# README ## MobileNetV2 This folder contains building code for MobileNetV2, based on [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://arxiv.org/abs/1801.04381) ## Performance ### Latency This is the timing of [MobileNetV1](https://github.com/deepdrive/deepdrive/tree/c64371bd9b652dbaaa84f868a7e2ed025dce2cd9/vendor/tensorflow/models/research/slim/nets/mobilenet_v1.md) vs MobileNetV2 using TF-Lite on the large core of Pixel 1 phone. ![mnet\_v1\_vs\_v2\_pixel1\_latency.png](https://3039641439-files.gitbook.io/~/files/v0/b/gitbook-legacy-files/o/assets%2F-Lf1Rq6VQn8vh1N1AVDz%2F-LtpYxlly72DZOwIVKwT%2F-LtpYyUzyWZRyXLWqhv7%2Fmnet_v1_vs_v2_pixel1_latency.png?generation=1573930397821610\&alt=media) ### 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](https://arxiv.org/abs/1707.01083) there are no published size numbers. We estimate it to be comparable to MobileNetV2 numbers. ![madds\_top1\_accuracy](https://3039641439-files.gitbook.io/~/files/v0/b/gitbook-legacy-files/o/assets%2F-Lf1Rq6VQn8vh1N1AVDz%2F-LtpYxlly72DZOwIVKwT%2F-LtpYyV0Q-SRAg1zRN93%2Fmadds_top1_accuracy.png?generation=1573930397952856\&alt=media) ## 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](https://storage.googleapis.com/mobilenet_v2/checkpoints/mobilenet_v2_1.4_224.tgz) | 582 | 6.06 | 75.0 | 92.5 | 138.0 | | [mobilenet\_v2\_1.3\_224](https://storage.googleapis.com/mobilenet_v2/checkpoints/mobilenet_v2_1.3_224.tgz) | 509 | 5.34 | 74.4 | 92.1 | 123.0 | | [mobilenet\_v2\_1.0\_224](https://storage.googleapis.com/mobilenet_v2/checkpoints/mobilenet_v2_1.0_224.tgz) | 300 | 3.47 | 71.8 | 91.0 | 73.8 | | [mobilenet\_v2\_1.0\_192](https://storage.googleapis.com/mobilenet_v2/checkpoints/mobilenet_v2_1.0_192.tgz) | 221 | 3.47 | 70.7 | 90.1 | 55.1 | | [mobilenet\_v2\_1.0\_160](https://storage.googleapis.com/mobilenet_v2/checkpoints/mobilenet_v2_1.0_160.tgz) | 154 | 3.47 | 68.8 | 89.0 | 40.2 | | [mobilenet\_v2\_1.0\_128](https://storage.googleapis.com/mobilenet_v2/checkpoints/mobilenet_v2_1.0_128.tgz) | 99 | 3.47 | 65.3 | 86.9 | 27.6 | | [mobilenet\_v2\_1.0\_96](https://storage.googleapis.com/mobilenet_v2/checkpoints/mobilenet_v2_1.0_96.tgz) | 56 | 3.47 | 60.3 | 83.2 | 17.6 | | [mobilenet\_v2\_0.75\_224](https://storage.googleapis.com/mobilenet_v2/checkpoints/mobilenet_v2_0.75_224.tgz) | 209 | 2.61 | 69.8 | 89.6 | 55.8 | | [mobilenet\_v2\_0.75\_192](https://storage.googleapis.com/mobilenet_v2/checkpoints/mobilenet_v2_0.75_192.tgz) | 153 | 2.61 | 68.7 | 88.9 | 41.6 | | [mobilenet\_v2\_0.75\_160](https://storage.googleapis.com/mobilenet_v2/checkpoints/mobilenet_v2_0.75_160.tgz) | 107 | 2.61 | 66.4 | 87.3 | 30.4 | | [mobilenet\_v2\_0.75\_128](https://storage.googleapis.com/mobilenet_v2/checkpoints/mobilenet_v2_0.75_128.tgz) | 69 | 2.61 | 63.2 | 85.3 | 21.9 | | [mobilenet\_v2\_0.75\_96](https://storage.googleapis.com/mobilenet_v2/checkpoints/mobilenet_v2_0.75_96.tgz) | 39 | 2.61 | 58.8 | 81.6 | 14.2 | | [mobilenet\_v2\_0.5\_224](https://storage.googleapis.com/mobilenet_v2/checkpoints/mobilenet_v2_0.5_224.tgz) | 97 | 1.95 | 65.4 | 86.4 | 28.7 | | [mobilenet\_v2\_0.5\_192](https://storage.googleapis.com/mobilenet_v2/checkpoints/mobilenet_v2_0.5_192.tgz) | 71 | 1.95 | 63.9 | 85.4 | 21.1 | | [mobilenet\_v2\_0.5\_160](https://storage.googleapis.com/mobilenet_v2/checkpoints/mobilenet_v2_0.5_160.tgz) | 50 | 1.95 | 61.0 | 83.2 | 14.9 | | [mobilenet\_v2\_0.5\_128](https://storage.googleapis.com/mobilenet_v2/checkpoints/mobilenet_v2_0.5_128.tgz) | 32 | 1.95 | 57.7 | 80.8 | 9.9 | | [mobilenet\_v2\_0.5\_96](https://storage.googleapis.com/mobilenet_v2/checkpoints/mobilenet_v2_0.5_96.tgz) | 18 | 1.95 | 51.2 | 75.8 | 6.4 | | [mobilenet\_v2\_0.35\_224](https://storage.googleapis.com/mobilenet_v2/checkpoints/mobilenet_v2_0.35_224.tgz) | 59 | 1.66 | 60.3 | 82.9 | 19.7 | | [mobilenet\_v2\_0.35\_192](https://storage.googleapis.com/mobilenet_v2/checkpoints/mobilenet_v2_0.35_192.tgz) | 43 | 1.66 | 58.2 | 81.2 | 14.6 | | [mobilenet\_v2\_0.35\_160](https://storage.googleapis.com/mobilenet_v2/checkpoints/mobilenet_v2_0.35_160.tgz) | 30 | 1.66 | 55.7 | 79.1 | 10.5 | | [mobilenet\_v2\_0.35\_128](https://storage.googleapis.com/mobilenet_v2/checkpoints/mobilenet_v2_0.35_128.tgz) | 20 | 1.66 | 50.8 | 75.0 | 6.9 | | [mobilenet\_v2\_0.35\_96](https://storage.googleapis.com/mobilenet_v2/checkpoints/mobilenet_v2_0.35_96.tgz) | 11 | 1.66 | 45.5 | 70.4 | 4.5 | ## Example See this [ipython notebook](https://github.com/deepdrive/deepdrive/tree/c64371bd9b652dbaaa84f868a7e2ed025dce2cd9/vendor/tensorflow/models/research/slim/nets/mobilenet/mobilenet_example.ipynb) or open and run the network directly in [Colaboratory](https://colab.research.google.com/github/tensorflow/models/blob/master/research/slim/nets/mobilenet/mobilenet_example.ipynb).