Official PyTorch implementation of RepViT, from the following paper:

RepViT: Revisiting Mobile CNN From ViT Perspective.
Ao Wang, Hui Chen, Zijia Lin, Hengjun Pu, and Guiguang Ding
[arXiv]

Models are trained on ImageNet-1K and deployed on iPhone 12 with Core ML Tools to get latency.

UPDATES 🔥

• 2023/09/28: RepViT-M0.9/1.0/1.1/1.5/2.3 models have been released.
• 2023/07/27: RepViT models have been integrated into timm. See huggingface/pytorch-image-models#1876.

Tips: Convert a training-time RepViT into the inference-time structure

from timm.models import create_model
import utils

model = create_model('repvit_m0_9')
utils.replace_batchnorm(model)

The latency reported in RepViT for iPhone 12 (iOS 16) uses the benchmark tool from XCode 14. For example, here is a latency measurement of RepViT-M0.9:

Tips: export the model to Core ML model

python export_coreml.py --model repvit_m0_9 --ckpt pretrain/repvit_m0_9_distill_300e.pth

Tips: measure the throughput on GPU

python speed_gpu.py --model repvit_m0_9

conda virtual environment is recommended.

conda create -n repvit python=3.8
pip install -r requirements.txt

Download and extract ImageNet train and val images from http://image-net.org/. The training and validation data are expected to be in the train folder and val folder respectively:

|-- /path/to/imagenet/
    |-- train
    |-- val

To train RepViT-M0.9 on an 8-GPU machine:

python -m torch.distributed.launch --nproc_per_node=8 --master_port 12346 --use_env main.py --model repvit_m0_9 --data-path ~/imagenet --dist-eval

Tips: specify your data path and model name!

For example, to test RepViT-M0.9:

python main.py --eval --model repvit_m0_9 --resume pretrain/repvit_m0_9_distill_300e.pth --data-path ~/imagenet

Object Detection and Instance Segmentation
Semantic Segmentation

Classification (ImageNet) code base is partly built with LeViT, PoolFormer and EfficientFormer.

The detection and segmentation pipeline is from MMCV (MMDetection and MMSegmentation).

Thanks for the great implementations!

If our code or models help your work, please cite our paper:

@misc{wang2023repvit,
      title={RepViT: Revisiting Mobile CNN From ViT Perspective}, 
      author={Ao Wang and Hui Chen and Zijia Lin and Hengjun Pu and Guiguang Ding},
      year={2023},
      eprint={2307.09283},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}