【PaddleHub模型贡献】一行代码实现蛇种识别

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【PaddleHub模型贡献】一行代码实现蛇种识别

2025-07-31

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该内容围绕蛇类识别模型展开，先安装PaddleX，解压数据集并划分，设置GPU后进行图像预处理与增强，定义数据迭代器，用ResNet50_vd_ssld模型训练，接着导出并转换模型为PaddleHub模块，测试单张和多张图片识别效果，最后介绍在GitHub提pr的步骤。

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【paddlehub模型贡献】一行代码实现蛇种识别 -

一、模型开发

1.安装必要的资源库

原项目使用PaddleX开发，因此这里先安装PaddleX：

In [5]

!pip install paddlex

2.数据预处理

2.1解压数据集

In [2]

!unzip data/data44587/snake_data.zip -d /home/aistudio/

2.2划分训练集

In [ ]

!paddlex --split_dataset --format ImageNet --dataset_dir '/home/aistudio/snake_data' --val_value 0.2 --test_value 0.1

3.模型训练

3.1设置使用0号GPU卡

In [ ]

import matplotlib
matplotlib.use('Agg') 
import os
os.environ['CUDA_VISIBLE_DEVICES'] = '0'import paddlex as pdx

3.2图像预处理+数据增强

In [ ]

from paddlex.cls import transforms
train_transforms = transforms.Compose([
    transforms.RandomCrop(crop_size=224),
    transforms.RandomHorizontalFlip(),
    transforms.Normalize()
])
eval_transforms = transforms.Compose([
    transforms.ResizeByShort(short_size=256),
    transforms.CenterCrop(crop_size=224),
    transforms.Normalize()
])

3.3数据迭代器的定义

In [ ]

train_dataset = pdx.datasets.ImageNet(
    data_dir='snake_data',
    file_list='snake_data/train_list.txt',
    label_list='snake_data/labels.txt',
    transforms=train_transforms,
    shuffle=True)
eval_dataset = pdx.datasets.ImageNet(
    data_dir='snake_data',
    file_list='snake_data/val_list.txt',
    label_list='snake_data/labels.txt',
    transforms=eval_transforms)

2025-07-19 11:49:17 [INFO]	Starting to read file list from dataset...
2025-07-19 11:49:17 [INFO]	17364 samples in file snake_data/train_list.txt
2025-07-19 11:49:17 [INFO]	Starting to read file list from dataset...
2025-07-19 11:49:17 [INFO]	25 samples in file snake_data/val_list.txt

3.4开始炼丹

In [ ]

num_classes = len(train_dataset.labels)
model = pdx.cls.ResNet50_vd_ssld(num_classes=num_classes)
model.train(num_epochs = 60,
            s*e_interval_epochs = 10,
            train_dataset = train_dataset,
            train_batch_size = 64,
            eval_dataset = eval_dataset,
            learning_rate = 0.025,
            warmup_steps = 1084,
            warmup_start_lr = 0.0001,
            lr_decay_epochs=[20, 40],
            lr_decay_gamma = 0.025,    
            s*e_dir='/home/aistudio',
            use_vdl=True)

4.查看模型预测效果

In [ ]

import cv2import matplotlib.pyplot as plt# 加载模型print('**************************************加载模型*****************************************')
model = pdx.load_model('best_model')# 显示图片img = cv2.imread('test.jpg')
b,g,r = cv2.split(img)
img = cv2.merge([r,g,b])
%matplotlib inline
plt.imshow(img)# 预测result = model.predict('test.jpg', topk=3)print('**************************************预测*****************************************')print(result[0])

**************************************加载模型*****************************************
2025-07-19 14:21:06 [INFO]	Model[ResNet50_vd_ssld] loaded.
**************************************预测*****************************************
{'category_id': 4, 'category': '西部菱斑响尾蛇', 'score': 0.9999999}

<Figure size 432x288 with 1 Axes>

二、封装Module

1.导出inference模型

参数	说明
--model_dir	inference模型所在的文件地址，文件包括：.pdparams、.pdopt、.pdmodel、.json和.yml
--s*e_dir	导出inference模型，文件将包括：__model__、__params__和model.yml

In [ ]

!paddlex --export_inference --model_dir=best_model --s*e_dir=./inference_model/ResNet50_vd_ssld

W0717 23:24:19.157521 13809 device_context.cc:252] Please NOTE: device: 0, CUDA Capability: 70, Driver API Version: 9.2, Runtime API Version: 9.0
W0717 23:24:19.161340 13809 device_context.cc:260] device: 0, cuDNN Version: 7.3.
2025-07-17 23:24:22 [INFO]	Model[ResNet50_vd_ssld] loaded.
2025-07-17 23:24:22 [INFO]	Model for inference deploy s*ed in ./inference_model/ResNet50_vd_ssld.

2.模型转换

PaddleX模型可以快速转换成PaddleHub模型，只需要用下面这一句命令即可：

In [1]

!hub convert --model_dir inference_model/ResNet50_vd_ssld \
              --module_name SnakeIdentification \
              --module_version 1.0.0 \
              --output_dir outputs

转换成功后的模型保存在outputs文件夹下，我们解压一下：

In [3]

!gzip -dfq /home/aistudio/outputs/SnakeIdentification.tar.gz
!tar -xf /home/aistudio/outputs/SnakeIdentification.tar

3.模型安装

安装我们刚刚转换的模型：

In [6]

!hub install SnakeIdentification

/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/setuptools/depends.py:2: DeprecationWarning: the imp module is deprecated in f*our of importlib; see the module's documentation for alternative uses
  import imp
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/__init__.py:107: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working
  from collections import MutableMapping
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/rcsetup.py:20: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working
  from collections import Iterable, Mapping
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/colors.py:53: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working
  from collections import Sized[2025-03-12 10:45:54,828] [    INFO] - Successfully uninstalled SnakeIdentification[2025-03-12 10:45:55,105] [    INFO] - Successfully installed SnakeIdentification-1.0.0

4.模型预测

预测单张图片

In [12]

import cv2import paddlehub as hub

module = hub.Module(name="SnakeIdentification")

images = [cv2.imread('snake_data/class_1/2421.jpg')]# execute predict and print the resultresults = module.predict(images=images)for result in results:    print(result)

[2025-03-12 10:55:05,972] [ WARNING] - The _initialize method in HubModule will soon be deprecated, you can use the __init__() to handle the initialization of the object

[{'category_id': 0, 'category': '水蛇', 'score': 0.9999205}]

预测多张图片

选取5张图片，每张图片对应一个类别：

In [13]

import cv2import paddlehub as hub

module = hub.Module(name="SnakeIdentification")

images = [cv2.imread('snake_data/class_1/2421.jpg'), cv2.imread('snake_data/class_2/113.jpg'), cv2.imread('snake_data/class_3/757.jpg'),
          cv2.imread('snake_data/class_4/1101.jpg'), cv2.imread('snake_data/class_5/2566.jpg')]# execute predict and print the resultresults = module.predict(images=images)for result in results:    print(result)

[2025-03-12 11:00:07,036] [ WARNING] - The _initialize method in HubModule will soon be deprecated, you can use the __init__() to handle the initialization of the object

[{'category_id': 0, 'category': '水蛇', 'score': 0.9999205}]
[{'category_id': 1, 'category': '剑纹带蛇', 'score': 0.9988399}]
[{'category_id': 2, 'category': '德凯斯氏蛇', 'score': 0.9867851}]
[{'category_id': 3, 'category': '黑鼠蛇', 'score': 0.9468411}]
[{'category_id': 4, 'category': '西部菱斑响尾蛇', 'score': 1.0}]