简介高效的 CV 入门指南: 100 行实现 ConvNext 图像分类-编程知识

简介高效的 CV 入门指南: 100 行实现 ConvNext 图像分类

概述
ConvNext
架构创新
参数效率
计算效率
100 行代码实现 ConvNext 图像分类
- ConvNext tiny
- ConvNext Base

概述

在当今快速发展的人工智能领域, 计算机视觉 (Computer Vision, CV) 已称为一个关键的研究和应用领域. CV 可以使计算机理解图像和视频内容. CV 的核心目标是模拟和扩展人类的数据额系统功能, 使得机器能从图像或视频中自动提取, 处理, 分析和理解有用信息.

随着深度学习 (Deep Learning) 和神经网络 (Neural Network) 的兴起, 计算机视觉领域已经取得了显著的进步. 这些技术使得计算机能够通过学习大量的图像数据, 来识别和分类对象, 场景和活动. 应用敢为广泛, 从简单的图像分类到复杂的场景理解, 计算机视觉正逐渐成为日常生活和工业应用中不可或缺的一部分.

CV 技术已广泛应用于医疗成像, 自动驾驶汽车, 监控系统, 人脸识别, VR 等领域. 举个栗子: 在医疗领域, CV 能够帮我们诊断疾病, 通过分析医学图像来辅助医生做出更准确的诊断.

ConvNext

ConvNext (Convolutional Network Net Generation), 即下一代卷积神经网络, 是近些年来 CV 领域的一个重要发展. ConvNext 由 Facebook AI Research 提出, 仅仅通过卷积结构就达到了与 Transformer 结构相媲美的 ImageNet Top-1 准确率, 这在近年来以 Transformer 为主导的视觉问题解决趋势中显得尤为突出.

ConvNext

架构创新

ConvNext 网络的设计理念是在保持卷积神经网络 (Convolutional Neural Network, CNN) 核心优势的同时, 引入创新以解决传统 CNN 在处理复杂图像任务时遇到的限制. 这种新架构通过对传统卷积层的优化和改进, 显著提高了性能.

在传统的 CNN 中, 卷积层 (Convolution Layer) 通过滑动窗口对输入图像进行特征提取, 这一过程虽然有效, 但在处理大规模或复杂数据时往往会遇到性能瓶颈. ConvNext 通过引入改进的卷积策略, 如深度可分离卷积 (Depthwise Separable Convolution), 优化了这一过程. 深度可分离卷积将标准句那几分解为两个部分: 深度卷积核逐点卷积, 这样不仅减少了模型的参数数量, 还提高了计算效率.

深度可分离卷积

此外, ConvNext 还采用了更大的卷积核 (Kernel) 和调整后的步长, 以覆盖更广泛的输入特征并减少信息丢失. 这种设计使得 ConvNext 能够更有效地捕捉图像中的细节和上下文信息, 从而提高了模型在复杂视觉任务上的表现.

参数效率

参数效率是衡量神经网络设计优劣的重要指标之一. ConvNext 通过一系列创新设计显著提高了参数效率. 首先, 通过采用深度可分离卷积. ConvNext 大幅减少了模型参数的数量. 在传统卷积中, 参数数量随着输入通道和输出通道输的乘积线性增长. 而深度可分离卷积通过分离这两个过程, 显著降低了参数数量.

其次, ConvNext 在设计时还考虑到了参数的重用性. 通过精心设计的残差连接 (Residual Connect) 和注意力机制 (Attention Mechanism), 使得模型能够在不增加额外参数的情况下, 复用已有的特征表示. 这种设计不仅提高了模型的学习能力, 还进一步优化了参数的使用效率.

计算效率

在提高计算效率方面, ConvNext 采取了多项措施以优化网络结构, 减少计算资源的需求. 首先, 通过使用深度可分离卷积, ConvNext 大幅减低了每层所需的乘法-加法操作 (MACs). 这直接减少了模型的计算负担.

其次, ConvNext 在网络设计中引入了层标准化 (Layer Normalization) 和 GELU(Gaussian Error Linear Unit) 激活函数. 这些设计优化了网络的前向传播过程, 减少了计算过程中的冗余操作. 蹭标准化有助于稳定训练过程, 减少训练时所需的计算资源, 而 GELU 激活函数则提供了飞线性激活则提供了非线性激活的同时保持了较高的计算效率.

GELU 激活函数

最后, ConvNext 通过精简网络结构和优化数据流动路径, 减少了内存访问次数和数据传输量, 以提高模型运行效率. 这使得 ConvNext 在资源受限的设备上也能有效运行.

100 行代码实现 ConvNext 图像分类

ConvNext tiny

import logging
import tensorflow as tf
from load_image import ImageDataGenerator3# 定义超参数
EPOCHS = 20  # 迭代次数
BATCH_SIZE = 16  # 一次训练的样本数目
learning_rate = 5e-5
optimizer = tf.keras.optimizers.Adam(learning_rate=learning_rate)  # 优化器
loss = tf.losses.BinaryCrossentropy()  # 损失
logging.basicConfig(filename='../model/convnext_tiny/training_log.log', level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')class TrainingLoggingCallback(tf.keras.callbacks.Callback):def on_epoch_end(self, epoch, logs=None):if logs is not None:logging.info(f"Epoch {epoch + 1}/{EPOCHS}")logging.info(f"loss: {logs['loss']} - accuracy: {logs['accuracy']}")logging.info(f"val_loss: {logs['val_loss']} - val_accuracy: {logs['val_accuracy']}")logging.info(f"lr: {self.model.optimizer.lr.numpy()}")class convnext_tiny(tf.keras.Model):def __init__(self):super(convnext_tiny, self).__init__()self.base_model = tf.keras.applications.convnext.ConvNeXtTiny(input_shape=(512, 512, 3), include_top=False, weights="imagenet")self.average_pooling_layer = tf.keras.layers.GlobalAveragePooling2D()self.output_layer = tf.keras.layers.Dense(1, activation="sigmoid")def call(self, inputs):x = self.base_model(inputs)x = self.average_pooling_layer(x)output = self.output_layer(x)return outputdef lr_schedule(epoch, lr):if epoch < 1:return lrelse:return lr * 0.95def main():# 获取数据image_generator = ImageDataGenerator3('../final_dataset-5_turns_chusai/train-metadata.json', batch_size=BATCH_SIZE)# 分割数据集，假设image_generator可以处理分割train_generator, val_generator = image_generator.split_data(test_size=0.2)# 建立模型inception = convnext_tiny()# 调试输出summaryinception.build(input_shape=[None, 512, 512, 3])print(inception.summary())# 配置模型inception.compile(optimizer=optimizer, loss=loss, metrics=["accuracy"])# 保存checkpoint = tf.keras.callbacks.ModelCheckpoint("../model/convnext_tiny/convnext_tiny.tf", monitor='val_loss',verbose=1, save_best_only=True, mode='min')# 学习率调度lr_scheduler = tf.keras.callbacks.LearningRateScheduler(lr_schedule)early_stopping = tf.keras.callbacks.EarlyStopping(monitor='val_loss',  # Monitor the validation losspatience=2,  # Number of epochs with no improvement after which training will be stoppedverbose=1,restore_best_weights=True# Whether to restore model weights from the epoch with the best value of the monitored quantity)# 训练inception.fit(train_generator, validation_data=val_generator, epochs=EPOCHS,callbacks=[checkpoint, lr_scheduler, TrainingLoggingCallback(), early_stopping])if __name__ == '__main__':main()

输出结果:

Model: "convnext_tiny"
_________________________________________________________________Layer (type)                Output Shape              Param #   
=================================================================convnext_tiny (Functional)  (None, 16, 16, 768)       27820128  global_average_pooling2d (  multiple                  0         GlobalAveragePooling2D)                                         dense (Dense)               multiple                  769       =================================================================
Total params: 27820897 (106.13 MB)
Trainable params: 27820897 (106.13 MB)
Non-trainable params: 0 (0.00 Byte)
_________________________________________________________________
None

ConvNext Base

import logging
import numpy as np
import tensorflow as tf
from load_image import ImageDataGenerator3# 定义超参数
EPOCHS = 20  # 迭代次数
BATCH_SIZE = 8  # 一次训练的样本数目
learning_rate = 5e-6
optimizer = tf.keras.optimizers.Adam(learning_rate=learning_rate)  # 优化器
loss = tf.losses.BinaryCrossentropy()  # 损失
logging.basicConfig(filename='../model/convnext_base/training_log.log', level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')class TrainingLoggingCallback(tf.keras.callbacks.Callback):def on_epoch_end(self, epoch, logs=None):if logs is not None:logging.info(f"Epoch {epoch + 1}/{EPOCHS}")logging.info(f"loss: {logs['loss']} - accuracy: {logs['accuracy']}")logging.info(f"val_loss: {logs['val_loss']} - val_accuracy: {logs['val_accuracy']}")logging.info(f"lr: {self.model.optimizer.lr.numpy()}")class convnext_base(tf.keras.Model):def __init__(self):super(convnext_base, self).__init__()self.base_model = tf.keras.applications.convnext.ConvNeXtBase(input_shape=(512, 512, 3), include_top=False, weights="imagenet")self.average_pooling_layer = tf.keras.layers.GlobalAveragePooling2D()self.output_layer = tf.keras.layers.Dense(1, activation="sigmoid")def call(self, inputs):x = self.base_model(inputs)x = self.average_pooling_layer(x)output = self.output_layer(x)return outputdef lr_schedule(epoch, lr):if epoch < 1:return lrelse:return lr * 0.95def main():# 获取数据image_generator = ImageDataGenerator3('../final_dataset-5_turns_chusai/train-metadata.json', batch_size=BATCH_SIZE)# 分割数据集，假设image_generator可以处理分割train_generator, val_generator = image_generator.split_data(test_size=0.125)# 建立模型inception = convnext_base()# 调试输出summaryinception.build(input_shape=[None, 512, 512, 3])print(inception.summary())# 配置模型inception.compile(optimizer=optimizer, loss=loss, metrics=["accuracy"])# 保存checkpoint = tf.keras.callbacks.ModelCheckpoint("../model/convnext_base/convnext_base.tf", monitor='val_loss',verbose=1, save_best_only=True, mode='min')# 学习率调度lr_scheduler = tf.keras.callbacks.LearningRateScheduler(lr_schedule)early_stopping = tf.keras.callbacks.EarlyStopping(monitor='val_loss',  # Monitor the validation losspatience=2,  # Number of epochs with no improvement after which training will be stoppedverbose=1,restore_best_weights=True# Whether to restore model weights from the epoch with the best value of the monitored quantity)# 训练inception.fit(train_generator, validation_data=val_generator, epochs=EPOCHS,callbacks=[checkpoint, lr_scheduler, TrainingLoggingCallback(), early_stopping])if __name__ == '__main__':main()

输出结果:

Model: "convnext_base"
_________________________________________________________________Layer (type)                Output Shape              Param #   
=================================================================convnext_base (Functional)  (None, 16, 16, 1024)      87566464  global_average_pooling2d (  multiple                  0         GlobalAveragePooling2D)                                         dense (Dense)               multiple                  1025      =================================================================
Total params: 87567489 (334.04 MB)
Trainable params: 87567489 (334.04 MB)
Non-trainable params: 0 (0.00 Byte)
_________________________________________________________________
None