ReXNet¶

ReXNet: Rethinking Channel Dimensions for Efficient Model Design

Introduction¶

ReXNets is a new model achieved based on parameterization. It utilizes a new search method for a channel configuration via piece-wise linear functions of block index. The search space contains the conventions, and an effective channel configuration that can be parameterized by a linear function of the block index is used. ReXNets outperforms the recent lightweight models including NAS-based models and further showed remarkable fine-tuning performances on COCO object detection, instance segmentation, and fine-grained classifications.

Results¶

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

Model	Context	Top-1 (%)	Top-5 (%)	Params (M)	Recipe	Download
rexnet_x09	D910x8-G	77.07	93.41	4.13	yaml	weights
rexnet_x10	D910x8-G	77.38	93.60	4.84	yaml	weights
rexnet_x13	D910x8-G	79.06	94.28	7.61	yaml	weights
rexnet_x15	D910x8-G	79.94	94.74	9.79	yaml	weights
rexnet_x20	D910x8-G	80.6	94.99	16.45	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/vit/vit_b32_224_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/vit/vit_b32_224_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/vit/vit_b32_224_ascend.yaml --data_dir /path/to/imagenet --ckpt_path /path/to/ckpt

Deployment¶

To deploy online inference services with the trained model efficiently, please refer to the deployment tutorial.

References¶

[1] Han D, Yun S, Heo B, et al. Rethinking channel dimensions for efficient model design[C]//Proceedings of the IEEE/CVF conference on Computer Vision and Pattern Recognition. 2021: 732-741.