VanillaNet: the Power of Minimalism in Deep Learning

Official PyTorch implementation of VanillaNet, from the following paper:
VanillaNet: the Power of Minimalism in Deep Learning
Hanting chen, Yunhe Wang, Jianyuan Guo and Dacheng Tao

VanillaNet is an innovative neural network architecture that focuses on simplicity and efficiency. Moving away from complex features such as shortcuts and attention mechanisms, VanillaNet uses a reduced number of layers while still maintaining excellent performance. This project showcases that it's possible to achieve effective results with a lean architecture, thereby setting a new path in the field of computer vision and challenging the status quo of foundation models.

Comparison of Depth and Speed

VanillaNet achieves comparable performance to prevalent computer vision foundation models, yet boasts a reduced depth and enhanced inference speed:

9-layers' VanillaNet achieves about 80% Top-1 accuracy with 3.59ms, over 100% speed increase compared to ResNet-50 (7.64ms).
13 layers' VanillaNet achieves about 83% Top-1 accuracy with 9.72ms, over 100% speed increase compared to Swin-T (20.25ms).

Downstream Tasks

Framework	Backbone	FLOPs(G)	#params(M)	FPS	AP^b	AP^m
RetinaNet	Swin-T	245	38.5	27.5	41.5	-
	VanillaNet-13	397	74.6	29.8	41.8	-
Mask RCNN	Swin-T	267	47.8	28.2	42.7	39.3
	VanillaNet-13	421	76.3	32.6	42.9	39.6

VanillaNet achieves a higher Frames Per Second (FPS) in detection and segmentation tasks.

Catalog

ImageNet-1K Testing Code
ImageNet-1K Training Code of VanillaNet-5 to VanillaNet-10
ImageNet-1K Pretrained Weights of VanillaNet-5 to VanillaNet-10
ImageNet-1K Training Code of VanillaNet-11 to VanillaNet-13
ImageNet-1K Pretrained Weights of VanillaNet-11 to VanillaNet-13
Downstream Transfer (Detection, Segmentation) Code

Results and Pre-trained Models

ImageNet-1K trained models

name	#params(M)	FLOPs(B)	Lacency(ms)	Acc(%)	model
VanillaNet-5	15.5	5.2	1.61	72.49	model
VanillaNet-6	32.5	6.0	2.01	76.36	model
VanillaNet-7	32.8	6.9	2.27	77.98	model
VanillaNet-8	37.1	7.7	2.56	79.13	model
VanillaNet-9	41.4	8.6	2.91	79.87	model
VanillaNet-10	45.7	9.4	3.24	80.57	model
VanillaNet-11	50.0	10.3	3.59	81.08	-
VanillaNet-12	54.3	11.1	3.82	81.55	-
VanillaNet-13	58.6	11.9	4.26	82.05	-
VanillaNet-13-1.5x	127.8	26.5	7.83	82.53	-
VanillaNet-13-1.5x†	127.8	48.9	9.72	83.11	-

Installation

The results are produced with torch==1.10.2+cu113 torchvision==0.11.3+cu113 timm==0.6.12. Other verions might also work.

Install Pytorch and, torchvision following official instructions.

Install required packages:

pip install timm==0.6.12
pip install cupy-cuda113
pip install torchprofile
pip install einops
pip install tensorboardX
pip install terminaltables

Testing

We give an example evaluation command for VanillaNet-5:

without deploy:

python -m torch.distributed.launch --nproc_per_node=1 main.py --model vanillanet_5 --data_path /path/to/imagenet-1k/ --real_labels /path/to/imagenet_real_labels.json --finetune /path/to/vanillanet_5.pth --eval True --model_key model_ema --crop_pct 0.875

with deploy:

python -m torch.distributed.launch --nproc_per_node=1 main.py --model vanillanet_5 --data_path /path/to/imagenet-1k/ --real_labels /path/to/imagenet_real_labels.json --finetune /path/to/vanillanet_5.pth --eval True --model_key model_ema --crop_pct 0.875 --switch_to_deploy /path/to/vanillanet_5_deploy.pth

Training

You can use the following command to train VanillaNet-5 on a single machine with 8 GPUs:

python -m torch.distributed.launch --nproc_per_node=8 main.py \
--model vanillanet_5 \
--data_path /path/to/imagenet-1k \
--batch_size 128 --update_freq 1  --epochs 300 --decay_epochs 100 \ 
--lr 3.5e-3 --weight_decay 0.35  --drop 0.05 \
--opt lamb --aa rand-m7-mstd0.5-inc1 --mixup 0.1 --bce_loss \
--output_dir /path/to/save_results \
--model_ema true --model_ema_eval true --model_ema_decay 0.99996 \
--use_amp true

Here, the effective batch size = --nproc_per_node * --batch_size * --update_freq. In the example above, the effective batch size is 8*128*1 = 1024.

To train other VanillaNet variants, --model need to be changed. Examples are given below.

VanillaNet-6

python -m torch.distributed.launch --nproc_per_node=8 main.py \
--model vanillanet_6 \
--data_path /path/to/imagenet-1k \
--batch_size 128 --update_freq 1  --epochs 300 --decay_epochs 100 \ 
--lr 4.8e-3 --weight_decay 0.32  --drop 0.05 \
--layer_decay 0.8 --layer_decay_num_layers 4 \
--opt lamb --aa rand-m7-mstd0.5-inc1 --mixup 0.15 --bce_loss \
--output_dir /path/to/save_results \
--model_ema true --model_ema_eval true --model_ema_decay 0.99996 \
--use_amp true

VanillaNet-7

python -m torch.distributed.launch --nproc_per_node=8 main.py \
--model vanillanet_7 \
--data_path /path/to/imagenet-1k \
--batch_size 128 --update_freq 1  --epochs 300 --decay_epochs 100 \ 
--lr 4.7e-3 --weight_decay 0.35  --drop 0.05 \
--layer_decay 0.8 --layer_decay_num_layers 5 \
--opt lamb --aa rand-m7-mstd0.5-inc1 --mixup 0.4 --bce_loss \
--output_dir /path/to/save_results \
--model_ema true --model_ema_eval true --model_ema_decay 0.99996 \
--use_amp true

VanillaNet-8

python -m torch.distributed.launch --nproc_per_node=8 main.py \
--model vanillanet_8 \
--data_path /path/to/imagenet-1k \
--batch_size 128 --update_freq 1  --epochs 300 --decay_epochs 100 \ 
--lr 3.5e-3 --weight_decay 0.3  --drop 0.05 \
--opt lamb --aa rand-m7-mstd0.5-inc1 --mixup 0.4 --bce_loss \
--output_dir /path/to/save_results \
--model_ema true --model_ema_eval true --model_ema_decay 0.99996 \
--use_amp true

VanillaNet-9

python -m torch.distributed.launch --nproc_per_node=8 main.py \
--model vanillanet_9 \
--data_path /path/to/imagenet-1k \
--batch_size 128 --update_freq 1  --epochs 300 --decay_epochs 100 \ 
--lr 3.5e-3 --weight_decay 0.3  --drop 0.05 \
--opt lamb --aa rand-m7-mstd0.5-inc1 --mixup 0.4 --bce_loss \
--output_dir /path/to/save_results \
--model_ema true --model_ema_eval true --model_ema_decay 0.99996 \
--use_amp true

VanillaNet-10

python -m torch.distributed.launch --nproc_per_node=8 main.py \
--model vanillanet_10 \
--data_path /path/to/imagenet-1k \
--batch_size 128 --update_freq 1  --epochs 300 --decay_epochs 100 \ 
--lr 3.5e-3 --weight_decay 0.25  --drop 0.05 \
--opt lamb --aa rand-m7-mstd0.5-inc1 --mixup 0.4 --bce_loss \
--output_dir /path/to/save_results \
--model_ema true --model_ema_eval true --model_ema_decay 0.99996 \
--use_amp true

Acknowledgement

This repository is built using the timm library, DeiT, BEiT, RegVGG, and ConvNeXt repositories.

License

This project is released under the MIT license. Please see the LICENSE file for more information.

Citation

If our work is useful for your research, please consider citing:

@article{chen2023vanillanet,
  title={VanillaNet: the Power of Minimalism in Deep Learning},
  author={Chen, Hanting and Wang, Yunhe and Guo, Jianyuan and Tao, Dacheng},
  journal={arXiv preprint arXiv:2305.12972},
  year={2023}
}

xqpinitial/VanillaNet