EdgeNeXt: Efficiently Amalgamated CNN-Transformer Architecture for Mobile Vision Applications
EdgeNeXt effectively combines the strengths of both CNN and Transformer models and is a new efficient hybrid architecture. EdgeNeXt introduces a split depth-wise transpose attention (SDTA) encoder that splits input tensors into multiple channel groups and utilizes depth-wise convolution along with self-attention across channel dimensions to implicitly increase the receptive field and encode multi-scale features.[1]
Figure 1. Architecture of EdgeNeXt [1]
Our reproduced model performance on ImageNet-1K is reported as follows.
| Model | Context | Top-1 (%) | Top-5 (%) | Params (M) | Recipe | Download |
|---|---|---|---|---|---|---|
| edgenext_xx_small | D910x8-G | 71.02 | 89.99 | 1.33 | yaml | weights |
| edgenext_x_small | D910x8-G | 75.14 | 92.50 | 2.34 | yaml | weights |
| edgenext_small | D910x8-G | 79.15 | 94.39 | 5.59 | yaml | weights |
| edgenext_base | D910x8-G | 82.24 | 95.94 | 18.51 | yaml | weights |
- 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.
Please refer to the installation instruction in MindCV.
Please download the ImageNet-1K dataset for model training and validation.
- 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
# distributed training on multiple GPU/Ascend devices
mpirun -n 8 python train.py --config configs/edgenext/edgenext_small_ascend.yaml --data_dir /path/to/imagenetIf the script is executed by the root user, the
--allow-run-as-rootparameter 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.
- Standalone Training
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/edgenext/edgenext_small_ascend.yaml --data_dir /path/to/dataset --distribute FalseTo 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/edgenext/edgenext_small_ascend.yaml --data_dir /path/to/imagenet --ckpt_path /path/to/ckpt
Please refer to the deployment tutorial in MindCV.
[1] Maaz M, Shaker A, Cholakkal H, et al. EdgeNeXt: efficiently amalgamated CNN-transformer architecture for Mobile vision applications[J]. arXiv preprint arXiv:2206.10589, 2022.