DenseNet201 is a deep convolutional neural network that stands out for its unique dense connection architecture, where each layer integrates features from all previous layers, effectively reusing features and reducing the number of parameters. This design not only enhances the network's information flow and parameter efficiency but also increases the model's regularization effect, helping to prevent overfitting. DenseNet201 consists of multiple dense blocks and transition layers, capable of capturing rich feature representations while maintaining computational efficiency, making it suitable for complex image recognition tasks.
| 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.pytorch.org/models/densenet201-c1103571.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 ../../ixrt_common/requirements.txtmkdir checkpoints
python3 ../../ixrt_common/export.py --model-name densenet201 --weight densenet201-c1103571.pth --output checkpoints/densenet201.onnxexport PROJ_DIR=./
export DATASETS_DIR=/path/to/imagenet_val/
export CHECKPOINTS_DIR=./checkpoints
export RUN_DIR=../../ixrt_common/
export CONFIG_DIR=../../ixrt_common/config/DENSENET201_CONFIG# Accuracy
bash scripts/infer_densenet201_fp16_accuracy.sh
# Performance
bash scripts/infer_densenet201_fp16_performance.sh| Model | BatchSize | Precision | FPS | Top-1(%) | Top-5(%) |
|---|---|---|---|---|---|
| DenseNet201 | 32 | FP16 | 788.946 | 76.88 | 93.37 |