 | 车型识别 | 场景化软硬一体设备 |
特定场景 | 动态车流 | 场景化软硬一体设备 |
特定场景 | 车辆属性 | 场景化软硬一体设备 |
特定场景 | 车牌识别 | 场景化软硬一体设备 |
模型库持续更新中...