@@ -91,38 +92,38 @@ Since the introduction of GoogLeNet, GAP (Global-Average-Pooling) is often direc
For image classification, ImageNet dataset is adopted. Compared with the current mainstream lightweight network, PP-LCNet can obtain faster inference speed with the same accuracy. When using Baidu’s self-developed SSLD distillation strategy, the accuracy is further improved, with the Top-1 Acc of ImageNet exceeding 80% at an inference speed of about 5ms on the Intel CPU side.
where `_ssld` represents the model after using `SSLD distillation`. For details about `SSLD distillation`, see [SSLD distillation](../advanced_tutorials/knowledge_distillation_en.md).
Performance comparison with other lightweight networks:
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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## 6. Reference
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## 8. Reference
Reference to cite when you use PP-LCNet in a paper:
The highest accuracy of the validation set is around 0.415.
** 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.
