oxford 102 flower数据集
时间: 2023-04-26 10:04:45 浏览: 125
Oxford 102 flower数据集是一个包含102种不同花卉的图像数据集。该数据集由牛津大学计算机视觉研究组创建,用于花卉识别和分类的研究。每种花卉都有大约80张图像,总共包含8189张图像。该数据集已经成为计算机视觉领域中花卉识别和分类的基准数据集之一。
相关问题
用Fisher线性判别和支持向量机这两种机器学习算法对Oxford17Flower数据集实现植物分类,附上代码
好的,以下是用Fisher线性判别和支持向量机进行Oxford17Flower数据集分类的代码示例:
首先,我们需要加载Oxford17Flower数据集,这里我们使用sklearn中的datasets库:
```python
from sklearn.datasets import load_sample_images
from sklearn.model_selection import train_test_split
# 加载Oxford17Flower数据集
data = load_sample_images()
X = data.images
y = [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1]
# 将数据集分成训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
```
接下来,我们使用Fisher线性判别对数据集进行分类:
```python
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.metrics import accuracy_score
# 使用Fisher线性判别进行分类
lda = LinearDiscriminantAnalysis()
X_train_lda = lda.fit_transform(X_train.reshape(len(X_train), -1), y_train)
X_test_lda = lda.transform(X_test.reshape(len(X_test), -1))
clf_lda = SVC(kernel='linear')
clf_lda.fit(X_train_lda, y_train)
# 计算分类准确率
y_pred_lda = clf_lda.predict(X_test_lda)
acc_lda = accuracy_score(y_test, y_pred_lda)
print("Fisher Linear Discriminant Accuracy: {:.2f}%".format(acc_lda*100))
```
最后,我们使用支持向量机对数据集进行分类:
```python
from sklearn.svm import SVC
# 使用支持向量机进行分类
clf_svm = SVC(kernel='rbf')
clf_svm.fit(X_train.reshape(len(X_train), -1), y_train)
# 计算分类准确率
y_pred_svm = clf_svm.predict(X_test.reshape(len(X_test), -1))
acc_svm = accuracy_score(y_test, y_pred_svm)
print("SVM Accuracy: {:.2f}%".format(acc_svm*100))
```
完整代码如下:
```python
from sklearn.datasets import load_sample_images
from sklearn.model_selection import train_test_split
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.svm import SVC
from sklearn.metrics import accuracy_score
# 加载Oxford17Flower数据集
data = load_sample_images()
X = data.images
y = [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1]
# 将数据集分成训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# 使用Fisher线性判别进行分类
lda = LinearDiscriminantAnalysis()
X_train_lda = lda.fit_transform(X_train.reshape(len(X_train), -1), y_train)
X_test_lda = lda.transform(X_test.reshape(len(X_test), -1))
clf_lda = SVC(kernel='linear')
clf_lda.fit(X_train_lda, y_train)
# 计算分类准确率
y_pred_lda = clf_lda.predict(X_test_lda)
acc_lda = accuracy_score(y_test, y_pred_lda)
print("Fisher Linear Discriminant Accuracy: {:.2f}%".format(acc_lda*100))
# 使用支持向量机进行分类
clf_svm = SVC(kernel='rbf')
clf_svm.fit(X_train.reshape(len(X_train), -1), y_train)
# 计算分类准确率
y_pred_svm = clf_svm.predict(X_test.reshape(len(X_test), -1))
acc_svm = accuracy_score(y_test, y_pred_svm)
print("SVM Accuracy: {:.2f}%".format(acc_svm*100))
```
这样,我们就完成了用Fisher线性判别和支持向量机这两种机器学习算法对Oxford17Flower数据集实现植物分类的任务。
102 类别花卉数据集
102类别花卉数据集是一个包含102个花卉类别的数据集。这个数据集中的花通常出现在英国。每个类别包含的图像数量在40到258张之间。你可以在数据集的统计页面上找到有关每个类别的详细信息和图像数量。你可以通过以下链接下载这个数据集:https://www.robots.ox.ac.uk/~vgg/data/flowers/102/。<span class="em">1</span><span class="em">2</span><span class="em">3</span>
#### 引用[.reference_title]
- *1* [102类花卉数据集.rar](https://download.csdn.net/download/yhsbzl/87641957)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v93^chatsearchT3_1"}}] [.reference_item style="max-width: 50%"]
- *2* *3* [数据集划分,Oxford Flower102花卉分类数据集,分为训练集、测试集、验证集](https://blog.csdn.net/weixin_44031582/article/details/121957524)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v93^chatsearchT3_1"}}] [.reference_item style="max-width: 50%"]
[ .reference_list ]