# 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](/files/-LtpYyUzyWZRyXLWqhv7) ### 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](/files/-LtpYyV0Q-SRAg1zRN93) ## 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). --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://docs.deepdrive.io/vendor/tensorflow/models/research/slim/nets/mobilenet.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.