EdgeNeXt¶

EdgeNeXt: Efficiently Amalgamated CNN-Transformer Architecture for Mobile Vision Applications

Introduction¶

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]

Results¶

Our reproduced model performance on ImageNet-1K is reported as follows.

Model	Context	Top-1 (%)	Top-5 (%)	Params (M)	Recipe	Download
edgenext_small	D910x8-G	79.15	94.39	5.59	yaml	weights

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 in MindCV.

Dataset Preparation¶

Please download the ImageNet-1K dataset for model training and validation.

Training¶

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

# distrubted training on multiple GPU/Ascend devices
mpirun -n 8 python train.py --config configs/edgenext/edgenext_small_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.

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 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/edgenext/edgenext_small_ascend.yaml --data_dir /path/to/imagenet --ckpt_path /path/to/ckpt

Deployment¶

Please refer to the deployment tutorial in MindCV.

References¶

[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.