mindspore

InceptionV3

Model Description

InceptionV3 is an advanced convolutional neural network architecture that improves upon previous Inception models with several key innovations. It introduces factorized convolutions, label smoothing, and an auxiliary classifier to enhance feature extraction and training stability. The network utilizes batch normalization in side branches to improve gradient flow and convergence. InceptionV3 achieves state-of-the-art performance in image classification tasks while maintaining computational efficiency, making it suitable for various computer vision applications requiring high accuracy and robust feature learning.

Supported Environments

GPU	IXUCA SDK	Release
BI-V100	3.1.0	23.09

Model Preparation

Prepare Resources

Sign up and login in ImageNet official website, then choose 'Download' to download the whole ImageNet dataset. Specify /path/to/imagenet to your ImageNet path in later training process.

The ImageNet dataset path structure should look like:

imagenet
├── train
│   └── n01440764
│       ├── n01440764_10026.JPEG
│       └── ...
├── train_list.txt
├── val
│   └── n01440764
│       ├── ILSVRC2012_val_00000293.JPEG
│       └── ...
└── val_list.txt

Install Dependencies

yum install -y mesa-libGL
pip3 install -r requirements.txt
wget https://download.open-mpi.org/release/open-mpi/v4.0/openmpi-4.0.7.tar.gz
tar xf openmpi-4.0.7.tar.gz
cd openmpi-4.0.7/
./configure --prefix=/usr/local/bin --with-orte
make -j4 && make install
export LD_LIBRARY_PATH=/usr/local/lib/:$LD_LIBRARY_PATH
export PATH=/usr/local/openmpi/bin:$PATH

Model Training

ln -sf $(which python3) $(which python)

# On single GPU
## bash scripts/run_standalone_train_gpu.sh DEVICE_ID DATA_DIR CKPT_PATH
bash scripts/run_standalone_train_gpu.sh /path/to/imagenet/train ./ckpt/

# Multiple GPUs on one machine
## bash scripts/run_distribute_train_gpu.sh DATA_DIR CKPT_PATH
bash scripts/run_distribute_train_gpu.sh /path/to/imagenet/train ./ckpt/

# Evaluation
cd scripts/
DEVICE_ID=0
bash run_eval_gpu.sh $DEVICE_ID /path/to/imagenet/val/ /path/to/checkpoint

Model Results

Model	GPU	epoch	Loss	ACC	FPS
InceptionV3	BI-V100×4	108	3.9033	'Top1-Acc': 0.4847, 'Top5-Acc': 0.7405	447.2

References

• mindspore/models