README

MobileNetV2

This folder contains building code for MobileNetV2, based on MobileNetV2: Inverted Residuals and Linear Bottlenecks

Performance

Latency

This is the timing of MobileNetV1 vs MobileNetV2 using TF-Lite on the large core of Pixel 1 phone.

mnet_v1_vs_v2_pixel1_latency.png

MACs

MACs, also sometimes known as MADDs - the number of multiply-accumulates needed to compute an inference on a single image is a common metric to measure the efficiency of the model.

Below is the graph comparing V2 vs a few selected networks. The size of each blob represents the number of parameters. Note for ShuffleNet there are no published size numbers. We estimate it to be comparable to MobileNetV2 numbers.

madds_top1_accuracy

Pretrained models

Imagenet Checkpoints

Classification Checkpoint	MACs (M)	Parameters (M)	Top 1 Accuracy	Top 5 Accuracy	Mobile CPU (ms) Pixel 1
mobilenet_v2_1.4_224	582	6.06	75.0	92.5	138.0
mobilenet_v2_1.3_224	509	5.34	74.4	92.1	123.0
mobilenet_v2_1.0_224	300	3.47	71.8	91.0	73.8
mobilenet_v2_1.0_192	221	3.47	70.7	90.1	55.1
mobilenet_v2_1.0_160	154	3.47	68.8	89.0	40.2
mobilenet_v2_1.0_128	99	3.47	65.3	86.9	27.6
mobilenet_v2_1.0_96	56	3.47	60.3	83.2	17.6
mobilenet_v2_0.75_224	209	2.61	69.8	89.6	55.8
mobilenet_v2_0.75_192	153	2.61	68.7	88.9	41.6
mobilenet_v2_0.75_160	107	2.61	66.4	87.3	30.4
mobilenet_v2_0.75_128	69	2.61	63.2	85.3	21.9
mobilenet_v2_0.75_96	39	2.61	58.8	81.6	14.2
mobilenet_v2_0.5_224	97	1.95	65.4	86.4	28.7
mobilenet_v2_0.5_192	71	1.95	63.9	85.4	21.1
mobilenet_v2_0.5_160	50	1.95	61.0	83.2	14.9
mobilenet_v2_0.5_128	32	1.95	57.7	80.8	9.9
mobilenet_v2_0.5_96	18	1.95	51.2	75.8	6.4
mobilenet_v2_0.35_224	59	1.66	60.3	82.9	19.7
mobilenet_v2_0.35_192	43	1.66	58.2	81.2	14.6
mobilenet_v2_0.35_160	30	1.66	55.7	79.1	10.5
mobilenet_v2_0.35_128	20	1.66	50.8	75.0	6.9
mobilenet_v2_0.35_96	11	1.66	45.5	70.4	4.5

Example

See this ipython notebook or open and run the network directly in Colaboratory.