polish some docs
cuicheng01
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@ -15,6 +15,7 @@
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- [4.1 Image Classification](#4.1)
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- [4.2 Object Detection](#4.2)
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- [4.3 Semantic Segmentation](#4.3)
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- [5. Conclusion](#5)
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- [6. Reference](#6)
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@ -93,36 +94,36 @@ For image classification, ImageNet dataset is adopted. Compared with the current
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| Model | Params(M) | FLOPs(M) | Top-1 Acc(\%) | Top-5 Acc(\%) | Latency(ms) |
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|-------|-----------|----------|---------------|---------------|-------------|
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| PP-LCNet-0.25x | 1.5 | 18 | 51.86 | 75.65 | 1.74 |
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| PP-LCNet-0.35x | 1.6 | 29 | 58.09 | 80.83 | 1.92 |
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| PP-LCNet-0.5x | 1.9 | 47 | 63.14 | 84.66 | 2.05 |
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| PP-LCNet-0.75x | 2.4 | 99 | 68.18 | 88.30 | 2.29 |
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| PP-LCNet-1x | 3.0 | 161 | 71.32 | 90.03 | 2.46 |
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| PP-LCNet-1.5x | 4.5 | 342 | 73.71 | 91.53 | 3.19 |
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| PP-LCNet-2x | 6.5 | 590 | 75.18 | 92.27 | 4.27 |
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| PP-LCNet-2.5x | 9.0 | 906 | 76.60 | 93.00 | 5.39 |
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| PP-LCNet-0.5x\* | 1.9 | 47 | 66.10 | 86.46 | 2.05 |
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| PP-LCNet-1.0x\* | 3.0 | 161 | 74.39 | 92.09 | 2.46 |
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| PP-LCNet-2.5x\* | 9.0 | 906 | 80.82 | 95.33 | 5.39 |
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| PPLCNet_x0_25 | 1.5 | 18 | 51.86 | 75.65 | 1.74 |
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| PPLCNet_x0_35 | 1.6 | 29 | 58.09 | 80.83 | 1.92 |
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| PPLCNet_x0_5 | 1.9 | 47 | 63.14 | 84.66 | 2.05 |
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| PPLCNet_x0_75 | 2.4 | 99 | 68.18 | 88.30 | 2.29 |
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| PPLCNet_x1_0 | 3.0 | 161 | 71.32 | 90.03 | 2.46 |
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| PPLCNet_x1_5 | 4.5 | 342 | 73.71 | 91.53 | 3.19 |
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| PPLCNet_x2_0 | 6.5 | 590 | 75.18 | 92.27 | 4.27 |
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| PPLCNet_x2_5 | 9.0 | 906 | 76.60 | 93.00 | 5.39 |
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| PPLCNet_x0_5_ssld | 1.9 | 47 | 66.10 | 86.46 | 2.05 |
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| PPLCNet_x1_0_ssld | 3.0 | 161 | 74.39 | 92.09 | 2.46 |
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| PPLCNet_x2_5_ssld | 9.0 | 906 | 80.82 | 95.33 | 5.39 |
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\* denotes the model after using SSLD distillation.
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where `_ssld` represents the model after using `SSLD distillation`. For details about `SSLD distillation`, see [SSLD distillation](../advanced_tutorials/knowledge_distillation_en.md).
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Performance comparison with other lightweight networks:
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| Model | Params(M) | FLOPs(M) | Top-1 Acc(\%) | Top-5 Acc(\%) | Latency(ms) |
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|-------|-----------|----------|---------------|---------------|-------------|
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| MobileNetV2-0.25x | 1.5 | 34 | 53.21 | 76.52 | 2.47 |
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| MobileNetV3-small-0.35x | 1.7 | 15 | 53.03 | 76.37 | 3.02 |
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| ShuffleNetV2-0.33x | 0.6 | 24 | 53.73 | 77.05 | 4.30 |
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| <b>PP-LCNet-0.25x<b> | <b>1.5<b> | <b>18<b> | <b>51.86<b> | <b>75.65<b> | <b>1.74<b> |
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| MobileNetV2-0.5x | 2.0 | 99 | 65.03 | 85.72 | 2.85 |
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| MobileNetV3-large-0.35x | 2.1 | 41 | 64.32 | 85.46 | 3.68 |
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| ShuffleNetV2-0.5x | 1.4 | 43 | 60.32 | 82.26 | 4.65 |
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| <b>PP-LCNet-0.5x<b> | <b>1.9<b> | <b>47<b> | <b>63.14<b> | <b>84.66<b> | <b>2.05<b> |
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| MobileNetV1-1x | 4.3 | 578 | 70.99 | 89.68 | 3.38 |
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| MobileNetV2-1x | 3.5 | 327 | 72.15 | 90.65 | 4.26 |
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| MobileNetV3-small-1.25x | 3.6 | 100 | 70.67 | 89.51 | 3.95 |
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| <b>PP-LCNet-1x<b> |<b> 3.0<b> | <b>161<b> | <b>71.32<b> | <b>90.03<b> | <b>2.46<b> |
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| MobileNetV2_x0_25 | 1.5 | 34 | 53.21 | 76.52 | 2.47 |
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| MobileNetV3_small_x0_35 | 1.7 | 15 | 53.03 | 76.37 | 3.02 |
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| ShuffleNetV2_x0_33 | 0.6 | 24 | 53.73 | 77.05 | 4.30 |
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| <b>PPLCNet_x0_25<b> | <b>1.5<b> | <b>18<b> | <b>51.86<b> | <b>75.65<b> | <b>1.74<b> |
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| MobileNetV2_x0_5 | 2.0 | 99 | 65.03 | 85.72 | 2.85 |
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| MobileNetV3_large_x0_35 | 2.1 | 41 | 64.32 | 85.46 | 3.68 |
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| ShuffleNetV2_x0_5 | 1.4 | 43 | 60.32 | 82.26 | 4.65 |
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| <b>PPLCNet_x0_5<b> | <b>1.9<b> | <b>47<b> | <b>63.14<b> | <b>84.66<b> | <b>2.05<b> |
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| MobileNetV1_x1_0 | 4.3 | 578 | 70.99 | 89.68 | 3.38 |
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| MobileNetV2_x1_0 | 3.5 | 327 | 72.15 | 90.65 | 4.26 |
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| MobileNetV3_small_x1_25 | 3.6 | 100 | 70.67 | 89.51 | 3.95 |
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| <b>PPLCNet_x1_0<b> |<b> 3.0<b> | <b>161<b> | <b>71.32<b> | <b>90.03<b> | <b>2.46<b> |
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<a name="4.2"></a>
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### 4.2 Object Detection
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@ -131,10 +132,10 @@ For object detection, we adopt Baidu’s self-developed PicoDet, which focuses o
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| Backbone | mAP(%) | Latency(ms) |
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|-------|-----------|----------|
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MobileNetV3-large-0.35x | 19.2 | 8.1 |
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<b>PP-LCNet-0.5x<b> | <b>20.3<b> | <b>6.0<b> |
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MobileNetV3-large-0.75x | 25.8 | 11.1 |
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<b>PP-LCNet-1x<b> | <b>26.9<b> | <b>7.9<b> |
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MobileNetV3_large_x0_35 | 19.2 | 8.1 |
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<b>PPLCNet_x0_5<b> | <b>20.3<b> | <b>6.0<b> |
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MobileNetV3_large_x0_75 | 25.8 | 11.1 |
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<b>PPLCNet_x1_0<b> | <b>26.9<b> | <b>7.9<b> |
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<a name="4.3"></a>
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### 4.3 Semantic Segmentation
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@ -143,18 +144,46 @@ For semantic segmentation, DeeplabV3+ is adopted. The following table presents t
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| Backbone | mIoU(%) | Latency(ms) |
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|-------|-----------|----------|
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MobileNetV3-large-0.5x | 55.42 | 135 |
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<b>PP-LCNet-0.5x<b> | <b>58.36<b> | <b>82<b> |
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MobileNetV3-large-0.75x | 64.53 | 151 |
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<b>PP-LCNet-1x<b> | <b>66.03<b> | <b>96<b> |
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MobileNetV3_large_x0_5 | 55.42 | 135 |
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<b>PPLCNet_x0_5<b> | <b>58.36<b> | <b>82<b> |
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MobileNetV3_large_x0_75 | 64.53 | 151 |
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<b>PPLCNet_x1_0<b> | <b>66.03<b> | <b>96<b> |
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<a name="5"></a>
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## 5. Conclusion
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## 5. Inference speed based on V100 GPU
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| Models | Crop Size | Resize Short Size | FP32<br>Batch Size=1<br>(ms) | FP32<br/>Batch Size=1\4<br/>(ms) | FP32<br/>Batch Size=8<br/>(ms) |
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| ------------- | --------- | ----------------- | ---------------------------- | -------------------------------- | ------------------------------ |
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| PPLCNet_x0_25 | 224 | 256 | 0.72 | 1.17 | 1.71 |
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| PPLCNet_x0_35 | 224 | 256 | 0.69 | 1.21 | 1.82 |
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| PPLCNet_x0_5 | 224 | 256 | 0.70 | 1.32 | 1.94 |
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| PPLCNet_x0_75 | 224 | 256 | 0.71 | 1.49 | 2.19 |
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| PPLCNet_x1_0 | 224 | 256 | 0.73 | 1.64 | 2.53 |
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| PPLCNet_x1_5 | 224 | 256 | 0.82 | 2.06 | 3.12 |
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| PPLCNet_x2_0 | 224 | 256 | 0.94 | 2.58 | 4.08 |
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<a name="6"></a>
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## 6. Inference speed based on SD855
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| Models | SD855 time(ms)<br>bs=1, thread=1 | SD855 time(ms)<br/>bs=1, thread=2 | SD855 time(ms)<br/>bs=1, thread=4 |
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| ------------- | -------------------------------- | --------------------------------- | --------------------------------- |
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| PPLCNet_x0_25 | 2.30 | 1.62 | 1.32 |
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| PPLCNet_x0_35 | 3.15 | 2.11 | 1.64 |
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| PPLCNet_x0_5 | 4.27 | 2.73 | 1.92 |
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| PPLCNet_x0_75 | 7.38 | 4.51 | 2.91 |
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| PPLCNet_x1_0 | 10.78 | 6.49 | 3.98 |
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| PPLCNet_x1_5 | 20.55 | 12.26 | 7.54 |
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| PPLCNet_x2_0 | 33.79 | 20.17 | 12.10 |
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| PPLCNet_x2_5 | 49.89 | 29.60 | 17.82 |
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<a name="7"></a>
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## 7. Conclusion
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Rather than holding on to perfect FLOPs and Params as academics do, PP-LCNet focuses on analyzing how to add Intel CPU-friendly modules to improve the performance of the model, which can better balance accuracy and inference time. The experimental conclusions therein are available to other researchers in network structure design, while providing NAS search researchers with a smaller search space and general conclusions. The finished PP-LCNet can also be better accepted and applied in industry.
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<a name="6"></a>
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## 6. Reference
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<a name="8"></a>
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## 8. Reference
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Reference to cite when you use PP-LCNet in a paper:
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```
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@ -75,6 +75,8 @@ python3 -m paddle.distributed.launch \
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The highest accuracy of the validation set is around 0.415.
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** Note** If the number of GPU cards is not 4, the accuracy of the validation set may be different from 0.415. To maintain a comparable accuracy, you need to change the learning rate in the configuration file to the current learning rate / 4 \* current card number. The same below.
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<a name="2.1.2"></a>
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@ -97,36 +97,36 @@ BaseNet 经过以上四个方面的改进，得到了 PP-LCNet。下表进一步
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| Model | Params(M) | FLOPs(M) | Top-1 Acc(\%) | Top-5 Acc(\%) | Latency(ms) |
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|-------|-----------|----------|---------------|---------------|-------------|
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| PP-LCNet-0.25x | 1.5 | 18 | 51.86 | 75.65 | 1.74 |
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| PP-LCNet-0.35x | 1.6 | 29 | 58.09 | 80.83 | 1.92 |
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| PP-LCNet-0.5x | 1.9 | 47 | 63.14 | 84.66 | 2.05 |
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| PP-LCNet-0.75x | 2.4 | 99 | 68.18 | 88.30 | 2.29 |
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| PP-LCNet-1x | 3.0 | 161 | 71.32 | 90.03 | 2.46 |
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| PP-LCNet-1.5x | 4.5 | 342 | 73.71 | 91.53 | 3.19 |
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| PP-LCNet-2x | 6.5 | 590 | 75.18 | 92.27 | 4.27 |
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| PP-LCNet-2.5x | 9.0 | 906 | 76.60 | 93.00 | 5.39 |
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| PP-LCNet-0.5x\* | 1.9 | 47 | 66.10 | 86.46 | 2.05 |
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| PP-LCNet-1.0x\* | 3.0 | 161 | 74.39 | 92.09 | 2.46 |
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| PP-LCNet-2.5x\* | 9.0 | 906 | 80.82 | 95.33 | 5.39 |
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| PPLCNet_x0_25 | 1.5 | 18 | 51.86 | 75.65 | 1.74 |
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| PPLCNet_x0_35 | 1.6 | 29 | 58.09 | 80.83 | 1.92 |
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| PPLCNet_x0_5 | 1.9 | 47 | 63.14 | 84.66 | 2.05 |
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| PPLCNet_x0_75 | 2.4 | 99 | 68.18 | 88.30 | 2.29 |
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| PPLCNet_x1_0 | 3.0 | 161 | 71.32 | 90.03 | 2.46 |
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| PPLCNet_x1_5 | 4.5 | 342 | 73.71 | 91.53 | 3.19 |
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| PPLCNet_x2_0 | 6.5 | 590 | 75.18 | 92.27 | 4.27 |
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| PPLCNet_x2_5 | 9.0 | 906 | 76.60 | 93.00 | 5.39 |
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| PPLCNet_x0_5_ssld | 1.9 | 47 | 66.10 | 86.46 | 2.05 |
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| PPLCNet_x1_0_ssld | 3.0 | 161 | 74.39 | 92.09 | 2.46 |
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| PPLCNet_x2_5_ssld | 9.0 | 906 | 80.82 | 95.33 | 5.39 |
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其中\*表示使用 SSLD 蒸馏后的模型。
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其中 `_ssld` 表示使用 `SSLD 蒸馏`后的模型。关于 `SSLD蒸馏` 的内容，详情 [SSLD 蒸馏](../advanced_tutorials/knowledge_distillation.md)。
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与其他轻量级网络的性能对比：
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| Model | Params(M) | FLOPs(M) | Top-1 Acc(\%) | Top-5 Acc(\%) | Latency(ms) |
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|-------|-----------|----------|---------------|---------------|-------------|
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| MobileNetV2-0.25x | 1.5 | 34 | 53.21 | 76.52 | 2.47 |
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| MobileNetV3-small-0.35x | 1.7 | 15 | 53.03 | 76.37 | 3.02 |
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| ShuffleNetV2-0.33x | 0.6 | 24 | 53.73 | 77.05 | 4.30 |
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| <b>PP-LCNet-0.25x<b> | <b>1.5<b> | <b>18<b> | <b>51.86<b> | <b>75.65<b> | <b>1.74<b> |
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| MobileNetV2-0.5x | 2.0 | 99 | 65.03 | 85.72 | 2.85 |
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| MobileNetV3-large-0.35x | 2.1 | 41 | 64.32 | 85.46 | 3.68 |
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| ShuffleNetV2-0.5x | 1.4 | 43 | 60.32 | 82.26 | 4.65 |
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| <b>PP-LCNet-0.5x<b> | <b>1.9<b> | <b>47<b> | <b>63.14<b> | <b>84.66<b> | <b>2.05<b> |
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| MobileNetV1-1x | 4.3 | 578 | 70.99 | 89.68 | 3.38 |
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| MobileNetV2-1x | 3.5 | 327 | 72.15 | 90.65 | 4.26 |
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| MobileNetV3-small-1.25x | 3.6 | 100 | 70.67 | 89.51 | 3.95 |
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| <b>PP-LCNet-1x<b> |<b> 3.0<b> | <b>161<b> | <b>71.32<b> | <b>90.03<b> | <b>2.46<b> |
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| MobileNetV2_x0_25 | 1.5 | 34 | 53.21 | 76.52 | 2.47 |
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| MobileNetV3_small_x0_35 | 1.7 | 15 | 53.03 | 76.37 | 3.02 |
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| ShuffleNetV2_x0_33 | 0.6 | 24 | 53.73 | 77.05 | 4.30 |
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| <b>PPLCNet_x0_25<b> | <b>1.5<b> | <b>18<b> | <b>51.86<b> | <b>75.65<b> | <b>1.74<b> |
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| MobileNetV2_x0_5 | 2.0 | 99 | 65.03 | 85.72 | 2.85 |
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| MobileNetV3_large_x0_35 | 2.1 | 41 | 64.32 | 85.46 | 3.68 |
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| ShuffleNetV2_x0_5 | 1.4 | 43 | 60.32 | 82.26 | 4.65 |
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| <b>PPLCNet_x0_5<b> | <b>1.9<b> | <b>47<b> | <b>63.14<b> | <b>84.66<b> | <b>2.05<b> |
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| MobileNetV1_x1_0 | 4.3 | 578 | 70.99 | 89.68 | 3.38 |
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| MobileNetV2_x1_0 | 3.5 | 327 | 72.15 | 90.65 | 4.26 |
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| MobileNetV3_small_x1_25 | 3.6 | 100 | 70.67 | 89.51 | 3.95 |
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| <b>PPLCNet_x1_0<b> |<b> 3.0<b> | <b>161<b> | <b>71.32<b> | <b>90.03<b> | <b>2.46<b> |
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<a name="4.2"></a>
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### 4.2 目标检测
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@ -135,10 +135,10 @@ BaseNet 经过以上四个方面的改进，得到了 PP-LCNet。下表进一步
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| Backbone | mAP(%) | Latency(ms) |
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|-------|-----------|----------|
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MobileNetV3-large-0.35x | 19.2 | 8.1 |
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<b>PP-LCNet-0.5x<b> | <b>20.3<b> | <b>6.0<b> |
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MobileNetV3-large-0.75x | 25.8 | 11.1 |
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<b>PP-LCNet-1x<b> | <b>26.9<b> | <b>7.9<b> |
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MobileNetV3_large_x0_35 | 19.2 | 8.1 |
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<b>PPLCNet_x0_5<b> | <b>20.3<b> | <b>6.0<b> |
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MobileNetV3_large_x0_75 | 25.8 | 11.1 |
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<b>PPLCNet_x1_0<b> | <b>26.9<b> | <b>7.9<b> |
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<a name="4.3"></a>
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### 4.3 语义分割
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@ -147,10 +147,10 @@ MobileNetV3-large-0.75x | 25.8 | 11.1 |
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| Backbone | mIoU(%) | Latency(ms) |
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|-------|-----------|----------|
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|MobileNetV3-large-0.5x | 55.42 | 135 |
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|<b>PP-LCNet-0.5x<b> | <b>58.36<b> | <b>82<b> |
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|MobileNetV3-large-0.75x | 64.53 | 151 |
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|<b>PP-LCNet-1x<b> | <b>66.03<b> | <b>96<b> |
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MobileNetV3_large_x0_5 | 55.42 | 135 |
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<b>PPLCNet_x0_5<b> | <b>58.36<b> | <b>82<b> |
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MobileNetV3_large_x0_75 | 64.53 | 151 |
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<b>PPLCNet_x1_0<b> | <b>66.03<b> | <b>96<b> |
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<a name="5"></a>
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@ -75,6 +75,8 @@ python3 -m paddle.distributed.launch \
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验证集的最高准确率为 0.415 左右。
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** 注意** 如果 GPU 卡数不是 4，验证集的准确率可能与 0.415 有差异，若需保持相当的准确率，需要将配置文件中的学习率改为当前学习率 / 4 \* 当前卡数。下同。
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<a name="2.1.2"></a>
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