(NOTICE) Our paper has been accepted at CVPR 2021!! The paper has been updated at arxiv!
Dongyoon Han, Sangdoo Yun, Byeongho Heo, and YoungJoon Yoo | Paper | Pretrained Models
NAVER AI Lab
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.
- We first illustrate our models' top-acc. vs. computational costs graphs compared with 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 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
-
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 0.66B/7.6M 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
-
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
-
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
- Python3
- PyTorch (> 1.0)
- Torchvision (> 0.2)
- NumPy
-
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)))
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.
This project is distributed under MIT license.
@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}
}