MindCV是一个基于 MindSpore 开发的，致力于计算机视觉相关技术研发的开源工具箱

# RepVGG <!--- Guideline: please use url linked to the paper abstract in ArXiv instead of PDF for fast loading. --> > [RepVGG: Making VGG-style ConvNets Great Again](https://arxiv.org/abs/2101.03697) ## Introduction <!--- Guideline: Introduce the model and architectures. Please cite if you use/adopt paper explanation from others. --> <!--- Guideline: If an architecture table/figure is available in the paper, please put one here and cite for intuitive illustration. --> The key idea of Repvgg is that by using re-parameterization, the model architecture could be trained in multi-branch but validated in single branch. Figure 1 shows the basic model architecture of Repvgg. By utilizing different values for a and b, we could get various repvgg models. Repvgg could achieve better model performance with smaller model parameters on ImageNet-1K dataset compared with previous methods.[[1](#references)] <p align="center"> <img src="https://user-images.githubusercontent.com/77485245/226785860-e109b0ea-eb6b-4464-91a9-8898b3e3e056.png" width=400 /> </p> <p align="center"> <em>Figure 1. Architecture of Repvgg [<a href="#references">1</a>] </em> </p> ## Results <!--- Guideline: Table Format: - Model: model name in lower case with _ seperator. - Context: Training context denoted as {device}x{pieces}-{MS mode}, where mindspore mode can be G - graph mode or F - pynative mode with ms function. For example, D910x8-G is for training on 8 pieces of Ascend 910 NPU using graph mode. - Top-1 and Top-5: Keep 2 digits after the decimal point. - Params (M): # of model parameters in millions (10^6). Keep 2 digits after the decimal point - Recipe: Training recipe/configuration linked to a yaml config file. Use absolute url path. - Download: url of the pretrained model weights. Use absolute url path. --> Our reproduced model performance on ImageNet-1K is reported as follows. <div align="center"> | Model | Context | Top-1 (%) | Top-5 (%) | Params (M) | Recipe | Download | |-----------|-----------|-----------|-----------|------------|------------------------------------------------------------------------------------------------|--------------------------------------------------------------------| | repvgg_a0 | D910x8-G | 72.19 | 90.75 | 9.13 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/repvgg/repvgg_a0_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/repvgg/repvgg_a0-6e71139d.ckpt) | | repvgg_a1 | D910x8-G | 74.19 | 91.89 | 14.12 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/repvgg/repvgg_a1_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/repvgg/repvgg_a1-539513ac.ckpt) | | repvgg_a2 | D910x8-G | 76.63 | 93.42 | 28.25 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/repvgg/repvgg_a2_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/repvgg/repvgg_a2-cdc90b11.ckpt) | | repvgg_b0 | D910x8-G | 74.99 | 92.40 | 15.85 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/repvgg/repvgg_b0_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/repvgg/repvgg_b0-54d5862c.ckpt) | | repvgg_b1 | D910x8-G | 78.81 | 94.37 | 57.48 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/repvgg/repvgg_b1_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/repvgg/repvgg_b1-4673797.ckpt) | | repvgg_b2 | D910x64-G | 79.29 | 94.66 | 89.11 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/repvgg/repvgg_b2_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/repvgg/repvgg_b2-7c91ccd4.ckpt) | | repvgg_b3 | D910x64-G | 80.46 | 95.34 | 123.19 | [yaml](https://github.com/mindspore-lab/mindcv/blob/main/configs/repvgg/repvgg_b3_ascend.yaml) | [weights](https://download.mindspore.cn/toolkits/mindcv/repvgg/repvgg_b3-30b35f52.ckpt) | </div> #### Notes - Context: Training context denoted as {device}x{pieces}-{MS mode}, where mindspore mode can be G - graph mode or F - pynative mode with ms function. For example, D910x8-G is for training on 8 pieces of Ascend 910 NPU using graph mode. - Top-1 and Top-5: Accuracy reported on the validation set of ImageNet-1K. ## Quick Start ### Preparation #### Installation Please refer to the [installation instruction](https://github.com/mindspore-lab/mindcv#installation) in MindCV. #### Dataset Preparation Please download the [ImageNet-1K](https://www.image-net.org/challenges/LSVRC/2012/index.php) dataset for model training and validation. ### Training <!--- Guideline: Please avoid using shell scripts in the command line. Python scripts preferred. --> * Distributed Training It is easy to reproduce the reported results with the pre-defined training recipe. For distributed training on multiple Ascend 910 devices, please run ```shell # distributed training on multiple GPU/Ascend devices mpirun -n 8 python train.py --config configs/repvgg/repvgg_a1_ascend.yaml --data_dir /path/to/imagenet ``` > If the script is executed by the root user, the `--allow-run-as-root` parameter must be added to `mpirun`. Similarly, you can train the model on multiple GPU devices with the above `mpirun` command. For detailed illustration of all hyper-parameters, please refer to [config.py](https://github.com/mindspore-lab/mindcv/blob/main/config.py). **Note:** As the global batch size (batch_size x num_devices) is an important hyper-parameter, it is recommended to keep the global batch size unchanged for reproduction or adjust the learning rate linearly to a new global batch size. * Standalone Training If you want to train or finetune the model on a smaller dataset without distributed training, please run: ```shell # standalone training on a CPU/GPU/Ascend device python train.py --config configs/repvgg/repvgg_a1_ascend.yaml --data_dir /path/to/dataset --distribute False ``` ### Validation To validate the accuracy of the trained model, you can use `validate.py` and parse the checkpoint path with `--ckpt_path`. ``` python validate.py -c configs/repvgg/repvgg_a1_ascend.yaml --data_dir /path/to/imagenet --ckpt_path /path/to/ckpt ``` ### Deployment Please refer to the [deployment tutorial](https://github.com/mindspore-lab/mindcv/blob/main/tutorials/deployment.md) in MindCV. ## References <!--- Guideline: Citation format GB/T 7714 is suggested. --> [1] Ding X, Zhang X, Ma N, et al. Repvgg: Making vgg-style convnets great again[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2021: 13733-13742.