--- layout: post title: Benchmark 数据 --- 可以参考[benchmark_tools](../benchmark_tools),推荐**一键benchmark**。 # 测试环境 * 测试模型 * fp32模型 * mobilenet_v1 * mobilenet_v2 * squeezenet_v1.1 * mnasnet * shufflenet_v2 * int8模型 * mobilenet_v1 * mobilenet_v2 * resnet50 * 测试机器(android ndk ndk-r17c) * 骁龙855 * xiaomi mi9, snapdragon 855 * 4xA76(1@2.84GHz + 3@2.4GHz) + 4xA55@1.78GHz * 骁龙845 * xiaomi mi8, 845 * 2.8GHz(大四核),1.7GHz(小四核) * 骁龙835 * xiaomi mix2, snapdragon 835 * 2.45GHz(大四核),1.9GHz(小四核) * 骁龙625 * oppo R9s, snapdragon625 * A53 x 8, big core@2.0GHz * 骁龙653 * 360 N5, snapdragon 653 * 4 x A73@2.0GHz + 4 x A53@1.4GHz * 麒麟970 * HUAWEI Mate10 * 测试说明 * branch: release/2.0.0 * warmup=10, repeats=30,统计平均时间,单位是ms * 当线程数为1时,```DeviceInfo::Global().SetRunMode```设置LITE_POWER_HIGH,否者设置LITE_POWER_NO_BIND * 模型的输入图像的维度是{1, 3, 224, 224},输入图像的每一位数值是1 # 测试数据 ## fp32模型测试数据 ### paddlepaddle model 骁龙855|armv7 | | |armv8 | | | ----| ---- | ---- | ---- | ---- |---- |---- threads num|1 |2 |4 |1 |2 |4 mobilenet_v1 |32.19 |18.81 |10.90 |30.92 |18.31 |10.15 mobilenet_v2 |22.91 |13.75 |8.64 |21.15 |12.79 |7.84 shufflenet_v2 |4.67 |3.37 |2.65 |4.43 |3.15 |2.66 squeezenet_v1.1 |25.10 |15.93 |9.68 |23.28 |14.61 |8.71 mnasnet |21.84 |13.14 |7.96 |19.61 |11.88 |7.55 骁龙835|armv7 | | |armv8 | | | ----| ---- | ---- | ---- | ---- |---- |---- threads num|1 |2 |4 |1 |2 |4 mobilenet_v1 |94.13 |52.17 |30.68 |88.28 |47.58 |26.64 mobilenet_v2 |61.24 |34.64 |22.36 |56.66 |32.19 |19.63 shufflenet_v2 |10.87 |6.92 |5.12 |10.41 |6.76 |4.97 squeezenet_v1.1 |73.61 |42.25 |24.44 |64.87 |38.43 |23.06 mnasnet |58.22 |33.43 |20.44 |53.43 |30.20 |18.09 麒麟980|armv7 | | |armv8 | | | ----| ---- | ---- | ---- | ---- |---- |---- threads num|1 |2 |4 |1 |2 |4 mobilenet_v1 |55.11 |28.24 |13.27 |34.24 |17.74 |12.41 mobilenet_v2 |37.03 |19.80 |51.94 |23.64 |12.98 |9.38 shufflenet_v2 |7.26 |4.94 |15.06 |5.32 |3.33 |2.82 squeezenet_v1.1 |42.73 |23.66 |57.39 |26.03 |14.53 |13.66 mnasnet |36.87 |20.15 |46.04 |21.85 |12.06 |8.68 麒麟970|armv7 | | |armv8 | | | ----| ---- | ---- | ---- | ---- |---- |---- threads num|1 |2 |4 |1 |2 |4 mobilenet_v1 |97.80 |52.64 |34.46 |94.51 |49.36 |28.43 mobilenet_v2 |66.55 |38.52 |23.19 |62.89 |34.93 |21.53 shufflenet_v2 |13.78 |8.11 |5.93 |11.95 |7.90 |5.91 squeezenet_v1.1 |77.64 |43.67 |25.72 |69.91 |40.66 |24.62 mnasnet |61.86 |34.62 |22.68 |59.61 |32.79 |19.56 ## caffe model 骁龙855|armv7 | | |armv8 | | | ----| ---- | ---- | ---- | ---- |---- |----| threads num|1 |2 |4 |1 |2 |4 | mobilenet_v1 |32.42 |18.68 |10.86 |30.92 |18.35 |10.07 | mobilenet_v2 |29.53 |17.76 |10.89 |27.19 |16.53 |9.75 | shufflenet_v2 |4.61 |3.29 |2.61 |4.36 |3.11 |2.51 | 骁龙835|armv7 | | |armv8 | | | ----| ---- | ---- | ---- | ---- |---- |----| threads num|1 |2 |4 |1 |2 |4 | mobilenet_v1 |92.52 |52.34 |30.37 |88.31 |49.75 |27.29 | mobilenet_v2 |79.50 |45.67 |28.79 |76.13 |44.01 |26.13 | shufflenet_v2 |10.94 |7.08 |5.16 |10.64 |6.83 |5.01 | 麒麟980|armv7 | | |armv8 | | | ----| ---- | ---- | ---- | ---- |---- |----| threads num|1 |2 |4 |1 |2 |4 | mobilenet_v1 |55.36 |28.18 |13.31 |34.42 |17.93 |12.52 | mobilenet_v2 |49.17 |26.10 |65.49 |30.50 |16.66 |11.72 | shufflenet_v2 |8.45 |5.00 |15.65 |4.58 |3.14 |2.83 | 麒麟970|armv7 | | |armv8 | | | ----| ---- | ---- | ---- | ---- |---- |----| threads num|1 |2 |4 |1 |2 |4 | mobilenet_v1 |97.85 |53.38 |33.85 |94.29 |49.42 |28.29 | mobilenet_v2 |87.40 |50.25 |31.85 |85.55 |48.11 |28.24 | shufflenet_v2 |12.16 |8.39 |6.21 |12.21 |8.33 |6.32 | ## int8量化模型测试数据 骁龙855|armv7 | | |armv8 | | | ----| ---- | ---- | ---- | ---- |---- |----| threads num|1 |2 |4 |1 |2 |4 | mobilenet_v1 |36.80 |21.58 |11.12 | 14.01 |8.13 |4.32 | mobilenet_v2 |28.72 |19.08 |12.49 | 17.24 |11.55 |7.82 | 骁龙835|armv7 | | |armv8 | | | ----| ---- | ---- | ---- | ---- |---- |----| threads num|1 |2 |4 |1 |2 |4 | mobilenet_v1 |60.76 |32.25 |16.66 |56.57 |29.84 |15.24 | mobilenet_v2 |49.38 |31.10 |22.07 |47.52 |28.18 |19.24 | 骁龙970|armv7 | | |armv8 | | | ----| ---- | ---- | ---- | ---- |---- |----| threads num|1 |2 |4 |1 |2 |4 | mobilenet_v1 |65.95 |34.39 |18.68 |60.86 |30.98 |16.31 | mobilenet_v2 |68.87 |39.39 |24.43 |65.57 |37.31 |20.87 |