deepfashion数据集的evaluation partitions标注能用来做什么,并提供示例代码
时间: 2024-03-21 18:40:15 浏览: 18
DeepFashion数据集的evaluation partitions标注可以用于训练和评估计算机视觉模型,特别是针对时尚图像的任务,如服装类别分类、属性预测、检测和分割等。以下是一个示例代码,演示如何使用DeepFashion数据集的evaluation partitions标注来训练一个服装类别分类器:
```python
import pandas as pd
import numpy as np
import os
import matplotlib.pyplot as plt
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPooling2D
# Load the evaluation partitions annotation file
anno_df = pd.read_csv('list_eval_partition.csv')
# Define the image directory
img_dir = 'img'
# Define the batch size
batch_size = 32
# Define the image generator with data augmentation
train_datagen = ImageDataGenerator(rescale=1./255,
shear_range=0.2,
zoom_range=0.2,
horizontal_flip=True)
# Generate the training data
train_generator = train_datagen.flow_from_dataframe(dataframe=anno_df[anno_df['evaluation_status']=='train'],
directory=img_dir,
x_col='image_name',
y_col='category_label',
target_size=(224, 224),
batch_size=batch_size,
class_mode='categorical')
# Define the model architecture
model = Sequential()
model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(224, 224, 3)))
model.add(MaxPooling2D((2, 2)))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(MaxPooling2D((2, 2)))
model.add(Conv2D(128, (3, 3), activation='relu'))
model.add(MaxPooling2D((2, 2)))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(10, activation='softmax'))
# Compile the model
model.compile(loss='categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'])
# Train the model
history = model.fit(train_generator,
steps_per_epoch=len(train_generator),
epochs=10)
# Evaluate the model on the test set
test_datagen = ImageDataGenerator(rescale=1./255)
test_generator = test_datagen.flow_from_dataframe(dataframe=anno_df[anno_df['evaluation_status']=='test'],
directory=img_dir,
x_col='image_name',
y_col='category_label',
target_size=(224, 224),
batch_size=batch_size,
class_mode='categorical')
loss, accuracy = model.evaluate(test_generator, steps=len(test_generator))
print('Test accuracy:', accuracy)
```
该代码使用Keras库构建了一个简单的卷积神经网络模型,并使用ImageDataGenerator从DeepFashion数据集中生成训练和测试数据。在这个示例中,我们使用evaluation partitions标注来分割数据集,并仅使用其中的训练数据进行模型训练,然后在测试数据上评估模型性能。