mirror of
https://github.com/PaddlePaddle/FastDeploy.git
synced 2025-10-07 01:22:59 +08:00
a231c9e7f3
* Add PaddleOCR Support * Add PaddleOCR Support * Add PaddleOCRv3 Support * Add PaddleOCRv3 Support * Update README.md * Update README.md * Update README.md * Update README.md * Add PaddleOCRv3 Support * Add PaddleOCRv3 Supports * Add PaddleOCRv3 Suport * Fix Rec diff * Remove useless functions * Remove useless comments * Add PaddleOCRv2 Support * Add PaddleOCRv3 & PaddleOCRv2 Support * remove useless parameters * Add utils of sorting det boxes * Fix code naming convention * Fix code naming convention * Fix code naming convention * Fix bug in the Classify process * Imporve OCR Readme * Fix diff in Cls model * Update Model Download Link in Readme * Fix diff in PPOCRv2 * Improve OCR readme * Imporve OCR readme * Improve OCR readme * Improve OCR readme * Imporve OCR readme * Improve OCR readme * Fix conflict * Add readme for OCRResult * Improve OCR readme * Add OCRResult readme * Improve OCR readme * Improve OCR readme * Add Model Quantization Demo * Fix Model Quantization Readme * Fix Model Quantization Readme * Add the function to do PTQ quantization * Improve quant tools readme * Improve quant tool readme * Improve quant tool readme * Add PaddleInference-GPU for OCR Rec model * Add QAT method to fastdeploy-quantization tool * Remove examples/slim for now * Move configs folder * Add Quantization Support for Classification Model * Imporve ways of importing preprocess * Upload YOLO Benchmark on readme * Upload YOLO Benchmark on readme * Upload YOLO Benchmark on readme * Improve Quantization configs and readme * Add support for multi-inputs model * Add backends and params file for YOLOv7 * Add quantized model deployment support for YOLO series * Fix YOLOv5 quantize readme * Fix YOLO quantize readme * Fix YOLO quantize readme * Improve quantize YOLO readme * Improve quantize YOLO readme * Improve quantize YOLO readme * Improve quantize YOLO readme * Improve quantize YOLO readme * Fix bug, change Fronted to ModelFormat * Change Fronted to ModelFormat * Add examples to deploy quantized paddleclas models * Fix readme * Add quantize Readme * Add quantize Readme * Add quantize Readme * Modify readme of quantization tools * Modify readme of quantization tools * Improve quantization tools readme * Improve quantization readme * Improve PaddleClas quantized model deployment readme * Add PPYOLOE-l quantized deployment examples * Improve quantization tools readme * Improve Quantize Readme * Fix conflicts * Fix conflicts * improve readme * Improve quantization tools and readme * Improve quantization tools and readme * Add quantized deployment examples for PaddleSeg model * Fix cpp readme * Fix memory leak of reader_wrapper function * Fix model file name in PaddleClas quantization examples * Update Runtime and E2E benchmark * Update Runtime and E2E benchmark * Rename quantization tools to auto compression tools * Remove PPYOLOE data when deployed on MKLDNN * Fix readme * Support PPYOLOE with OR without NMS and update readme * Update Readme * Update configs and readme * Update configs and readme * Add Paddle-TensorRT backend in quantized model deploy examples * Support PPYOLOE+ series
PaddleClas 模型部署
模型版本说明
目前FastDeploy支持如下模型的部署
- PP-LCNet系列模型
- PP-LCNetV2系列模型
- EfficientNet系列模型
- GhostNet系列模型
- MobileNet系列模型(包含v1,v2,v3)
- ShuffleNet系列模型
- SqueezeNet系列模型
- Inception系列模型
- PP-HGNet系列模型
- ResNet系列模型（包含vd系列）
准备PaddleClas部署模型
PaddleClas模型导出，请参考其文档说明模型导出
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中的infer部分的配置信息进行修改。
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 | 12MB | 224x224 | 71.32% | 90.03% |
| PPLCNetV2_base | 26MB | 224x224 | 77.04% | 93.27% |
| EfficientNetB7 | 255MB | 600x600 | 84.3% | 96.9% |
| EfficientNetB0_small | 18MB | 224x224 | 75.8% | 75.8% |
| GhostNet_x1_3_ssld | 29MB | 224x224 | 75.7% | 92.5% |
| GhostNet_x0_5 | 10MB | 224x224 | 66.8% | 86.9% |
| MobileNetV1_x0_25 | 1.9MB | 224x224 | 51.4% | 75.5% |
| MobileNetV1_ssld | 17MB | 224x224 | 77.9% | 93.9% |
| MobileNetV2_x0_25 | 5.9MB | 224x224 | 53.2% | 76.5% |
| MobileNetV2_ssld | 14MB | 224x224 | 76.74% | 93.39% |
| MobileNetV3_small_x0_35_ssld | 6.4MB | 224x224 | 55.55% | 77.71% |
| MobileNetV3_large_x1_0_ssld | 22MB | 224x224 | 78.96% | 94.48% |
| ShuffleNetV2_x0_25 | 2.4MB | 224x224 | 49.9% | 73.79% |
| ShuffleNetV2_x2_0 | 29MB | 224x224 | 73.15% | 91.2% |
| SqueezeNet1_1 | 4.8MB | 224x224 | 60.1% | 81.9% |
| InceptionV3 | 92MB | 299x299 | 79.14% | 94.59% |
| PPHGNet_tiny_ssld | 57MB | 224x224 | 81.95% | 96.12% |
| PPHGNet_base_ssld | 274MB | 224x224 | 85.0% | 97.35% |
| ResNet50_vd | 98MB | 224x224 | 79.12% | 94.44% |