mirror of
https://github.com/PaddlePaddle/FastDeploy.git
synced 2025-10-31 20:02:53 +08:00
c91e99b5f5
* Adjust folders structures in paddleclas * remove useless files * Update sophgo * improve readme
74 lines
6.7 KiB
Markdown
74 lines
6.7 KiB
Markdown
[English](README.md) | 简体中文
|
||
|
||
# PaddleClas 模型在CPU与GPU上的部署方案-FastDeploy
|
||
|
||
## 1. 说明
|
||
PaddleClas支持通过FastDeploy在NVIDIA GPU、X86 CPU、飞腾CPU、ARM CPU、Intel GPU(独立显卡/集成显卡)硬件上快速部署PaddleClas系列模型
|
||
|
||
## 2. 模型版本说明
|
||
|
||
- [PaddleClas Release/2.4](https://github.com/PaddlePaddle/PaddleClas/tree/release/2.4)
|
||
|
||
目前FastDeploy支持如下模型的部署
|
||
|
||
- [PP-LCNet系列模型](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.4/docs/zh_CN/models/PP-LCNet.md)
|
||
- [PP-LCNetV2系列模型](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.4/docs/zh_CN/models/PP-LCNetV2.md)
|
||
- [EfficientNet系列模型](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.4/docs/zh_CN/models/EfficientNet_and_ResNeXt101_wsl.md)
|
||
- [GhostNet系列模型](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.4/docs/zh_CN/models/Mobile.md)
|
||
- [MobileNet系列模型(包含v1,v2,v3)](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.4/docs/zh_CN/models/Mobile.md)
|
||
- [ShuffleNet系列模型](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.4/docs/zh_CN/models/Mobile.md)
|
||
- [SqueezeNet系列模型](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.4/docs/zh_CN/models/Others.md)
|
||
- [Inception系列模型](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.4/docs/zh_CN/models/Inception.md)
|
||
- [PP-HGNet系列模型](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.4/docs/zh_CN/models/PP-HGNet.md)
|
||
- [ResNet系列模型（包含vd系列）](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.4/docs/zh_CN/models/ResNet_and_vd.md)
|
||
|
||
### 2.1 准备PaddleClas部署模型
|
||
|
||
PaddleClas模型导出，请参考其文档说明[模型导出](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.4/docs/zh_CN/inference_deployment/export_model.md#2-%E5%88%86%E7%B1%BB%E6%A8%A1%E5%9E%8B%E5%AF%BC%E5%87%BA)
|
||
|
||
注意：PaddleClas导出的模型仅包含`inference.pdmodel`和`inference.pdiparams`两个文件，但为了满足部署的需求，同时也需准备其提供的通用[inference_cls.yaml](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.4/deploy/configs/inference_cls.yaml)文件，FastDeploy会从yaml文件中获取模型在推理时需要的预处理信息，开发者可直接下载此文件使用。但需根据自己的需求修改yaml文件中的配置参数，具体可比照PaddleClas模型训练[config](https://github.com/PaddlePaddle/PaddleClas/tree/release/2.4/ppcls/configs/ImageNet)中的infer部分的配置信息进行修改。
|
||
|
||
|
||
### 2.2 下载预训练模型
|
||
|
||
为了方便开发者的测试，下面提供了PaddleClas导出的部分模型（含inference_cls.yaml文件），开发者可直接下载使用。
|
||
|
||
| 模型 | 参数文件大小 |输入Shape | Top1 | Top5 |
|
||
|:---------------------------------------------------------------- |:----- |:----- | :----- | :----- |
|
||
| [PPLCNet_x1_0](https://bj.bcebos.com/paddlehub/fastdeploy/PPLCNet_x1_0_infer.tgz) | 12MB | 224x224 |71.32% | 90.03% |
|
||
| [PPLCNetV2_base](https://bj.bcebos.com/paddlehub/fastdeploy/PPLCNetV2_base_infer.tgz) | 26MB | 224x224 |77.04% | 93.27% |
|
||
| [EfficientNetB7](https://bj.bcebos.com/paddlehub/fastdeploy/EfficientNetB7_infer.tgz) | 255MB | 600x600 | 84.3% | 96.9% |
|
||
| [EfficientNetB0](https://bj.bcebos.com/paddlehub/fastdeploy/EfficientNetB0_infer.tgz)| 19MB | 224x224 | 77.38% | 93.31% |
|
||
| [EfficientNetB0_small](https://bj.bcebos.com/paddlehub/fastdeploy/EfficientNetB0_small_infer.tgz)| 18MB | 224x224 | 75.8% | 92.58% |
|
||
| [GhostNet_x1_3](https://bj.bcebos.com/paddlehub/fastdeploy/GhostNet_x1_3_infer.tgz) | 27MB | 224x224 | 75.79% | 92.54% |
|
||
| [GhostNet_x1_3_ssld](https://bj.bcebos.com/paddlehub/fastdeploy/GhostNet_x1_3_ssld_infer.tgz) | 29MB | 224x224 | 79.3% | 94.49% |
|
||
| [GhostNet_x0_5](https://bj.bcebos.com/paddlehub/fastdeploy/GhostNet_x0_5_infer.tgz) | 10MB | 224x224 | 66.8% | 86.9% |
|
||
| [MobileNetV1_x0_25](https://bj.bcebos.com/paddlehub/fastdeploy/MobileNetV1_x0_25_infer.tgz) | 1.9MB | 224x224 | 51.4% | 75.5% |
|
||
| [MobileNetV1_ssld](https://bj.bcebos.com/paddlehub/fastdeploy/MobileNetV1_ssld_infer.tgz) | 17MB | 224x224 | 77.9% | 93.9% |
|
||
| [MobileNetV2_x0_25](https://bj.bcebos.com/paddlehub/fastdeploy/MobileNetV2_x0_25_infer.tgz) | 5.9MB | 224x224 | 53.2% | 76.5% |
|
||
| [MobileNetV2](https://bj.bcebos.com/paddlehub/fastdeploy/MobileNetV2_infer.tgz) | 13MB | 224x224 | 72.15% | 90.65% |
|
||
| [MobileNetV2_ssld](https://bj.bcebos.com/paddlehub/fastdeploy/MobileNetV2_ssld_infer.tgz) | 14MB | 224x224 | 76.74% | 93.39% |
|
||
| [MobileNetV3_small_x1_0](https://bj.bcebos.com/paddlehub/fastdeploy/MobileNetV3_small_x1_0_infer.tgz) | 11MB | 224x224 | 68.24% | 88.06% |
|
||
| [MobileNetV3_small_x0_35_ssld](https://bj.bcebos.com/paddlehub/fastdeploy/MobileNetV3_small_x0_35_ssld_infer.tgz) | 6.4MB | 224x224 | 55.55% | 77.71% |
|
||
| [MobileNetV3_large_x1_0_ssld](https://bj.bcebos.com/paddlehub/fastdeploy/MobileNetV3_large_x1_0_ssld_infer.tgz) | 22MB | 224x224 | 78.96% | 94.48% |
|
||
| [ShuffleNetV2_x0_25](https://bj.bcebos.com/paddlehub/fastdeploy/ShuffleNetV2_x0_25_infer.tgz) | 2.4MB | 224x224 | 49.9% | 73.79% |
|
||
| [ShuffleNetV2_x2_0](https://bj.bcebos.com/paddlehub/fastdeploy/ShuffleNetV2_x2_0_infer.tgz) | 29MB | 224x224 | 73.15% | 91.2% |
|
||
| [SqueezeNet1_1](https://bj.bcebos.com/paddlehub/fastdeploy/SqueezeNet1_1_infer.tgz) | 4.8MB | 224x224 | 60.1% | 81.9% |
|
||
| [InceptionV3](https://bj.bcebos.com/paddlehub/fastdeploy/InceptionV3_infer.tgz) | 92MB | 299x299 | 79.14% | 94.59% |
|
||
| [PPHGNet_tiny_ssld](https://bj.bcebos.com/paddlehub/fastdeploy/PPHGNet_tiny_ssld_infer.tgz) | 57MB | 224x224 | 81.95% | 96.12% |
|
||
| [PPHGNet_small](https://bj.bcebos.com/paddlehub/fastdeploy/PPHGNet_small_infer.tgz) | 87MB | 224x224 | 81.51% | 95.82% |
|
||
| [PPHGNet_base_ssld](https://bj.bcebos.com/paddlehub/fastdeploy/PPHGNet_base_ssld_infer.tgz) | 274MB | 224x224 | 85.0% | 97.35% |
|
||
| [ResNet50_vd](https://bj.bcebos.com/paddlehub/fastdeploy/ResNet50_vd_infer.tgz) | 98MB | 224x224 | 79.12% | 94.44% |
|
||
| [ResNet50](https://bj.bcebos.com/paddlehub/fastdeploy/ResNet50_infer.tgz) | 91MB | 224x224 | 76.5% | 93% |
|
||
| [ResNeXt50_32x4d](https://bj.bcebos.com/paddlehub/fastdeploy/ResNeXt50_32x4d_infer.tgz) | 89MB | 224x224 | 77.75% | 93.82% |
|
||
| [DenseNet121](https://bj.bcebos.com/paddlehub/fastdeploy/DenseNet121_infer.tgz) | 29MB | 224x224 | 75.66% | 92.58% |
|
||
| [PULC_person_exists](https://bj.bcebos.com/paddlehub/fastdeploy/person_exists_infer.tgz) | 6MB | 224x224 | | |
|
||
| [ViT_large_patch16_224](https://bj.bcebos.com/paddlehub/fastdeploy/ViT_large_patch16_224_infer.tgz) | 1.1GB | 224x224 | 83.23% | 96.50%|
|
||
|
||
|
||
## 3. 详细部署的部署示例
|
||
- [Python部署](python)
|
||
- [C++部署](cpp)
|
||
- [C部署](c)
|
||
- [C#部署](csharp)
|