(NOTICE) All the ReXNet-lite's model files have been updated!

(NOTICE) Our paper has been accepted at CVPR 2021!! The paper has been updated at arxiv!

Rethinking Channel Dimensions for Efficient Model Design

Dongyoon Han, Sangdoo Yun, Byeongho Heo, and YoungJoon Yoo | Paper | Pretrained Models

NAVER AI Lab

Abstract

Designing an efficient model within the limited computational cost is challenging. We argue the accuracy of a lightweight model has been further limited by the design convention: a stage-wise configuration of the channel dimensions, which looks like a piecewise linear function of the network stage. In this paper, we study an effective channel dimension configuration towards better performance than the convention. To this end, we empirically study how to design a single layer properly by analyzing the rank of the output feature. We then investigate the channel configuration of a model by searching network architectures concerning the channel configuration under the computational cost restriction. Based on the investigation, we propose a simple yet effective channel configuration that can be parameterized by the layer index. As a result, our proposed model following the channel parameterization achieves remarkable performance on ImageNet classification and transfer learning tasks including COCO object detection, COCO instance segmentation, and fine-grained classifications.

Model performance

• We first illustrate our models' top-acc. vs. computational costs graphs compared with EfficientNets

Performance comparison

ReXNets vs EfficientNets

• The CPU latencies are tested on Xeon E5-2630_v4 with a single image and the GPU latencies are measured on a V100 GPU with the batchsize of 64.

• EfficientNets' scores are taken form arxiv v3 of the paper.

  Model	Input Res.	Top-1 acc.	Top-5 acc.	FLOPs/params.	CPU Lat./ GPU Lat.
  ReXNet_0.9	224x224	77.2	93.5	0.35B/4.1M	45ms/20ms
  EfficientNet-B0	224x224	77.3	93.5	0.39B/5.3M	47ms/23ms
  ReXNet_1.0	224x224	77.9	93.9	0.40B/4.8M	47ms/21ms
  EfficientNet-B1	240x240	79.2	94.5	0.70B/7.8M	70ms/37ms
  ReXNet_1.3	224x224	79.5	94.7	0.66B/7.6M	55ms/28ms
  EfficientNet-B2	260x260	80.3	95.0	1.0B/9.2M	77ms/48ms
  ReXNet_1.5	224x224	80.3	95.2	0.88B/9.7M	59ms/31ms
  EfficientNet-B3	300x300	81.7	95.6	1.8B/12M	100ms/78ms
  ReXNet_2.0	224x224	81.6	95.7	1.8B/19M	69ms/40ms

ReXNet-lites vs. EfficientNet-lites

• ReXNet-lites do not use SE-net an SiLU activations aiming to faster training and inference speed.

• We compare ReXNet-lites with EfficientNet-lites.

• Here the GPU latencies are measured on two M40 GPUs, we will update the number run on a V100 GPU soon.

  Model	Input Res.	Top-1 acc.	Top-5 acc.	FLOPs/params	CPU Lat./ GPU Lat.
  EfficientNet-lite0	224x224	75.1	-	0.41B/4.7M	30ms/49ms
  ReXNet-lite_1.0	224x224	76.2	92.8	0.41B/4.7M	31ms/49ms
  EfficientNet-lite1	240x240	76.7	-	0.63B/5.4M	44ms/73ms
  ReXNet-lite_1.3	224x224	77.8	93.8	0.65B/6.8M	36ms/61ms
  EfficientNet-lite2	260x260	77.6	-	0.90B/ 6.1M	48ms/93ms
  ReXNet-lite_1.5	224x224	78.6	94.2	0.84B/8.3M	39ms/68ms
  EfficientNet-lite3	280x280	79.8	-	1.4B/ 8.2M	60ms/131ms
  ReXNet-lite_2.0	224x224	80.2	95.0	1.5B/13M	49ms/90ms

ImageNet-1k Pretrained models

ImageNet classification results

• Please refer the following pretrained models. Top-1 and top-5 accuraies are reported with the computational costs.

• Note that all the models are trained and evaluated with 224x224 image size.

  Model	Input Res.	Top-1 acc.	Top-5 acc.	FLOPs/params
  ReXNet_1.0	224x224	77.9	93.9	0.40B/4.8M
  ReXNet_1.3	224x224	79.5	94.7	0.66B/7.6M
  ReXNet_1.5	224x224	80.3	95.2	0.88B/9.7M
  ReXNet_2.0	224x224	81.6	95.7	1.5B/16M
  ReXNet_3.0	224x224	82.8	96.2	3.4B/34M
  ReXNet-lite_1.0	224x224	76.2	92.8	0.41B/4.7M
  ReXNet-lite_1.3	224x224	77.8	93.8	0.65B/6.8M
  ReXNet-lite_1.5	224x224	78.6	94.2	0.84B/8.3M
  ReXNet-lite_2.0	224x224	80.2	95.0	1.5B/13M

Finetuning results

COCO Object detection

• The following results are trained with Faster RCNN with FPN:

  Backbone	Img. Size	B_AP (%)	B_AP_0.5 (%)	B_AP_0.75 (%)	Params.	FLOPs	Eval. set
  FBNet-C-FPN	1200x800	35.1	57.4	37.2	21.4M	119.0B	val2017
  EfficientNetB0-FPN	1200x800	38.0	60.1	40.4	21.0M	123.0B	val2017
  ReXNet_0.9-FPN	1200x800	38.0	60.6	40.8	20.1M	123.0B	val2017
  ReXNet_1.0-FPN	1200x800	38.5	60.6	41.5	20.7M	124.1B	val2017
  ResNet50-FPN	1200x800	37.6	58.2	40.9	41.8M	202.2B	val2017
  ResNeXt-101-FPN	1200x800	40.3	62.1	44.1	60.4M	272.4B	val2017
  ReXNet_2.2-FPN	1200x800	41.5	64.0	44.9	33.0M	153.8B	val2017

COCO instance segmentation

• The following results are trained with Mask RCNN with FPN, S_AP and B_AP denote segmentation AP and box AP, respectively:

  Backbone	Img. Size	S_AP (%)	S_AP_0.5 (%)	S_AP_0.75 (%)	B_AP (%)	B_AP_0.5 (%)	B_AP_0.75 (%)	Params.	FLOPs	Eval. set
  EfficientNetB0_FPN	1200x800	34.8	56.8	36.6	38.4	60.2	40.8	23.7M	123.0B	val2017
  ReXNet_0.9-FPN	1200x800	35.2	57.4	37.1	38.7	60.8	41.6	22.8M	123.0B	val2017
  ReXNet_1.0-FPN	1200x800	35.4	57.7	37.4	38.9	61.1	42.1	23.3M	124.1B	val2017
  ResNet50-FPN	1200x800	34.6	55.9	36.8	38.5	59.0	41.6	44.2M	207B	val2017
  ReXNet_2.2-FPN	1200x800	37.8	61.0	40.2	42.0	64.5	45.6	35.6M	153.8B	val2017

Getting Started

Requirements

• Python3
• PyTorch (> 1.0)
• Torchvision (> 0.2)
• NumPy

Using the pretrained models

• timm>=0.3.0 provides the wonderful wrap-up of ours models thanks to Ross Wightman. Otherwise, the models can be loaded as follows:

  • To use ReXNet on a GPU:

  import torch
  import rexnetv1
  
  model = rexnetv1.ReXNetV1(width_mult=1.0).cuda()
  model.load_state_dict(torch.load('./rexnetv1_1.0.pth'))
  model.eval()
  print(model(torch.randn(1, 3, 224, 224).cuda()))

  • To use ReXNet-lite on a CPU:

  import torch
  import rexnetv1_lite
  
  model = rexnetv1_lite.ReXNetV1_lite(multiplier=1.0)
  model.load_state_dict(torch.load('./rexnet_lite_1.0.pth', map_location=torch.device('cpu')))
  model.eval()
  print(model(torch.randn(1, 3, 224, 224)))

Training own ReXNet

ReXNet can be trained with any PyTorch training codes including ImageNet training in PyTorch with the model file and proper arguments. Since the provided model file is not complicated, we simply convert the model to train a ReXNet in other frameworks like MXNet. For MXNet, we recommend MXnet-gluoncv as a training code.

Using PyTorch, we trained ReXNets with one of the popular imagenet classification code, Ross Wightman's pytorch-image-models for more efficient training. After including ReXNet's model file into the training code, one can train ReXNet-1.0x with the following command line:

./distributed_train.sh 4 /imagenet/ --model rexnetv1 --rex-width-mult 1.0 --opt sgd --amp \
 --lr 0.5 --weight-decay 1e-5 \
 --batch-size 128 --epochs 400 --sched cosine \
 --remode pixel --reprob 0.2 --drop 0.2 --aa rand-m9-mstd0.5 

Using droppath or MixUP may need to train a bigger model.

License

This project is distributed under MIT license.

How to cite

@misc{han2021rethinking,
      title={Rethinking Channel Dimensions for Efficient Model Design}, 
      author={Dongyoon Han and Sangdoo Yun and Byeongho Heo and YoungJoon Yoo},
      year={2021},
      eprint={2007.00992},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}