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+# MobileNetV3
+## Model description
+MobileNetV3 is a convolutional neural network that 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. Advances include (1) complementary search techniques, (2) new efficient versions of nonlinearities practical for the mobile setting, (3) new efficient network design.
+
+## Step 1: Installing
+```
+git clone https://github.com/PaddlePaddle/PaddleClas.git
+```
+
+```bash
+cd PaddleClas
+pip3 install -r requirements.txt
+```
+
+## Step 2: Prepare Datasets
+Download [ImageNet](https://www.image-net.org/), the path as /home/datasets/imagenet/, then the imagenet path as follows:
+```
+# IMAGENET PATH as follow:
+drwxr-xr-x 1002 root root    24576 Mar  1 15:33 train
+-rw-r--r--    1 root root 43829433 May 16 07:55 train_list.txt
+drwxr-xr-x 1002 root root    24576 Mar  1 15:41 val
+-rw-r--r--    1 root root  2144499 May 16 07:56 val_list.txt
+```
+
+## Step 3: Training
+Notice: modify PaddleClas/ppcls/configs/ImageNet/MobileNetV3/MobileNetV3_small_x1_25.yaml file, modify the datasets path as yours.
+```
+cd PaddleClas
+export FLAGS_cudnn_exhaustive_search=True
+export FLAGS_cudnn_batchnorm_spatial_persistent=True
+export CUDA_VISIBLE_DEVICES=0,1,2,3
+python3 -u -m paddle.distributed.launch --gpus=0,1,2,3 tools/train.py -c ppcls/configs/ImageNet/MobileNetV3/MobileNetV3_large_x1_0.yaml -o Arch.pretrained=False -o Global.device=gpu
+```
+
+
+
+## Reference
+- [PaddleClas](https://github.com/PaddlePaddle/PaddleClas)
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