# CNN-ML-Classify-PyTorch **Repository Path**: mpj1941804034/CNN-ML-Classify-PyTorch ## Basic Information - **Project Name**: CNN-ML-Classify-PyTorch - **Description**: 使用PyTorch预训练好的RNN模型来提取预分类特征，进而使用预分类特征在机器学习上进行训练和分类。 - **Primary Language**: Python - **License**: Apache-2.0 - **Default Branch**: main - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 3 - **Forks**: 0 - **Created**: 2022-02-20 - **Last Updated**: 2024-02-04 ## Categories & Tags **Categories**: Uncategorized **Tags**: PyTorch, RNN, MachineLearning, classify, Python ## README # CNN-ML-Classify-PyTorch 使用PyTorch预训练好的RNN模型来提取预分类特征，进而使用预分类特征在机器学习上进行训练和分类。 ## 项目来源 #### 项目借鉴于github上的([地址](https://github.com/snatch59/cnn-svm-classifier)) 1. 此示例使用来自 Caltech 图像的 48 个标记图像的子集设置(http://www.vision.caltech.edu/Image_Datasets/Caltech101/)，每个标签限制在 40 到 80 张图像之间。图像被CNN模型进行特征提取。 2. 使用 2048-d 特征进行降维 `t-distributed stochastic neighbor embedding` (t-SNE) t-分布随机邻域嵌入来转换它们成易于可视化的二维特征。请注意，使用 **t-SNE** 作为一个提供信息的步骤。如果相同的颜色/标签点大多是聚集在一起，我们很有可能可以使用这些功能训练一个高精度的分类器。 3. 将 2048-d 标记的特征呈现给许多分类器。 最初，该项目是训练支持向量机 分类图像，但是为了比较，这已扩展到下列的： * Support Vector Machine (SVM, LinearSVC) * Extra Trees (ET) * Random Forest (RF) * K-Nearest Neighbor (KNN) * Multi-Layer Perceptron (ML) * Gaussian Naive Bayes (GNB) * Linear Discriminant Analysis (LDA) * Quadratic Discriminant Analysis (QDA) ## 安装教程 需要安装 PyTorch GPU 和 sklearn，最好使用GPU。 电脑显卡型号：NVIDIA GeForce GTX1650，整个`train.py`文件运行时间233.41s，整个`test.py`文件运行时间61.64s。 ## 使用说明 1. 下载完代码后，首先直接在此目录下解压`caltech_101_images.zip`，然后将图像放入`./caltech_101_images/train`文件夹中，同时和train同一级目录创建test目录。 * 最终文件结构： * | * |-- caltech_101_images * | |-- train **解压放置的目录** * | |-- test 2. 可以直接使用`datasetSegmentation.py`脚本进行数据分割，脚本默认是30%测试数据，脚本会将分割好的测试数据集从`train`目录搬运到`test`目录中。 3. 之后可以使用`train.py`脚本进行训练，脚本会将训练好的模型放到`model`目录中，同时在训练中也会进行test的测试，这个测试是和`test.py` 这个脚本测试结果相同，`test.py`这个脚本测试是需要`train.py`训练完的模型。 4. 可以使用`c-svc_classify_features.py`脚本进行SVC的网格搜索，寻找较好的参数。 ## 训练和测试的结果如下： #### 训练集图片数量： 2071；测试集图片数量：860. ### 每个CNN模型运行`train.py`的时间和最好的正确率 | 模型 | time | best acc | 特征维度 | |-----------------|---------|----------|------| | Inception V3 | 194.68s | 94.4% | 2048 | | Resnet18 | 73.47s | 91.9% | 512 | | Resnet34 | 84.78s | 94.0% | 512 | | Resnet50 | 143.16s | 93.3% | 2048 | | ResNext50_32x4d | 155.48s | 94.9% | 2048 | | DenseNet121 | 121.56s | 96.9% | 1024 | | MNASNet0_5 | 104.16s | 91.7% | 1280 | | MNASNet1_0 | 94.50s | 93.0% | 1280 | ### Inception V3 模型 | 模型 | 时间 | 正确率 | |---------------------------------|--------|-------| | LinearSVC | 5.00s | 94.4% | | SVC | 12.72s | 93.5% | | Extra Tree | 0.70s | 88.5% | | Random Forest | 7.73s | 85.1% | | K-Nearest Neighbours | 2.68s | 87.1% | | Multi-layer Perceptron | 10.43s | 94.1% | | Gaussian Naive Bayes | 1.10s | 87.7% | | Linear Discriminant Analysis | 4.38s | 31.9% | | Quadratic Discriminant Analysis | 1.06s | 3.7% | ### Resnet18 模型 | 模型 | 时间 | 正确率 | |---------------------------------|-------|-------| | LinearSVC | 2.33s | 91.4% | | SVC | 2.90s | 91.5% | | Extra Tree | 0.48s | 85.8% | | Random Forest | 4.26s | 82.2% | | K-Nearest Neighbours | 0.71s | 80.9% | | Multi-layer Perceptron | 3.75s | 91.9% | | Gaussian Naive Bayes | 0.35s | 87.8% | | Linear Discriminant Analysis | 0.40s | 90.7% | | Quadratic Discriminant Analysis | 0.51s | 3.7% | ### Resnet34 模型 | 模型 | 时间 | 正确率 | |---------------------------------|-------|-------| | LinearSVC | 1.94s | 93.0% | | SVC | 2.78s | 93.5% | | Extra Tree | 0.43s | 90.1% | | Random Forest | 4.27s | 86.6% | | K-Nearest Neighbours | 0.68s | 87.9% | | Multi-layer Perceptron | 3.01s | 93.7% | | Gaussian Naive Bayes | 0.27s | 90.3% | | Linear Discriminant Analysis | 0.39s | 94.0% | | Quadratic Discriminant Analysis | 0.46s | 3.8% | ## Resnet50 模型 | 模型 | 时间 | 正确率 | |---------------------------------|--------|-------| | LinearSVC | 8.35s | 92.2% | | SVC | 10.15s | 93.0% | | Extra Tree | 0.78s | 88.8% | | Random Forest | 8.28s | 87.2% | | K-Nearest Neighbours | 2.70s | 84.2% | | Multi-layer Perceptron | 16.04s | 93.3% | | Gaussian Naive Bayes | 1.07s | 89.4% | | Linear Discriminant Analysis | 4.46s | 31.9% | | Quadratic Discriminant Analysis | 1.04s | 2.2% | ## ResNext50_32x4d 模型 | 模型 | 时间 | 正确率 | |---------------------------------|--------|-------| | LinearSVC | 6.37s | 94.5% | | SVC | 10.95s | 93.7% | | Extra Tree | 0.79s | 88.8% | | Random Forest | 8.38s | 87.0% | | K-Nearest Neighbours | 2.64s | 87.4% | | Multi-layer Perceptron | 14.96s | 94.9% | | Gaussian Naive Bayes | 1.07s | 91.0% | | Linear Discriminant Analysis | 4.45s | 29.7% | | Quadratic Discriminant Analysis | 1.04s | 3.0% | ## DenseNet121 模型 | 模型 | 时间 | 正确率 | |---------------------------------|-------|-------| | LinearSVC | 2.71s | 96.3% | | SVC | 5.72s | 96.5% | | Extra Tree | 0.55s | 92.7% | | Random Forest | 5.50s | 91.2% | | K-Nearest Neighbours | 1.33s | 92.7% | | Multi-layer Perceptron | 3.78s | 96.3% | | Gaussian Naive Bayes | 0.53s | 93.3% | | Linear Discriminant Analysis | 1.11s | 96.9% | | Quadratic Discriminant Analysis | 0.74s | 1.7% | ## MNASNet0_5 模型 | 模型 | 时间 | 正确率 | |---------------------------------|--------|-------| | LinearSVC | 1.41s | 91.7% | | SVC | 12.20s | 56.3% | | Extra Tree | 0.50s | 81.3% | | Random Forest | 3.72s | 77.0% | | K-Nearest Neighbours | 1.76s | 76.5% | | Multi-layer Perceptron | 10.10s | 91.5% | | Gaussian Naive Bayes | 0.72s | 51.0% | | Linear Discriminant Analysis | 2.25s | 85.6% | | Quadratic Discriminant Analysis | 0.82s | 2.1% | ## MNASNet1_0 模型 | 模型 | 时间 | 正确率 | |---------------------------------|-------|--------| | LinearSVC | 2.68s | 93.0% | | SVC | 6.96s | 91.9% | | Extra Tree | 0.75s | 86.4% | | Random Forest | 6.13s | 83.7% | | K-Nearest Neighbours | 1.66s | 84.1% | | Multi-layer Perceptron | 5.26s | 93.0% | | Gaussian Naive Bayes | 0.67s | 81.5% | | Linear Discriminant Analysis | 2.06s | 88.5% | | Quadratic Discriminant Analysis | 0.77s | 92.62% | ## 下面是MNASNet1_0 模型的预测结果: ## t-SNE train features ![t-SNE 训练的特征](./assets/tsne_train_features.png) ## t-SNE test features ![t-SNE 测试的特征](./assets/tsne_test_features.png) ## Linear SVC ![Linear SVC](./assets/LinearSVC%20Confusion%20matrix.png) ## SVC ![SVC](./assets/Linear Discriminant Analysis Confusion matrix.png) ## Extra Tree ![Extra Tree](./assets/Extra%20Trees%20Confusion%20matrix.png) ## Random Forest ![Random Tree](./assets/Random%20Forest%20Confusion%20matrix.png) ## K-Nearest Neighbours ![k-Nearest Neighbours](./assets/K-Nearest%20Neighbor%20Confusion%20matrix.png) ## Multi-layer Perceptron ![Multi-layer Perceptron](./assets/Multi-layer%20Perceptron%20Confusion%20matrix.png) ## Gaussian Naive Bayes ![Gaussian Naive Bayes](./assets/Gaussian%20Naive%20Bayes%20Confusion%20matrix.png) ## Linear Discriminant Analysis ![Linear Discriminant Analysis](./assets/Linear%20Discriminant%20Analysis%20Confusion%20matrix.png) ## Quadratic Discriminant Analysis ![Quadratic Discriminant Analysis](./assets/Quadratic%20Discriminant%20Analysis%20Confusion%20matrix.png)