ResNet101 is a convolutional neural network architecture that belongs to the ResNet (Residual Network) family.With a total of 101 layers, ResNet101 comprises multiple residual blocks, each containing convolutional layers with batch normalization and rectified linear unit (ReLU) activations. These residual blocks allow the network to effectively capture complex features at different levels of abstraction, leading to superior performance on 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/resnet101-63fe2227.pth
Dataset: https://www.image-net.org/download.php to download the validation dataset.
pip3 install -r ../../igie_common/requirements.txtpython3 ../../igie_common/export.py --model-name resnet101 --weight resnet101-63fe2227.pth --output resnet101.onnxexport DATASETS_DIR=/Path/to/imagenet_val/
export RUN_DIR=../../igie_common/# Accuracy
bash scripts/infer_resnet101_fp16_accuracy.sh
# Performance
bash scripts/infer_resnet101_fp16_performance.sh# Accuracy
bash scripts/infer_resnet101_int8_accuracy.sh
# Performance
bash scripts/infer_resnet101_int8_performance.sh| Model | BatchSize | Precision | FPS | Top-1(%) | Top-5(%) |
|---|---|---|---|---|---|
| ResNet101 | 32 | FP16 | 2507.074 | 77.331 | 93.520 |
| ResNet101 | 32 | INT8 | 5458.890 | 76.719 | 93.348 |