算法003.图像识别

yolov5详解

包括模型训练、模型推理、等内容. 主要目的：

  1. 了解基于xxx 进行深度学习模型训练的系统支持，包括梯度下降xxx等等概念。
  2. 了解模型训练的流程，以及怎么训练出一个好模型。

模型训练

数据收集和准备

选择数据集：freiburg-groceries（计算机视觉领域的数据集，主要用于目标检测和图像分类任务）。

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import tensorflow as tf
from tensorflow.keras import layers, models
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelBinarizer
import os
import numpy as np
from PIL import Image

# Define the path to your dataset
dataset_path = '/kaggle/input/freiburg-groceries/images/OIL/'

# Load and preprocess your dataset
image_paths = [os.path.join(dataset_path, filename) for filename in os.listdir(dataset_path)]
images = []
labels = []

for image_path in image_paths:
    # Load and resize the image to a common size (e.g., 224x224)
    image = Image.open(image_path)
    image = image.resize((224, 224))
    image = np.array(image) / 255.0  # Normalize pixel values to [0, 1]
    images.append(image)
    
    # Extract the class label from the image path (assuming the path structure)
    label = image_path.split(os.path.sep)[-2]  # Assuming the label is the second-to-last directory
    labels.append(label)

# Convert labels to one-hot encoded vectors
label_binarizer = LabelBinarizer()
labels = label_binarizer.fit_transform(labels)

# Split the dataset into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(images, labels, test_size=0.2, random_state=42)

# Create a simple CNN model
model = models.Sequential([
    layers.Conv2D(32, (3, 3), activation='relu', input_shape=(224, 224, 3)),
    layers.MaxPooling2D((2, 2)),
    layers.Conv2D(64, (3, 3), activation='relu'),
    layers.MaxPooling2D((2, 2)),
    layers.Conv2D(64, (3, 3), activation='relu'),
    layers.Flatten(),
    layers.Dense(64, activation='relu'),
    layers.Dense(len(label_binarizer.classes_), activation='softmax')  # Output layer with the number of classes
])

# Compile the model
model.compile(optimizer='adam',
              loss='categorical_crossentropy',
              metrics=['accuracy'])

# Train the model
model.fit(np.array(X_train), np.array(y_train), epochs=10, batch_size=32, validation_split=0.2)

# Evaluate the model on the test data
test_loss, test_acc = model.evaluate(np.array(X_test), np.array(y_test))
print(f'Test accuracy: {test_acc}')