mmpretrain/projects/internimage_classification

InternImage Classification

Description

This is the implementation of InternImage for image classification.

Usage

Setup Environment

Please refer to Get Started documentation of MMPretrain to finish installation.

Please install DCNv3. Run the command below following the InternImage official installation instructions.

cd ops_dcnv3
sh ./make.sh

Training and Test Commands

At first, you need to add the current folder to PYTHONPATH, so that Python can find your model files. In projects/internimage_classification/ root directory, please run command below to add it.

export PYTHONPATH=`pwd`:$PYTHONPATH

Training

On Local Single GPU

# train with mim
mim train mmpretrain ${CONFIG} --work-dir ${WORK_DIR}

# a specific command example
mim train mmpretrain configs/internimage-tiny_8xb128_in1k-224.py \
	--work-dir work_dirs/internimage-tiny_8xb128_in1k-224/

On Multiple GPUs

# train with mim
mim train mmpretrain ${CONFIG} \
    --work-dir ${WORK_DIR} \
    --launcher pytorch --gpus 8

On Multiple GPUs with Slurm

# train with mim
mim train mmpretrain ${CONFIG} \
    --work-dir ${WORK_DIR} \
    --launcher slurm --gpus 16 --gpus-per-node 8 \
    --partition ${PARTITION}

Test

Please download the pretrain weight provided by OpenGVLab from here

On Local Single GPU

# test with mim
mim test mmpretrain ${CONFIG} -C ${CHECKPOINT}

# a specific command example
mim test mmpretrain configs/internimage-tiny_8xb128_in1k-224.py -C /PATH/TO/internimage_t_1k_224.pth

On Multiple GPUs

# test with mim
# a specific command examples, 8 GPUs here
mim test mmpretrain configs/internimage_t_1k_224.py \
	-C /PATH/TO/internimage_t_1k_224.pth \
    --launcher pytorch --gpus 8

On Multiple GPUs with Slurm

# test with mim
mim test mmpretrain ${CONFIG} \
    -C ${CHECKPOINT}
    --work-dir ${WORK_DIR} \
    --launcher slurm --gpus 8 --gpus-per-node 8 \
    --partition ${PARTITION} \
    $PY_ARGS

Note: PY_ARGS is other optional args.

Results on ImageNet1K

The accuracy of different models on ImageNet1K,

name	resolution	acc@1	acc@5	config	weight
InternImage-T	224	83.4700	96.5340	config	model
InternImage-S	224	84.1640	96.9320	config	model
InternImage-B	224	84.8660	97.1820	config	model
InternImage-L	384	87.7060	98.3820	config	model
InternImage-XL	384	88.0460	98.5620	config	model
InternImage-H	640	89.5500	98.8500	config	model
InternImage-G	512	90.0580	98.9700	config	model

Citation

@article{wang2022internimage,
  title={InternImage: Exploring Large-Scale Vision Foundation Models with Deformable Convolutions},
  author={Wang, Wenhai and Dai, Jifeng and Chen, Zhe and Huang, Zhenhang and Li, Zhiqi and Zhu, Xizhou and Hu, Xiaowei and Lu, Tong and Lu, Lewei and Li, Hongsheng and others},
  journal={arXiv preprint arXiv:2211.05778},
  year={2022}
}