VAN-B0 is a lightweight visual attention network that combines convolution and attention mechanisms to enhance image classification performance. It achieves efficient feature capture by focusing on key areas and multi-scale feature extraction, making it suitable for running on resource-constrained devices.
| GPU | IXUCA SDK | Release | Branch |
|---|---|---|---|
| MR-V100 | 4.4.0 | 26.03 | release/26.03 |
| MR-V100 | 4.3.0 | 25.12 | release/25.12 |
Note: 请切换到与您的 SDK 版本对应的 Release 分支进行测试。请勿直接在 master 分支上运行测试，因为 master 分支可能包含与您的本地 SDK 版本不兼容的最新更改。
切换分支命令示例：
git checkout release/26.03
Pretrained model: https://download.openmmlab.com/mmclassification/v0/van/van-tiny_8xb128_in1k_20220501-385941af.pth
Dataset: https://www.image-net.org/download.php to download the validation dataset.
# Install libGL
## CentOS
yum install -y mesa-libGL
## Ubuntu
apt install -y libgl1-mesa-glx
pip3 install -r ../../igie_common/requirements.txt
pip3 install --no-build-isolation mmcv==1.5.3 mmcls==0.24.0# git clone mmpretrain
git clone -b v0.24.0 https://github.com/open-mmlab/mmpretrain.git
# export onnx model
python3 ../../igie_common/export_mmcls.py --cfg mmpretrain/configs/van/van-b0_8xb128_in1k.py --weight van-tiny_8xb128_in1k_20220501-385941af.pth --output van_b0.onnx
export DATASETS_DIR=/Path/to/imagenet_val/
export RUN_DIR=../../igie_common/# Accuracy
bash scripts/infer_van_b0_fp16_accuracy.sh
# Performance
bash scripts/infer_van_b0_fp16_performance.sh| Model | BatchSize | Precision | FPS | Top-1(%) | Top-5(%) |
|---|---|---|---|---|---|
| VAN_B0 | 32 | FP16 | 2155.35 | 72.079 | 91.209 |