XixinYang 1e3d2810a3 | 11 months ago | |
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README.md | 11 months ago | |
convnext_base_ascend.yaml | 1 year ago | |
convnext_small_ascend.yaml | 1 year ago | |
convnext_tiny_ascend.yaml | 1 year ago |
In this work, the authors reexamine the design spaces and test the limits of what a pure ConvNet can achieve.
The authors gradually "modernize" a standard ResNet toward the design of a vision Transformer, and discover several key
components that contribute to the performance difference along the way. The outcome of this exploration is a family of
pure ConvNet models dubbed ConvNeXt. Constructed entirely from standard ConvNet modules, ConvNeXts compete favorably
with Transformers in terms of accuracy and scalability, achieving 87.8% ImageNet top-1 accuracy, while maintaining the
simplicity and efficiency of standard ConvNets.[1]
Figure 1. Architecture of ConvNeXt [1]
Our reproduced model performance on ImageNet-1K is reported as follows.
Model | Context | Top-1 (%) | Top-5 (%) | Params (M) | Recipe | Download |
---|---|---|---|---|---|---|
ConvNeXt_tiny | D910x64-G | 81.91 | 95.79 | 28.59 | yaml | weights |
ConvNeXt_small | D910x64-G | 83.40 | 96.36 | 50.22 | yaml | weights |
ConvNeXt_base | D910x64-G | 83.32 | 96.24 | 88.59 | yaml | weights |
Please refer to the installation instruction in MindCV.
Please download the ImageNet-1K dataset for model training and validation.
It is easy to reproduce the reported results with the pre-defined training recipe. For distributed training on multiple Ascend 910 devices, please run
# distributed training on multiple GPU/Ascend devices
mpirun -n 8 python train.py --config configs/convnext/convnext_tiny_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 tompirun
.
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.
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.
If you want to train or finetune the model on a smaller dataset without distributed training, please run:
# standalone training on a CPU/GPU/Ascend device
python train.py --config configs/convnext/convnext_tiny_ascend.yaml --data_dir /path/to/dataset --distribute False
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/convnext/convnext_tiny_ascend.yaml --data_dir /path/to/imagenet --ckpt_path /path/to/ckpt
Please refer to the deployment tutorial in MindCV.
[1] Liu Z, Mao H, Wu C Y, et al. A convnet for the 2020s[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2022: 11976-11986.
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。它提供大量的计算机视觉领域的经典模型和SoTA模型以及它们的预训练权重。同时,还提供了AutoAugment等SoTA算法来提高性能。通过解耦的模块设计,您可以轻松地将MindCV应用到您自己的CV任务中。
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