基于CNN+ViT的蔬果图像分类实验

本文只是做一个简单融合的实验，没有任何新颖，大家看看就行了。

1.数据集

本文所采用的数据集为Fruit-360 果蔬图像数据集，该数据集由 Horea Mureșan 等人整理并发布于 GitHub（项目地址：Horea94/Fruit-Images-Dataset），广泛应用于图像分类和目标识别等计算机视觉任务。该数据集共包含141 类水果和蔬菜图像，总计 94,110 张图像，每张图像的尺寸统一为 100×100 像素，且背景已统一处理为白色背景，以减少背景噪声对模型训练的影响。

数据集中涵盖了大量常见和不常见的果蔬品类，主要包括：

1. 苹果（多个品种：如深雪、金苹果、金红、青奶奶、粉红女士、红苹果、红美味等）
2. 香蕉（黄色、红色、淑女手指等）
3. 葡萄（蓝色、粉红色、白色多个品种）
4. 柑橘类（橙子、柠檬、酸橙、葡萄柚、柑橘等）
5. 热带水果（芒果、木瓜、红毛丹、百香果、番石榴、荔枝、菠萝、火龙果等）
6. 浆果类（蓝莓、覆盆子、草莓、黑加仑、红醋栗、桑葚等）
7. 核果类与坚果类（桃子、李子、杏、椰子、榛子、核桃、栗子、山核桃等）
8. 蔬菜类（黄瓜、茄子、胡椒、番茄、洋葱、花椰菜、甜菜根、玉米、土豆等）
9. 其他类如：仙人掌果实、杨布拉、姜根、格兰纳迪拉、Physalis（灯笼果）、油桃、佩皮诺、罗望子、大头菜等。

在数据划分方面，本研究按照如下比例进行数据集划分：

（1）训练集：70,491 张图像
          其中按照 8:2 的比例划分出验证集，得到最终：

                        训练子集：56,432 张

                        验证集：14,059 张

（2）测试集：23,619 张图像

2.模型简述

在图像分类任务中，深度学习方法已经取得了显著的进展，如残差神经网络（ResNet）,Vision Transformer展现了较强的性能。ResNet作为CNN下的网络架构，在局部特征提取方面具有优势，能够有效地捕捉图像中的空间结构信息。而Vision Transformer作为Transformer的变种，在捕捉全局依赖关系和建模长程依赖性方面的具有更好的优势。

由于CNN的卷积操作本质上能够生成具有空间局部关联性的特征图，实际上可以视为一种变相的patch操作。因此，在将CNN与Transformer相结合时，可以避免传统ViT中对输入图像进行切分patch的操作，只需对图像进行位置编码，从而使得Transformer能够有效处理这些具有空间结构的特征图。这种设计不仅减少了计算开销，还使得整个模型在处理图像时更具效率与准确性。

同时，与原始ViT框架中描述的技术不同，原始框架通常会将一个可学习的位置嵌入向量预先添加到编码后的patch序列中，作为图像的位置信息进行表示。然而，为了简化模型的实现并提高计算效率，本文在架构设计上有所调整，省略了额外的位置编码步骤。具体来说，本文的模型通过直接输入编码后的patch序列到Transformer块中，跳过了对每个patch进行独立位置编码的操作。

基于这一思路，结合了残差神经网络（ResNet）和Vision Transformer（ViT）两种网络架构，将它们以串行连接的方式进行融合。具体模型架构图如下图所示

3.实验

模型代码(基于tensorflow2.X)

import glob
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras import layers,models
import warnings
warnings.filterwarnings('ignore')
import osTrain = r"D:\archive (1)\fruits-360_dataset_100x100\fruits-360\Training"
Test = r"D:\archive (1)\fruits-360_dataset_100x100\fruits-360\Test"IMAGE_SIZE = 100
NUM_CLASSES = 141
BATCH_SIZE = 32imagegenerator = ImageDataGenerator(rescale=1.0 / 255.0, validation_split=0.2, rotation_range=10, horizontal_flip=True)# Training and validation data generators
Train_Data = imagegenerator.flow_from_directory(Train,target_size=(IMAGE_SIZE, IMAGE_SIZE),batch_size=BATCH_SIZE,class_mode='categorical',subset='training'
)
Validation_Data = imagegenerator.flow_from_directory(Train,target_size=(IMAGE_SIZE, IMAGE_SIZE),batch_size=BATCH_SIZE,class_mode='categorical',subset='validation'
)# Test data generator (no augmentation)
test_imagegenerator = ImageDataGenerator(rescale=1.0 / 255.0)
Test_Data = test_imagegenerator.flow_from_directory(Test,target_size=(IMAGE_SIZE,IMAGE_SIZE),batch_size=BATCH_SIZE,class_mode='categorical',# subset='test'
)
class ResidualBlock(layers.Layer):def __init__(self, filters, kernel_size=(3, 3), strides=1):super(ResidualBlock, self).__init__()self.conv1 = layers.Conv2D(filters, kernel_size, strides=strides, padding="same", activation='relu')self.conv2 = layers.Conv2D(filters, kernel_size, strides=1, padding='same', activation='relu')self.shortcut = layers.Conv2D(filters, (1, 1), strides=strides, padding='same', activation='relu')self.bn1 = layers.BatchNormalization()self.bn2 = layers.BatchNormalization()self.relu = layers.ReLU()def call(self, inputs):x = self.conv1(inputs)x = self.bn1(x)x = self.relu(x)x = self.conv2(x)x = self.bn2(x)shortcut = self.shortcut(inputs)x = layers.add([x, shortcut])x = self.relu(x)return x# ResNet Model definition
class ResNetModel(layers.Layer):def __init__(self):super(ResNetModel, self).__init__()self.conv1 = layers.Conv2D(32, (5, 5), activation='relu', input_shape=(IMAGE_SIZE, IMAGE_SIZE, 3),padding='same')self.maxpool1 = layers.MaxPooling2D((2, 2))# Residual Blocksself.resblock1 = ResidualBlock(32,strides=1)self.resblock2 = ResidualBlock(64,strides=2)self.resblock3 = ResidualBlock(128,strides=2)self.resblock4 = ResidualBlock(256, strides=2)# self.global_avg_pool = layers.GlobalAveragePooling2D()def call(self, inputs):print(inputs.shape)x = self.conv1(inputs)print(x.shape)x = self.maxpool1(x)print(x.shape)# Apply Residual Blocksx = self.resblock1(x)print(x.shape)x = self.resblock2(x)print(x.shape)# x = self.resblock3(x)# print(x.shape)# x = self.resblock4(x)# x = self.global_avg_pool(x)# print(x.shape)return x
class TransformerEncoder(layers.Layer):def __init__(self, num_heads=8, key_dim=64, ff_dim=256, dropout_rate=0.1):super(TransformerEncoder, self).__init__()self.attention = layers.MultiHeadAttention(num_heads=num_heads, key_dim=key_dim)self.dropout1 = layers.Dropout(dropout_rate)self.norm1 = layers.LayerNormalization()self.ff = layers.Dense(ff_dim, activation='relu')self.ff_output = layers.Dense(key_dim*num_heads)self.dropout2 = layers.Dropout(dropout_rate)self.norm2 = layers.LayerNormalization()def call(self, x):# Multi-head self-attentionattention_output = self.attention(x, x)attention_output = self.dropout1(attention_output)x = self.norm1(attention_output + x)  # Residual connection# Feed Forward Networkff_output = self.ff(x)ff_output = self.ff_output(ff_output)ff_output = self.dropout2(ff_output)x = self.norm2(ff_output + x)  # Residual connectionreturn x# Vision Transformer (ViT) 模型
class VisionTransformer(models.Model):def __init__(self, input_shape=(100, 100, 3), num_classes=141, num_encoders=3, patch_size=8, num_heads=16,key_dim=4, ff_dim=256, dropout_rate=0.2):super(VisionTransformer, self).__init__()self.patch_size = patch_size#Resnetself.resnet=ResNetModel()# Patch Embeddingself.conv = layers.Conv2D(64, (patch_size, patch_size), strides=(patch_size, patch_size), padding='valid')self.reshape = layers.Reshape((-1, 64))self.norm = layers.LayerNormalization()# 位置编码层self.position_encoding = self.add_weight("position_encoding", shape=(1, 625, 64))# Stack multiple Transformer Encoder layersself.encoders = [TransformerEncoder(num_heads=num_heads, key_dim=key_dim, ff_dim=ff_dim, dropout_rate=dropout_rate) for _ inrange(num_encoders)]# Global Average Poolingself.global_avg_pooling = layers.GlobalAveragePooling1D()# Fully connected layerself.fc1 = layers.Dense(256, activation='relu')self.dropout = layers.Dropout(0.2)self.fc2 = layers.Dense(num_classes, activation='softmax')def call(self, inputs):#resnetx = self.resnet(inputs)# print("===========================")# print(x.shape)# Patch Embeddingx = self.reshape(x)# 添加位置编码x = x + self.position_encoding  # 将位置编码加到Patch嵌入向量中# print(x.shape)# x = self.norm(x)# Apply multiple Transformer encodersfor encoder in self.encoders:x = encoder(x)# Global Average Poolingx = self.global_avg_pooling(x)# Fully connected layersx = self.fc1(x)x = self.dropout(x)x = self.fc2(x)return x
# 构建 Vision Transformer 模型vit_model = VisionTransformer(input_shape=(100, 100, 3), num_classes=141, num_encoders=3)
vit_model.build(input_shape=(None, IMAGE_SIZE, IMAGE_SIZE, 3))  # 手动构建模型
# 打印模型摘要
vit_model.summary()
# 编译模型
vit_model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.0001),loss='categorical_crossentropy',metrics=['accuracy']
)
checkpoint_path = "training_checkpoints_1/vit_model_checkpoint_epoch_{epoch:02d}.h5"# 创建ModelCheckpoint回调
checkpoint_callback = tf.keras.callbacks.ModelCheckpoint(checkpoint_path,monitor='val_accuracy',  # 你可以选择监控验证集的损失或准确度save_best_only=True,  # 只保存验证集损失最小的模型save_weights_only=True,  # 只保存权重（而不是整个模型）verbose=1  # 打印日志
)
# 检查是否有保存的模型权重文件
checkpoint_dir = "training_checkpoints_1/"
# 查找所有的 .h5 文件
checkpoint_files = glob.glob(os.path.join(checkpoint_dir, "vit_model_checkpoint_epoch_*.h5"))
# print(latest_checkpoint)
if checkpoint_files:# 使用 os.path.getctime() 获取文件创建时间（或者使用 getmtime() 获取修改时间）latest_checkpoint = max(checkpoint_files, key=os.path.getctime)print(f"Loading model from checkpoint: {latest_checkpoint}")# 加载模型权重vit_model.load_weights(latest_checkpoint)
else:print("No checkpoint found, starting from scratch.")# 训练模型
history = vit_model.fit(Train_Data,epochs=20,validation_data=Validation_Data,shuffle=True,callbacks=[checkpoint_callback]
)# 评估模型
test_loss, test_acc = vit_model.evaluate(Test_Data)
print(f"Test Loss: {test_loss}")
print(f"Test Accuracy: {test_acc}")
# 训练和验证的准确率和损失历史记录
def plot_training_history(history):# 创建子图plt.figure(figsize=(14, 6))# 准备训练准确率和验证准确率的图plt.subplot(1, 2, 1)plt.title('Accuracy History')plt.xlabel('Epochs')plt.ylabel('Accuracy')plt.plot(history.history['accuracy'], label='Training Accuracy', marker='o')plt.plot(history.history['val_accuracy'], label='Validation Accuracy', color='green', marker='o')plt.legend()# 准备训练损失和验证损失的图plt.subplot(1, 2, 2)plt.title('Loss History')plt.xlabel('Epochs')plt.ylabel('Loss')plt.plot(history.history['loss'], label='Training Loss', marker='o')plt.plot(history.history['val_loss'], label='Validation Loss', color='green', marker='o')plt.legend()# 显示图形plt.tight_layout()plt.show()# 绘制训练过程
plot_training_history(history)
for i in range(16):# 获取测试数据的下一个批次img_batch, labels_batch = Test_Data.next()img = img_batch[0]  # 获取当前批次的第一张图像true_label_idx = np.argmax(labels_batch[0])  # 获取真实标签的索引# 获取真实标签的名称true_label = [key for key, value in Train_Data.class_indices.items() if value == true_label_idx]# 扩展维度以匹配模型输入EachImage = np.expand_dims(img, axis=0)# 进行预测prediction = vit_model.predict(EachImage)# 获取预测标签predicted_label = [key for key, value in Train_Data.class_indices.items() ifvalue == np.argmax(prediction, axis=1)[0]]# 获取预测的概率predicted_prob = np.max(prediction, axis=1)[0]# 绘制图像plt.subplot(4, 4, i + 1)plt.imshow(img)plt.title(f"True: {true_label[0]} \nPred: {predicted_label[0]} \nProb: {predicted_prob:.2f}")plt.axis('off')plt.tight_layout()
plt.show()

做了如下参数实验

 分类效果图