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"INTERN-2.5" is a powerful multimodal multitask general model jointly released by SenseTime and Shanghai AI Laboratory. It consists of large-scale vision foundation model "InternImage", pre-training method "M3I-Pretraining", generic decoder "Uni-Perceiver" series, and generic encoder for autonomous driving perception "BEVFormer" series.
CUDA>=10.2 with cudnn>=7
PyTorch>=1.10.0 and torchvision>=0.9.0 with CUDA>=10.2
## Install libGL
yum install -y mesa-libGL
## Install mmcv
cd mmcv/
bash clean_mmcv.sh
bash build_mmcv.sh
bash install_mmcv.sh
cd ../
## Install timm and mmdet
pip3 install timm==0.6.11 mmdet==2.28.1
pip3 install addict yapf opencv-python termcolor yacs pyyaml scipy
cd ./ops_dcnv3
sh ./make.sh
# unit test (should see all checking is True)
python3 test.py
cd ../
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
# Training on 8 GPUs
export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
export LOCAL_SIZE=8
python3 -m torch.distributed.launch --nproc_per_node 8 --master_port 12345 main.py --cfg configs/internimage_t_1k_224.yaml --data-path /path/to/imagenet
# Training on 1 GPU
export CUDA_VISIBLE_DEVICES=0
export LOCAL_SIZE=1
python3 main.py --cfg configs/internimage_t_1k_224.yaml --data-path /path/to/imagenet
| GPU | FP32 |
|---|---|
| 8 cards | Acc@1 83.440 fps 252 |
| 1 card | fps 31 |
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