【复现】在CUB_200_2011数据集上复现分类程序IELT

我是在程序中，IELT-main/utils/dataset.py中设置download=True让它自己下载的。或者直接下zip也挺好用，有的时候git clone会出问题，下zip目前没什么问题。唯独还没看到测试的代码，没找得到测试方法，没找到可视化方法，未能应用于自己的数据集。缺啥装啥，由于代码比较新（复现于2023/12/8），我没遇到版本问题。由于代码中使用了较多的 os.path.jo

MayYou_SSS

1068人浏览 · 2023-12-08 12:17:59

MayYou_SSS · 2023-12-08 12:17:59 发布

几个网站

细粒度领域的资源整合：Awesome Fine-Grained Image Analysis – Papers, Codes and Datasets (weixiushen.com)

深度学习各个领域的排行榜网站：Browse the State-of-the-Art in Machine Learning | Papers With Code

在CUB-200-2011上的排行：

CUB-200-2011 Benchmark (Fine-Grained Image Classification) | Papers With Code

IELT效果差不多，年份也近，所以选择了该网络。

项目情况

代码地址：GitHub - mobulan/IELT: Source code of the paper Fine-Grained Visual Classification via Internal Ensemble Learning Transformer

Article: Fine-Grained Visual Classification via Internal Ensemble Learning Transformer

Published in: IEEE Transactions on Multimedia ( Early Access ）

注：什么是Early Access的论文？ 为了学术成果更快更广泛的传播、交流，有些论文在正式见刊（印刷）之前便已网络见刊，这些论文已经被数据库收录，除了没有分配卷、期、页码外，论文的其他信息（内容、DOI等）均已确定，可以检索、下载、阅读和引用，被称为“Early Access论文”。

-------------------------------------------------------------------------------------------------------------------------------

我的复现

由于代码中使用了较多的 os.path.join ，会有正反斜杠问题，在windows10上训练时出了很多问题，我直接换平台，问题一个没有，代码很好跑。特别是没有版本问题，很好，这可能就是代码年份较新的好处吧。

唯独还没看到测试的代码，没找得到测试方法，没找到可视化方法，未能应用于自己的数据集。

TBC

硬件环境：

数据集： CUB_200_2011

我是在程序中，IELT-main/utils/dataset.py中设置download=True让它自己下载的。（建议）

也能自己下载好放到合适的目录。

在dataset.py中，作者给出的参考网址：

http://www.vision.caltech.edu/visipedia-data/CUB-200-2011/CUB_200_2011.tgz

目录组织形式参考：

clone到本地

git clone https://github.com/mobulan/ielt

或者直接下zip也挺好用，有的时候git clone会出问题，下zip目前没什么问题。

准备虚拟环境

conda create -n IELT python=3.9
conda activate IELT

直接pytorch官网安装，这样最保险、最全面、最好用：Start Locally | PyTorch

pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118

缺啥装啥，由于代码比较新（复现于2023/12/8），我没遇到版本问题。

pip install timm tensorboard ml_collections scipy yacs pandas

下载预训练模型

需要一个谷歌账号，没有就直接买一个吧hh。

网址：https://console.cloud.google.com/storage/browser/_details/vit_models/imagenet21k/ViT-B_16.npz;tab=live_object

参考组织形式

修改配置文件

cub.yaml

参考

根目录setup.py文件

主要是选一下cfg_file,显卡

from settings.defaults import _C
from settings.setup_functions import *

config = _C.clone()
cfg_file = os.path.join('configs', 'cub.yaml')
# 可以修改log名称
# cfg_file = os.path.join('..','configs', 'cub.yaml')
config = SetupConfig(config, cfg_file)
config.defrost()

## Log Name and Perferences
config.write = True			# comment it to disable all the log writing
config.train.checkpoint = True		# comment it to disable saving the checkpoint
config.misc.exp_name = f'{config.data.dataset}'
config.misc.log_name = f'IELT'
config.cuda_visible = '0'
# 可以选择使用的显卡

# Environment Settings
config.data.log_path = os.path.join(config.misc.output, config.misc.exp_name, config.misc.log_name
                                    + time.strftime(' %m-%d_%H-%M', time.localtime()))

config.model.pretrained = os.path.join(config.model.pretrained,
                                       config.model.name + config.model.pre_version + config.model.pre_suffix)
os.environ['CUDA_VISIBLE_DEVICES'] = config.cuda_visible
os.environ['OMP_NUM_THREADS'] = '1'

# Setup Functions
config.nprocess, config.local_rank = SetupDevice()
config.data.data_root, config.data.batch_size = LocateDatasets(config)
config.train.lr = ScaleLr(config)
log = SetupLogs(config, config.local_rank)
if config.write and config.local_rank in [-1, 0]:
	with open(config.data.log_path + '/config.json', "w") as f:
		f.write(config.dump())
config.freeze()
SetSeed(config)

开始训练

上述官方命令有点小问题，修改如下：

python main.py
或者
python -m main

输出

log

================================================================================
 Fine-Grained Visual Classification via Internal Ensemble Learning Transformer
                            Pytorch Implementation
================================================================================
Author:		Xu Qin,		Wang Jiahui,		Jiang Bo,		Luo Bin
Institute:	Anhui University					Date: 2023-02-13
--------------------------------------------------------------------------------
Python Version: 3.9.18         Pytorch Version: 2.1.1+cu118         Cuda Version: 11.8
-------------------------------------------------------------------------------- 

============================     Data Settings      ============================
dataset       cub                       batch_size    4                      
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 
data_root     ./data/                   img_size      448                    
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 
resize        600                       padding       0                      
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 
no_crop       0                         autoaug       0                      
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 
blur          0.0                       color         0.4                    
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 
saturation    0.0                       hue           0.0                    
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 
mixup         0.0                       cutmix        0.0                    
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 
rotate        0.0                       log_path      ./output/cub/IELT 12-07_23-43   
============================    Hyper Parameters    ============================
vote_perhead  24            update_warm   10            loss_alpha    0.4         
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 
total_num     126           fix           1             dsm           1           
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 
cam           1             assess        0             
============================   Training Settings    ============================
start_epoch   0             epochs        50            warmup_epochs 0           
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 
weight_decay  0             clip_grad     None          checkpoint    1           
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 
lr            0.0025        scheduler     cosine        lr_epoch_update0           
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 
optimizer     SGD           freeze_backbone0             eps           1e-08       
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 
betas         0.9,0.999     momentum      0.9           
============================     Other Settings     ============================
amp           1             output        ./output      exp_name      cub         
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 
log_name      IELT          eval_every    1             seed          42          
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 
eval_mode     0             throughput    0             fused_window  1             
============================    Model Structure     ============================
type          ViT           name          ViT-B_16      baseline_model0           
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 
pretrained    pretrained/ViT-B_16.npz  pre_version                 pre_suffix    .npz        
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 
resume                      num_classes   200           drop_path     0.0         
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 
dropout       0.0           label_smooth  0.0           parameters    93.493M       
============================  Training Information  ============================
Train samples 5994                      Test samples  5794                   
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 
Total Batch Size  4                     Load Time     3s                     
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 
Train Steps   74900                     Warm Epochs   0                         
============================     Start Training     ============================
Epoch  1 /50 : Accuracy 66.051    BA 66.051    BE   1    Loss 1.1079    TA 45.91
Epoch  2 /50 : Accuracy 69.814    BA 69.814    BE   2    Loss 0.9985    TA 62.67
Epoch  3 /50 : Accuracy 71.246    BA 71.246    BE   3    Loss 1.0025    TA 71.04
Epoch  4 /50 : Accuracy 72.006    BA 72.006    BE   4    Loss 1.0287    TA 78.81
Epoch  5 /50 : Accuracy 72.523    BA 72.523    BE   5    Loss 1.0503    TA 82.93
Epoch  6 /50 : Accuracy 75.768    BA 75.768    BE   6    Loss 0.9947    TA 87.40
Epoch  7 /50 : Accuracy 74.836    BA 75.768    BE   6    Loss 1.0967    TA 88.85
Epoch  8 /50 : Accuracy 78.961    BA 78.961    BE   8    Loss 0.9542    TA 91.51
Epoch  9 /50 : Accuracy 81.101    BA 81.101    BE   9    Loss 0.8824    TA 92.99
Epoch 10 /50 : Accuracy 80.290    BA 81.101    BE   9    Loss 0.9127    TA 95.11
Epoch 11 /50 : Accuracy 79.703    BA 81.101    BE   9    Loss 0.9738    TA 94.04
Epoch 12 /50 : Accuracy 78.840    BA 81.101    BE   9    Loss 1.0418    TA 95.51
Epoch 13 /50 : Accuracy 82.171    BA 82.171    BE  13    Loss 0.8814    TA 96.76
Epoch 14 /50 : Accuracy 82.137    BA 82.171    BE  13    Loss 0.9301    TA 97.55
Epoch 15 /50 : Accuracy 82.206    BA 82.206    BE  15    Loss 0.9095    TA 97.91
Epoch 16 /50 : Accuracy 82.171    BA 82.206    BE  15    Loss 0.9413    TA 98.87
Epoch 17 /50 : Accuracy 83.345    BA 83.345    BE  17    Loss 0.8929    TA 98.93
Epoch 18 /50 : Accuracy 83.828    BA 83.828    BE  18    Loss 0.8763    TA 99.33
Epoch 19 /50 : Accuracy 84.311    BA 84.311    BE  19    Loss 0.8594    TA 99.73
Epoch 20 /50 : Accuracy 84.001    BA 84.311    BE  19    Loss 0.8794    TA 99.65
Epoch 21 /50 : Accuracy 84.122    BA 84.311    BE  19    Loss 0.8902    TA 99.47
Epoch 22 /50 : Accuracy 85.243    BA 85.243    BE  22    Loss 0.8365    TA 99.93
Epoch 23 /50 : Accuracy 84.984    BA 85.243    BE  22    Loss 0.8427    TA 99.77
Epoch 24 /50 : Accuracy 84.829    BA 85.243    BE  22    Loss 0.8708    TA 99.77
Epoch 25 /50 : Accuracy 85.243    BA 85.243    BE  22    Loss 0.8370    TA 99.77
Epoch 26 /50 : Accuracy 86.055    BA 86.055    BE  26    Loss 0.8122    TA 99.97
Epoch 27 /50 : Accuracy 86.210    BA 86.210    BE  27    Loss 0.8063    TA 99.98
Epoch 28 /50 : Accuracy 85.882    BA 86.210    BE  27    Loss 0.8244    TA 99.95
Epoch 29 /50 : Accuracy 85.658    BA 86.210    BE  27    Loss 0.8194    TA 99.97
Epoch 30 /50 : Accuracy 85.934    BA 86.210    BE  27    Loss 0.8201    TA 99.97
Epoch 31 /50 : Accuracy 85.968    BA 86.210    BE  27    Loss 0.8277    TA 99.93
Epoch 32 /50 : Accuracy 85.986    BA 86.210    BE  27    Loss 0.8212    TA 99.97
Epoch 33 /50 : Accuracy 86.210    BA 86.210    BE  27    Loss 0.8157    TA 99.95
Epoch 34 /50 : Accuracy 86.124    BA 86.210    BE  27    Loss 0.8106    TA 100.00
Epoch 35 /50 : Accuracy 85.986    BA 86.210    BE  27    Loss 0.8110    TA 99.98
Epoch 36 /50 : Accuracy 86.158    BA 86.210    BE  27    Loss 0.8093    TA 100.00
Epoch 37 /50 : Accuracy 86.020    BA 86.210    BE  27    Loss 0.8084    TA 99.98
Epoch 38 /50 : Accuracy 86.141    BA 86.210    BE  27    Loss 0.8073    TA 100.00
Epoch 39 /50 : Accuracy 86.158    BA 86.210    BE  27    Loss 0.8032    TA 99.93
Epoch 40 /50 : Accuracy 86.175    BA 86.210    BE  27    Loss 0.8025    TA 100.00
Epoch 41 /50 : Accuracy 86.210    BA 86.210    BE  27    Loss 0.8016    TA 100.00
Epoch 42 /50 : Accuracy 86.193    BA 86.210    BE  27    Loss 0.8015    TA 100.00
Epoch 43 /50 : Accuracy 86.193    BA 86.210    BE  27    Loss 0.8014    TA 100.00
Epoch 44 /50 : Accuracy 86.175    BA 86.210    BE  27    Loss 0.8024    TA 99.97
Epoch 45 /50 : Accuracy 86.175    BA 86.210    BE  27    Loss 0.8018    TA 99.95
Epoch 46 /50 : Accuracy 86.193    BA 86.210    BE  27    Loss 0.8020    TA 100.00
Epoch 47 /50 : Accuracy 86.227    BA 86.227    BE  47    Loss 0.8017    TA 99.98
Epoch 48 /50 : Accuracy 86.175    BA 86.227    BE  47    Loss 0.8017    TA 100.00
Epoch 49 /50 : Accuracy 86.210    BA 86.227    BE  47    Loss 0.8018    TA 99.98
Epoch 50 /50 : Accuracy 86.175    BA 86.227    BE  47    Loss 0.8017    TA 99.97
============================    Finish Training     ============================
Best Accuracy 86.227                    Best Epoch    47                     
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 
Training Time 126.95 min                Testing Time  44.73 min              
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 
Total Time    171.68 min

TBC

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