# MobileNetV2 Description MobileNetV2 is tuned to mobile phone CPUs through a combination of hardware- aware network architecture search (NAS) complemented by the NetAdapt algorithm and then subsequently improved through novel architecture advances.Nov 20, 2019. [Paper](https://arxiv.org/pdf/1905.02244) Howard, Andrew, Mark Sandler, Grace Chu, Liang-Chieh Chen, Bo Chen, Mingxing Tan, Weijun Wang et al. "Searching for MobileNetV2." In Proceedings of the IEEE International Conference on Computer Vision, pp. 1314-1324. 2019. # Model architecture The overall network architecture of MobileNetV2 is show below: [Link](https://arxiv.org/pdf/1905.02244) # Dataset Dataset used: [imagenet](http://www.image-net.org/) - Dataset size: ~125G, 1.2W colorful images in 1000 classes - Train: 120G, 1.2W images - Test: 5G, 50000 images - Data format: RGB images. - Note: Data will be processed in src/dataset.py # Features # Environment Requirements - Hardware（Ascend/GPU） - Prepare hardware environment with Ascend or GPU processor. If you want to try Ascend , please send the [application form](https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/file/other/Ascend%20Model%20Zoo%E4%BD%93%E9%AA%8C%E8%B5%84%E6%BA%90%E7%94%B3%E8%AF%B7%E8%A1%A8.docx) to ascend@huawei.com. Once approved, you can get the resources. - Framework - [MindSpore](http://10.90.67.50/mindspore/archive/20200506/OpenSource/me_vm_x86/) - For more information, please check the resources below： - [MindSpore tutorials](https://www.mindspore.cn/tutorial/zh-CN/master/index.html) - [MindSpore API](https://www.mindspore.cn/api/zh-CN/master/index.html) # Script description ## Script and sample code ```python ├── MobileNetV2 ├── Readme.md ├── scripts │ ├──run_train.sh │ ├──run_eval.sh ├── src │ ├──config.py │ ├──dataset.py │ ├──luanch.py │ ├──lr_generator.py │ ├──mobilenetV2.py ├── train.py ├── eval.py ``` ## Training process ### Usage - Ascend: sh run_train.sh Ascend [DEVICE_NUM] [SERVER_IP(x.x.x.x)] [VISIABLE_DEVICES(0,1,2,3,4,5,6,7)] [DATASET_PATH] [CKPT_PATH] - GPU: sh run_trian.sh GPU [DEVICE_NUM] [VISIABLE_DEVICES(0,1,2,3,4,5,6,7)] [DATASET_PATH] ### Launch ``` # training example Ascend: sh run_train.sh Ascend 8 192.168.0.1 0,1,2,3,4,5,6,7 ~/imagenet/train/ mobilenet_199.ckpt GPU: sh run_train.sh GPU 8 0,1,2,3,4,5,6,7 ~/imagenet/train/ ``` ### Result Training result will be stored in the example path. Checkpoints will be stored at `. /checkpoint` by default, and training log will be redirected to `./train/train.log` like followings. ``` epoch: [ 0/200], step:[ 624/ 625], loss:[5.258/5.258], time:[140412.236], lr:[0.100] epoch time: 140522.500, per step time: 224.836, avg loss: 5.258 epoch: [ 1/200], step:[ 624/ 625], loss:[3.917/3.917], time:[138221.250], lr:[0.200] epoch time: 138331.250, per step time: 221.330, avg loss: 3.917 ``` ## Eval process ### Usage - Ascend: sh run_infer.sh Ascend [DATASET_PATH] [CHECKPOINT_PATH] - GPU: sh run_infer.sh GPU [DATASET_PATH] [CHECKPOINT_PATH] ### Launch ``` # infer example Ascend: sh run_infer.sh Ascend ~/imagenet/val/ ~/train/mobilenet-200_625.ckpt GPU: sh run_infer.sh GPU ~/imagenet/val/ ~/train/mobilenet-200_625.ckpt ``` > checkpoint can be produced in training process. ### Result Inference result will be stored in the example path, you can find result like the followings in `val.log`. ``` result: {'acc': 0.71976314102564111} ckpt=/path/to/checkpoint/mobilenet-200_625.ckpt ``` # Model description ## Performance ### Training Performance | Parameters | MobilenetV2 | | | -------------------------- | ---------------------------------------------------------- | ------------------------- | | Model Version | | large | | Resource | Ascend 910, cpu:2.60GHz 56cores, memory:314G | NV SMX2 V100-32G | | uploaded Date | 05/06/2020 | 05/06/2020 | | MindSpore Version | 0.3.0 | 0.3.0 | | Dataset | ImageNet | ImageNet | | Training Parameters | src/config.py | src/config.py | | Optimizer | Momentum | Momentum | | Loss Function | SoftmaxCrossEntropy | SoftmaxCrossEntropy | | outputs | | | | Loss | | 1.913 | | Accuracy | | ACC1[77.09%] ACC5[92.57%] | | Total time | | | | Params (M) | | | | Checkpoint for Fine tuning | | | | Model for inference | | | #### Inference Performance | Parameters | | | | | -------------------------- | ----------------------------- | ------------------------- | -------------------- | | Model Version | V1 | | | | Resource | Huawei 910 | NV SMX2 V100-32G | Huawei 310 | | uploaded Date | 05/06/2020 | 05/22/2020 | | | MindSpore Version | 0.2.0 | 0.2.0 | 0.2.0 | | Dataset | ImageNet, 1.2W | ImageNet, 1.2W | ImageNet, 1.2W | | batch_size | | 130(8P) | | | outputs | | | | | Accuracy | | ACC1[72.07%] ACC5[90.90%] | | | Speed | | | | | Total time | | | | | Model for inference | | | | # ModelZoo Homepage [Link](https://gitee.com/mindspore/mindspore/tree/master/mindspore/model_zoo)