EfficientNet_Mobile

Mobile friendly EfficientNet based on ImageNet dataset with tensorflow keras

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Mobile friendly EfficientNet is described in Here

Install

You will need a machine with a GPU and CUDA installed.
Then, you prepare runtime environment:

pip install -r requirements.txt

Use

1. Prepare your dataset

Step-by-step

0. Clone this repository.

   $ git clone https://github.com/da2so/EfficientNet_Mobile.git
   $ cd EfficientNet_Mobile/dataset/raw_data

1. Download the "Training images (Task 1 & 2)" and "Validation images (all tasks)" from the ImageNet Large Scale Visual Recognition Challenge 2012 (ILSVRC2012) download page at dataset/raw_data directory.

   $ ls -l ./
   -rwxr-xr-x 1 shkang shkang 147897477120 Feb  14 14:55 ILSVRC2012_img_train.tar
   -rwxr-xr-x 1 shkang shkang   6744924160 Feb  14 15:58 ILSVRC2012_img_val.tar

2. Untar the "train" and "val" files. For example, I put the untarred files at dataset/raw_data.

   $ mkdir train
   $ cd train
   $ tar xvf ../ILSVRC2012_img_train.tar
   $ find . -name "*.tar" | while read NAME ; do \
         mkdir -p "${NAME%.tar}"; \
         tar -xvf "${NAME}" -C "${NAME%.tar}"; \
         rm -f "${NAME}"; \
     done
   $ cd ..
   $ mkdir validation
   $ cd validation
   $ tar xvf ../ILSVRC2012_img_val.tar

3. Pre-process the validation image files. (The script would move the JPEG files into corresponding subfolders.)

   $ cd ../../../data  # EfficientNet_Mobile/dataset/raw_data/validation -> EfficientNet_mobile/data
   $ python  ./process_val.py \
             ./dataset/raw_data/valiation/ \
             imagenet_2012_validation_synset_labels.txt

4. Build TFRecord files for "train" and "validation". (This step could take a couple of hours, since there are 1,281,167 training images and 50,000 validation images in total.)

   $ cd .. 
   $ mkdir /dataset/tfrecord/
   $ python  convert2tfrecord.py \
             --output_directory=./dataset/tfrecord/ \
             --train_directory=./dataset/raw_data/train/ \
             --validation_directory=./dataset/raw_data/val/

2. Train Mobile Friendly EfficientNet

Train mobile friendly EfficientNet based on ImageNet dataset

$ CUDA_VISIBLE DEVICES=0 python train.py \
                                --imagenet_path=./dataset/tfrecord/ \
                                --model_name=EfficientNetB0_M \
                                --bs=16 \
                                --epochs=100 \
                                --lr=1e-2 \
                                --save_dir=./result/ \

Train Using Multi-GPU

$ CUDA_VISIBLE DEVICES=0,1,2,3 python train.py \
                                --imagenet_path=./dataset/tfrecord/ \
                                --model_name=EfficientNetB0_M \
                                --bs=16 \
                                --epochs=100 \
                                --lr=1e-2 \
                                --save_dir=./result/ \

The total number of batch size will be (16 x The number of GPUs) = 16 x 4 = 64.

Arguments:

• imagenet_path - ImageNet dataset path
• model_name - Name of Mobile friendly EfficientNet
  • Choices = ['EfficientNetB0_M', 'EfficientNetB1_M', 'EfficientNetB2_M', 'EfficientNetB3_M']
• bs - Batch size
• epochs - The number of epochs
• lr - Learning rate
• save_dir - Save directory for EfficientNet model

TODO

• Multi-GPU training
• TFLite conversion
• Comparing results between original and our EfficientNet
• Train a model based on Tiny ImageNet