mindspore/model_zoo/official/cv/efficientnet

• Contents
• EfficientNet-B0 Description
• Model architecture
• Dataset
• Environment Requirements
• Script description
• • Script and sample code
  • Script Parameters
  • Training Process
  • • Usage
    • Launch
  • Evaluation Process
  • • Usage
    • • Launch
      • Result
• Model description
• • Performance
  • • Training Performance
    • Inference Performance
• ModelZoo Homepage

Contents

• EfficientNet-B0 Description
• Model Architecture
• Dataset
• Environment Requirements
• Quick Start
• Script Description
  • Script and Sample Code
  • Script Parameters
  • Training Process
  • Evaluation Process
• Model Description
  • Performance
    • Training Performance
    • Inference Performance
• ModelZoo Homepage

EfficientNet-B0 Description

Paper: Mingxing Tan, Quoc V. Le. EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks. 2019.

Model architecture

The overall network architecture of EfficientNet-B0 is show below:

Link

Dataset

Dataset used: imagenet

• 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

Environment Requirements

• Hardware GPU
  • Prepare hardware environment with GPU processor.
• Framework
  • MindSpore
• For more information, please check the resources below：
  • MindSpore Tutorials
  • MindSpore Python API

Script description

Script and sample code

.
└─efficientnet
  ├─README.md
  ├─scripts
    ├─run_standalone_train_for_gpu.sh # launch standalone training with gpu platform(1p)
    ├─run_distribute_train_for_gpu.sh # launch distributed training with gpu platform(8p)
    └─run_eval_for_gpu.sh             # launch evaluating with gpu platform
  ├─src
    ├─config.py                       # parameter configuration
    ├─dataset.py                      # data preprocessing
    ├─efficientnet.py                 # network definition
    ├─loss.py                         # Customized loss function
    ├─transform_utils.py              # random augment utils
    ├─transform.py                    # random augment class
├─eval.py                             # eval net
└─train.py                            # train net

Script Parameters

Parameters for both training and evaluating can be set in config.py.

'random_seed': 1,                # fix random seed
'model': 'efficientnet_b0',      # model name
'drop': 0.2,                     # dropout rate
'drop_connect': 0.2,             # drop connect rate
'opt_eps': 0.001,                # optimizer epsilon
'lr': 0.064,                     # learning rate LR
'batch_size': 128,               # batch size
'decay_epochs': 2.4,             # epoch interval to decay LR
'warmup_epochs': 5,              # epochs to warmup LR
'decay_rate': 0.97,              # LR decay rate
'weight_decay': 1e-5,            # weight decay
'epochs': 600,                   # number of epochs to train
'workers': 8,                    # number of data processing processes
'amp_level': 'O0',               # amp level
'opt': 'rmsprop',                # optimizer
'num_classes': 1000,             # number of classes
'gp': 'avg',                     # type of global pool, "avg", "max", "avgmax", "avgmaxc"
'momentum': 0.9,                 # optimizer momentum
'warmup_lr_init': 0.0001,        # init warmup LR
'smoothing': 0.1,                # label smoothing factor
'bn_tf': False,                  # use Tensorflow BatchNorm defaults
'keep_checkpoint_max': 10,       # max number ckpts to keep
'loss_scale': 1024,              # loss scale
'resume_start_epoch': 0,         # resume start epoch

Training Process

Usage

GPU:
    # distribute training example(8p)
    sh run_distribute_train_for_gpu.sh
    # standalone training
    sh run_standalone_train_for_gpu.sh DEVICE_ID DATA_DIR

Launch

# distributed training example(8p) for GPU
cd scripts
sh run_distribute_train_for_gpu.sh 8 0,1,2,3,4,5,6,7 /dataset/train
# standalone training example for GPU
cd scripts
sh run_standalone_train_for_gpu.sh 0 /dataset/train

You can find checkpoint file together with result in log.

Evaluation Process

Usage

# Evaluation
sh run_eval_for_gpu.sh DATA_DIR DEVICE_ID PATH_CHECKPOINT

Launch

# Evaluation with checkpoint
cd scripts
sh run_eval_for_gpu.sh /dataset/eval ./checkpoint/efficientnet_b0-600_1251.ckpt

Result

Evaluation result will be stored in the scripts path. Under this, you can find result like the following in log.

acc=76.96%(TOP1)

Model description

Performance

Training Performance

Parameters	efficientnet_b0
Resource	NV SMX2 V100-32G
uploaded Date	10/26/2020
MindSpore Version	1.0.0
Dataset	ImageNet
Training Parameters	src/config.py
Optimizer	rmsprop
Loss Function	LabelSmoothingCrossEntropy
Loss	1.8886
Accuracy	76.96%(TOP1)
Total time	132 h 8ps
Checkpoint for Fine tuning	64 M(.ckpt file)

Inference Performance

Parameters
Resource	NV SMX2 V100-32G
uploaded Date	10/26/2020
MindSpore Version	1.0.0
Dataset	ImageNet, 1.2W
batch_size	128
outputs	probability
Accuracy	acc=76.96%(TOP1)

ModelZoo Homepage

Please check the official homepage.