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example/auto_compression/README.md
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...@@ -68,25 +68,38 @@ ACT相比传统的模型压缩方法， ...@@ -68,25 +68,38 @@ ACT相比传统的模型压缩方法，
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| 模型类型 | model name | 压缩前<br/>精度(Top1 Acc %) | 压缩后<br/>精度(Top1 Acc %) | 压缩前<br/>推理时延（ms） | 压缩后<br/>推理时延（ms） | 推理<br/>加速比 | 芯片 | | 模型类型 | model name | 压缩前<br/>精度(Top1 Acc %) | 压缩后<br/>精度(Top1 Acc %) | 压缩前<br/>推理时延（ms） | 压缩后<br/>推理时延（ms） | 推理<br/>加速比 | 芯片 |
| ------------------------------- | ---------------------------- | ---------------------- | ---------------------- | ---------------- | ---------------- | ---------- | ----------------- | | ------------------------------- | ----------------------------- | ---------------------- | ---------------------- | ---------------- | ---------------- | ---------- | --------------- |
| [图像分类](./image_classification) | MobileNetV1 | 70.90 | 70.57 | 33.15 | 13.64 | **2.43** | SDM865（骁龙865） | | [图像分类](./image_classification) | MobileNetV1 | 70.90 | 70.57 | 33.15 | 13.64 | **2.43** | SDM865（骁龙865） |
| [图像分类](./image_classification) | ShuffleNetV2_x1_0 | 68.65 | 68.32 | 10.43 | 5.51 | **1.89** | SDM865（骁龙865） | | [图像分类](./image_classification) | MobileNetV3_large_x1_0 | 75.32 | 74.04 | 16.62 | 9.85 | **1.69** | SDM865（骁龙865） |
| [图像分类](./image_classification) | SqueezeNet1_0_infer | 59.60 | 59.45 | 35.98 | 16.96 | **2.12** | SDM865（骁龙865） | | [图像分类](./image_classification) | MobileNetV3_large_x1_0_ssld | 78.96 | 77.17 | 16.62 | 9.85 | **1.69** | SDM865（骁龙865） |
| [图像分类](./image_classification) | PPLCNetV2_base | 76.86 | 76.43 | 36.50 | 15.79 | **2.31** | SDM865（骁龙865） | | [图像分类](./image_classification) | ShuffleNetV2_x1_0 | 68.65 | 68.32 | 10.43 | 5.51 | **1.89** | SDM865（骁龙865） |
| [图像分类](./image_classification) | ResNet50_vd | 79.12 | 78.74 | 3.19 | 0.92 | **3.47** | NVIDIA Tesla T4 | | [图像分类](./image_classification) | SqueezeNet1_0_infer | 59.60 | 59.45 | 35.98 | 16.96 | **2.12** | SDM865（骁龙865） |
| [语义分割](./semantic_segmentation) | PPHGNet_tiny | 79.59 | 79.20 | 2.82 | 0.98 | **2.88** | NVIDIA Tesla T4 | | [图像分类](./image_classification) | PPLCNetV2_base | 76.86 | 76.39 | 36.50 | 15.79 | **2.31** | SDM865（骁龙865） |
| [语义分割](./semantic_segmentation) | PP-HumanSeg-Lite | 92.87 | 92.35 | 56.36 | 37.71 | **1.49** | SDM710 | | [图像分类](./image_classification) | ResNet50_vd | 79.12 | 78.74 | 3.19 | 0.92 | **3.47** | NVIDIA Tesla T4 |
| [语义分割](./semantic_segmentation) | PP-LiteSeg | 77.04 | 76.93 | 1.43 | 1.16 | **1.23** | NVIDIA Tesla T4 | | [图像分类](./image_classification) | PPHGNet_tiny | 79.59 | 79.20 | 2.82 | 0.98 | **2.88** | NVIDIA Tesla T4 |
| [语义分割](./semantic_segmentation) | HRNet | 78.97 | 78.90 | 8.19 | 5.81 | **1.41** | NVIDIA Tesla T4 | | [图像分类](./image_classification) | InceptionV3 | 79.14 | 78.32 | 4.79 | 1.47 | **3.26** | NVIDIA Tesla T4 |
| [语义分割](./semantic_segmentation) | UNet | 65.00 | 64.93 | 15.29 | 10.23 | **1.49** | NVIDIA Tesla T4 | | [图像分类](./image_classification) | EfficientNetB0 | 77.02 | 74.27 | 1.95 | 1.44 | **1.35** | NVIDIA Tesla T4 |
| [NLP](./nlp) | PP-MiniLM | 72.81 | 72.44 | 128.01 | 17.97 | **7.12** | NVIDIA Tesla T4 | | [图像分类](./image_classification) | GhostNet_x1_0 | 74.02 | 72.62 | 2.93 | 1.03 | **2.84** | NVIDIA Tesla T4 |
| [NLP](./nlp) | ERNIE 3.0-Medium | 73.09 | 72.40 | 29.25(fp16) | 19.61 | **1.49** | NVIDIA Tesla T4 | | [语义分割](./semantic_segmentation) | PP-HumanSeg-Lite | 92.87 | 92.35 | 56.36 | 37.71 | **1.49** | SDM710 |
| [目标检测](./pytorch_yolo_series) | YOLOv5s<br/>(PyTorch) | 37.40 | 36.9 | 5.95 | 1.87 | **3.18** | NVIDIA Tesla T4 | | [语义分割](./semantic_segmentation) | PP-LiteSeg | 77.04 | 76.93 | 1.43 | 1.16 | **1.23** | NVIDIA Tesla T4 |
| [目标检测](./pytorch_yolo_series) | YOLOv6s<br/>(PyTorch) | 42.4 | 41.3 | 9.06 | 1.83 | **4.95** | NVIDIA Tesla T4 | | [语义分割](./semantic_segmentation) | HRNet | 78.97 | 78.90 | 8.188 | 5.812 | **1.41** | NVIDIA Tesla T4 |
| [目标检测](./pytorch_yolo_series) | YOLOv7<br/>(PyTorch) | 51.1 | 50.8 | 26.84 | 4.55 | **5.89** | NVIDIA Tesla T4 | | [语义分割](./semantic_segmentation) | UNet | 65.00 | 64.93 | 15.29 | 10.23 | **1.49** | NVIDIA Tesla T4 |
| [目标检测](./detection) | PP-YOLOE-s | 43.1 | 42.6 | 6.51 | 2.12 | **3.07** | NVIDIA Tesla T4 | | [语义分割](./semantic_segmentation) | Deeplabv3-ResNet50 | 79.90 | 79.26 | 12.766 | 8.839 | **1.44** | NVIDIA Tesla T4 |
| [图像分类](./image_classification) | MobileNetV1<br/>(TensorFlow) | 71.0 | 70.22 | 30.45 | 15.86 | **1.92** | SDMM865（骁龙865） | | [语义分割](./semantic_segmentation) | BiSeNetV2 | 73.17 | 73.20 | 35.61 | 15.94 | **2.23** | NVIDIA Tesla T4 |
| [NLP](./nlp) | PP-MiniLM | 72.81 | 72.44 | 128.01 | 17.97 | **7.12** | NVIDIA Tesla T4 |
| [NLP](./nlp) | ERNIE 3.0-Medium | 73.09 | 72.40 | 29.25(fp16) | 19.61 | **1.49** | NVIDIA Tesla T4 |
| [NLP](./pytorch_huggingface) | bert-base-cased（Hugging-Face） | 81.35 | 81.51 | 11.60 | 4.83 | **2.40** | NVIDIA Tesla T4 |
| [目标检测](./detection) | SSD-MobileNetv1 | 73.8(voc) | 73.52 | 4.0 | 1.7 | **2.35** | NVIDIA Tesla T4 |
| [目标检测](./pytorch_yolo_series) | YOLOv5s<br/>(PyTorch) | 37.4 | 36.9 | 5.95 | 1.87 | **3.18** | NVIDIA Tesla T4 |
| [目标检测](./pytorch_yolo_series) | YOLOv6s<br/>(PyTorch) | 42.4 | 41.3 | 9.06 | 1.83 | **4.95** | NVIDIA Tesla T4 |
| [目标检测](./pytorch_yolo_series) | YOLOv6s_v2(PyTorch) | 43.4 | 43.0 | 9.06 | 1.83 | **4.95** | NVIDIA Tesla T4 |
| [目标检测](./pytorch_yolo_series) | YOLOv7-Tiny(PyTorch) | 37.3 | 37.0 | 5.06 | 1.68 | **3.01** | NVIDIA Tesla T4 |
| [目标检测](./pytorch_yolo_series) | YOLOv7<br/>(PyTorch) | 51.1 | 50.8 | 26.84 | 4.55 | **5.89** | NVIDIA Tesla T4 |
| [目标检测](./detection) | PP-YOLOE-l | 50.9 | 50.6 | 11.2 | 6.7 | **1.67** | NVIDIA Tesla T4 |
| [目标检测](./detection) | PP-YOLOE-s | 43.1 | 42.6 | 6.51 | 2.12 | **3.07** | NVIDIA Tesla T4 |
| [图像分类](./image_classification) | MobileNetV1<br/>(TensorFlow) | 71.0 | 70.22 | 30.45 | 15.86 | **1.92** | SDMM865（骁龙865） |
- 备注：目标检测精度指标为mAP（0.5:0.95）精度测量结果。图像分割精度指标为IoU精度测量结果。 - 备注：目标检测精度指标为mAP（0.5:0.95）精度测量结果。图像分割精度指标为IoU精度测量结果。
- 更多飞桨模型应用示例及Benchmark可以参考：[图像分类](./image_classification)，[目标检测](./detection)，[语义分割](./semantic_segmentation)，[自然语言处理](./nlp) - 更多飞桨模型应用示例及Benchmark可以参考：[图像分类](./image_classification)，[目标检测](./detection)，[语义分割](./semantic_segmentation)，[自然语言处理](./nlp)
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