The paper "Deep Relative Distance Learning: Tell the Difference Between Similar Vehicles" introduces a model named Deep Relative Distance Learning (DRDL), specifically designed for the problem of vehicle re-identification. DRDL employs a dual-branch deep convolutional network architecture, combined with a coupled clusters loss function and a mixed difference network structure, effectively mapping vehicle images into Euclidean space for similarity measurement.
| 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://github.com/CaptainEven/RepNet-MDNet-VehicleReID
Dataset: https://www.pkuml.org/resources/pku-vehicleid.html to download the VehicleID dataset.
pip3 install -r requirements.txtmkdir -p checkpoints
python3 export.py --weight epoch_14.pth --output checkpoints/repnet.onnx# Accuracy
bash scripts/infer_repnet_fp16_accuracy.sh
# Performance
bash scripts/infer_repnet_fp16_performance.sh| Model | BatchSize | Precision | FPS | cos sim | max diff |
|---|---|---|---|---|---|
| RepNet | 32 | FP16 | 1592.872 | 0.999 | 0.0039 |