【PaddleHub模型贡献】一行代码实现蛇种识别

!pip install paddlex

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

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

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

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()
])

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)

num_classes = len(train_dataset.labels)
model = pdx.cls.ResNet50_vd_ssld(num_classes=num_classes)
model.train(num_epochs = 60,
            save_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,    
            save_dir='/home/aistudio',
            use_vdl=True)

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])

!paddlex --export_inference --model_dir=best_model --save_dir=./inference_model/ResNet50_vd_ssld

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

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

!hub install SnakeIdentification

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)

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)