• Stars
    star
    922
  • Rank 49,555 (Top 1.0 %)
  • Language
    Jupyter Notebook
  • License
    Apache License 2.0
  • Created over 3 years ago
  • Updated about 2 years ago

Reviews

There are no reviews yet. Be the first to send feedback to the community and the maintainers!

Repository Details

VOLO: Vision Outlooker for Visual Recognition

VOLO: Vision Outlooker for Visual Recognition, TPAMI, arxiv

This is a PyTorch implementation of our paper. We present Vision Outlooker (VOLO). We show that our VOLO achieves SOTA performance on ImageNet and CityScapes. No extra training data is used in our work.

ImageNet top-1 accuracy comparison with the state-of-the-art (sota) CNN-based and Transformer-based models. All results are based on the best test resolutions. Our VOLO-D5 achieves SOTA performance on ImageNet without extra data in 2021/06.

(Updating... codes and models for downstream tasks like semantic segmentation are coming soon.)

You may be also interested in our new MLP-like Model: Vision Permutator and our Token Labeling training objective for Vision Transformers.

Reference

@article{yuan2022volo,
  title={Volo: Vision outlooker for visual recognition},
  author={Yuan, Li and Hou, Qibin and Jiang, Zihang and Feng, Jiashi and Yan, Shuicheng},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
  year={2022},
  publisher={IEEE}
}

1. Requirements

torch>=1.7.0; torchvision>=0.8.0; timm==0.4.5; tlt==0.1.0; pyyaml; apex-amp

data prepare: ImageNet with the following folder structure, you can extract imagenet by this script.

β”‚imagenet/
β”œβ”€β”€train/
β”‚  β”œβ”€β”€ n01440764
β”‚  β”‚   β”œβ”€β”€ n01440764_10026.JPEG
β”‚  β”‚   β”œβ”€β”€ n01440764_10027.JPEG
β”‚  β”‚   β”œβ”€β”€ ......
β”‚  β”œβ”€β”€ ......
β”œβ”€β”€val/
β”‚  β”œβ”€β”€ n01440764
β”‚  β”‚   β”œβ”€β”€ ILSVRC2012_val_00000293.JPEG
β”‚  β”‚   β”œβ”€β”€ ILSVRC2012_val_00002138.JPEG
β”‚  β”‚   β”œβ”€β”€ ......
β”‚  β”œβ”€β”€ ......

Directory structure in this repo:

β”‚volo/
β”œβ”€β”€figures/
β”œβ”€β”€loss/
β”‚  β”œβ”€β”€ __init__.py
β”‚  β”œβ”€β”€ cross_entropy.py
β”œβ”€β”€models/
β”‚  β”œβ”€β”€ __init__.py
β”‚  β”œβ”€β”€ volo.py
β”œβ”€β”€utils/
β”‚  β”œβ”€β”€ __init__.py
β”‚  β”œβ”€β”€ utils.py
β”œβ”€β”€LICENSE
β”œβ”€β”€README.md
β”œβ”€β”€distributed_train.sh
β”œβ”€β”€main.py
β”œβ”€β”€validate.py

2. VOLO Models

Model #params Image resolution Top1 Acc Download
volo_d1 27M 224 84.2 here
volo_d1 ↑384 27M 384 85.2 here
volo_d2 59M 224 85.2 here
volo_d2 ↑384 59M 384 86.0 here
volo_d3 86M 224 85.4 here
volo_d3 ↑448 86M 448 86.3 here
volo_d4 193M 224 85.7 here
volo_d4 ↑448 193M 448 86.8 here
volo_d5 296M 224 86.1 here
volo_d5 ↑448 296M 448 87.0 here
volo_d5 ↑512 296M 512 87.1 here

All the pretrained models can also be downloaded by BaiDu Yun (password: ttbp).

Usage

Instructions on how to use our pre-trained VOLO models:

from models.volo import *
from utils import load_pretrained_weights 

# create model
model = volo_d1()

# load the pretrained weights
# change num_classes based on dataset, can work for different image size 
# as we interpolate the position embeding for different image size.
load_pretrained_weights(model, "/path/to/pretrained/weights", use_ema=False, 
                        strict=False, num_classes=1000)  

We also provide a Colab notebook which run the steps to perform inference with VOLO.

3. Validation

To evaluate our VOLO models, run:

python3 validate.py /path/to/imagenet  --model volo_d1 \
  --checkpoint /path/to/checkpoint --no-test-pool --apex-amp --img-size 224 -b 128

Change the --img-size from 224 to 384 or 448 for different image resolution, for example, to evaluate volo-d5 on 512 (87.1), run:

python3 validate.py /path/to/imagenet  --model volo_d5 \
  --checkpoint /path/to/volo_d5_512 --no-test-pool --apex-amp --img-size 512 -b 32

4. Train

As we use token labeling, please download the token labeling data in Google Drive or BaiDu Yun (password: y6j2), details about token labling are in here.

For each VOLO model, we first train it with image-size as 224 then finetune on image-size as 384 or 448/512:

train volo_d1 on 224 and finetune on 384 8 GPU, batch_size=1024, 19G GPU-memory in each GPU with apex-amp (mixed precision training)

Train volo_d1 on 224 with 310 epoch, acc=84.2

CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 ./distributed_train.sh 8 /path/to/imagenet \
  --model volo_d1 --img-size 224 \
  -b 128 --lr 1.6e-3 --drop-path 0.1 --apex-amp \
  --token-label --token-label-size 14 --token-label-data /path/to/token_label_data

Finetune on 384 with 40 epoch based on the pretrained checkpoint on 224, final acc=85.2 on 384

CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 ./distributed_train.sh 8 /path/to/imagenet \
  --model volo_d1 --img-size 384 \
  -b 64 --lr 8.0e-6 --min-lr 4.0e-6 --drop-path 0.1 --epochs 30 --apex-amp \
  --weight-decay 1.0e-8 --warmup-epochs 5  --ground-truth \
  --token-label --token-label-size 24 --token-label-data /path/to/token_label_data \
  --finetune /path/to/pretrained_224_volo_d1/
train volo_d2 on 224 and finetune on 384 8 GPU, batch_size=1024, 27G GPU-memory in each GPU with apex-amp (mixed precision training)

Train volo_d2 on 224 with 300 epoch, acc=85.2

CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 ./distributed_train.sh 8 /path/to/imagenet \
  --model volo_d2 --img-size 224 \
  -b 128 --lr 1.0e-3 --drop-path 0.2 --apex-amp \
  --token-label --token-label-size 14 --token-label-data /path/to/token_label_data

Finetune on 384 with 30 epoch based on the pretrained checkpoint on 224, final acc=86.0 on 384

CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 ./distributed_train.sh 8 /path/to/imagenet \
  --model volo_d2 --img-size 384 \
  -b 48 --lr 8.0e-6 --min-lr 4.0e-6 --drop-path 0.2 --epochs 30 --apex-amp \
  --weight-decay 1.0e-8 --warmup-epochs 5  --ground-truth \
  --token-label --token-label-size 24 --token-label-data /path/to/token_label_data \
  --finetune /path/to/pretrained_224_volo_d2/
train volo_d3 on 224 and finetune on 448

Train volo_d3 on 224 with 300 epoch, acc=85.4

CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 ./distributed_train.sh 8 /path/to/imagenet \
  --model volo_d3 --img-size 224 \
  -b 128 --lr 1.0e-3 --drop-path 0.5 --apex-amp \
  --token-label --token-label-size 14 --token-label-data /path/to/token_label_data

Finetune on 448 with 30 epoch based on the pretrained checkpoint on 224, final acc=86.3 on 448

CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 ./distributed_train.sh 8 /path/to/imagenet \
  --model volo_d3 --img-size 448 \
  -b 30 --lr 8.0e-6 --min-lr 4.0e-6 --drop-path 0.5 --epochs 30 --apex-amp \
  --weight-decay 1.0e-8 --warmup-epochs 5  --ground-truth \
  --token-label --token-label-size 28 --token-label-data /path/to/token_label_data \
  --finetune /path/to/pretrained_224_volo_d3/

5. Acknowledgement

We gratefully acknowledge the support of NVIDIA AI Tech Center (NVAITC) to this research project, especially the great helps in GPU technology supports from Terry Jianxiong Yin (NVAITC) and Qingyi Tao (NVAITC).

Related project: T2T-ViT, Token_labeling, pytorch-image-models, official imagenet example

LICENSE

This repo is under the Apache-2.0 license. For commercial use, please contact with the authors.

More Repositories

1

EditAnything

Edit anything in images powered by segment-anything, ControlNet, StableDiffusion, etc. (ACM MM)
Python
3,256
star
2

poolformer

PoolFormer: MetaFormer Is Actually What You Need for Vision (CVPR 2022 Oral)
Python
1,290
star
3

envpool

C++-based high-performance parallel environment execution engine (vectorized env) for general RL environments.
C++
1,084
star
4

Adan

Adan: Adaptive Nesterov Momentum Algorithm for Faster Optimizing Deep Models
Python
743
star
5

MDT

Masked Diffusion Transformer is the SOTA for image synthesis. (ICCV 2023)
Python
494
star
6

metaformer

MetaFormer Baselines for Vision (TPAMI 2024)
Python
414
star
7

lorahub

The official repository of paper "LoraHub: Efficient Cross-Task Generalization via Dynamic LoRA Composition".
Python
380
star
8

mvp

NeurIPS-2021: Direct Multi-view Multi-person 3D Human Pose Estimation
Python
324
star
9

CLoT

CVPR'24, Official Codebase of our Paper: "Let's Think Outside the Box: Exploring Leap-of-Thought in Large Language Models with Creative Humor Generation".
Python
290
star
10

inceptionnext

InceptionNeXt: When Inception Meets ConvNeXt (CVPR 2024)
Python
245
star
11

iFormer

iFormer: Inception Transformer
Python
226
star
12

ptp

[CVPR2023] The code for γ€ŠPosition-guided Text Prompt for Vision-Language Pre-training》
Python
148
star
13

BindDiffusion

BindDiffusion: One Diffusion Model to Bind Them All
Python
140
star
14

sailor-llm

βš“οΈ Sailor: Open Language Models for South-East Asia
Python
87
star
15

FDM

The official PyTorch implementation of Fast Diffusion Model
Python
83
star
16

mugs

A PyTorch implementation of Mugs proposed by our paper "Mugs: A Multi-Granular Self-Supervised Learning Framework".
Python
78
star
17

Agent-Smith

[ICML2024] Agent Smith: A Single Image Can Jailbreak One Million Multimodal LLM Agents Exponentially Fast
Python
69
star
18

sdft

[ACL 2024] The official codebase for the paper "Self-Distillation Bridges Distribution Gap in Language Model Fine-tuning".
Shell
67
star
19

symbolic-instruction-tuning

The official repository for the paper "From Zero to Hero: Examining the Power of Symbolic Tasks in Instruction Tuning".
Python
58
star
20

scaling-with-vocab

πŸ“ˆ Scaling Laws with Vocabulary: Larger Models Deserve Larger Vocabularies https://arxiv.org/abs/2407.13623
Python
52
star
21

ScaleLong

The official repository of paper "ScaleLong: Towards More Stable Training of Diffusion Model via Scaling Network Long Skip Connection" (NeurIPS 2023)
Python
47
star
22

VGT

Video Graph Transformer for Video Question Answering (ECCV'22)
Python
44
star
23

jax_xc

Exchange correlation functionals translated from libxc to jax
Python
43
star
24

d4ft

A JAX library for Density Functional Theory.
Python
40
star
25

finetune-fair-diffusion

Code of the paper: Finetuning Text-to-Image Diffusion Models for Fairness
Python
38
star
26

dice

Official implementation of Bootstrapping Language Models via DPO Implicit Rewards
Python
36
star
27

ILD

Imitation Learning via Differentiable Physics
Python
33
star
28

GP-Nerf

Official implementation for GP-NeRF (ECCV 2022)
Python
33
star
29

Consistent3D

The official PyTorch implementation of Consistent3D (CVPR 2024)
Python
33
star
30

edp

[NeurIPS 2023] Efficient Diffusion Policy
Python
32
star
31

rosmo

Codes for "Efficient Offline Policy Optimization with a Learned Model", ICLR2023
Python
28
star
32

MMCBench

Python
27
star
33

GDPO

Graph Diffusion Policy Optimization
Python
24
star
34

dualformer

Python
23
star
35

hloenv

an environment based on XLA for deep learning compiler optimization research.
C++
23
star
36

DiffMemorize

On Memorization in Diffusion Models
Python
21
star
37

optim4rl

Optim4RL is a Jax framework of learning to optimize for reinforcement learning.
Python
21
star
38

TEC

Python
15
star
39

numcc

NU-MCC: Multiview Compressive Coding with Neighborhood Decoder and Repulsive UDF
Python
12
star
40

PatchAIL

Implementation of PatchAIL in the ICLR 2023 paper <Visual Imitation with Patch Rewards>
Python
12
star
41

offbench

Python
11
star
42

OPER

code for the paper Offline Prioritized Experience Replay
Jupyter Notebook
11
star
43

win

Python
4
star
44

P-DoS

[ArXiv 2024] Denial-of-Service Poisoning Attacks on Large Language Models
Python
4
star
45

sailcompass

Python
3
star
46

SLRLA-optimizer

Python
2
star
47

Cheating-LLM-Benchmarks

Jupyter Notebook
2
star
48

I-FSJ

Improved Few-Shot Jailbreaking Can Circumvent Aligned Language Models and Their Defenses
Python
2
star
49

MISA

[NeurIPS 2023] Mutual Information Regularized Offline Reinforcement Learning
Python
1
star