@@ -8,6 +8,8 @@ PaddleClas is an image recognition toolset for industry and academia, helping us
**Recent updates**
- 2022.4.21 Added the related [code](https://github.com/PaddlePaddle/PaddleClas/pull/1820/files) of the CVPR2022 oral paper [MixFormmer](https://arxiv.org/pdf/2204.02557.pdf).
- 2021.09.17 Add PP-LCNet series model developed by PaddleClas, these models show strong competitiveness on Intel CPUs.
For the introduction of PP-LCNet, please refer to [paper](https://arxiv.org/pdf/2109.15099.pdf) or [PP-LCNet model introduction](docs/en/models/PP-LCNet_en.md). The metrics and pretrained model are available [here](docs/en/ImageNet_models_en.md).
...
...
@@ -38,10 +40,11 @@ Four sample solutions are provided, including product recognition, vehicle recog
## Welcome to Join the Technical Exchange Group
* You can also scan the QR code below to join the PaddleClas WeChat group to get more efficient answers to your questions and to communicate with developers from all walks of life. We look forward to hearing from you.
* You can also scan the QR code below to join the PaddleClas QQ group and WeChat group (add and replay "C") to get more efficient answers to your questions and to communicate with developers from all walks of life. We look forward to hearing from you.
The highest accuracy of the validation set is around 0.415.
*** Note**
Here, multiple GPUs are used for training. If only one GPU is used, please specify the GPU with the `CUDA_VISIBLE_DEVICES` setting, and specify the GPU with the `--gpus` setting, the same below. For example, to train with only GPU 0:
* The GPUs specified in `--gpus` can be a subset of the GPUs specified in `CUDA_VISIBLE_DEVICES`.
* Since the initial learning rate and batch-size need to maintain a linear relationship, when training is switched from 4 GPUs to 1 GPU, the total batch-size is reduced to 1/4 of the original, and the learning rate also needs to be reduced to 1/4 of the original, so changed the default learning rate from 0.04 to 0.01.
* 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.
