# Succulent Plant Image Classification Based on Lightweight GoogLeNet with CBAM Attention Mechanism
## 3 Results
### Model Predictive Analytics  
The purpose of this experiment was to evaluate the performance of the improved GoogLeNetCBAM model in the succulent plant image classification task, and to explore the effect of mixed-precision training on the model performance improvement The dataset consists of 691 images of succulent plants, covering 10 categories. It was divided into a training set and a test set in a ratio of 9:1, where the training set contained 626 images and the test set contained 65 images. The experiment evaluated the classification accuracy and confidence of the model by visualizing and analyzing the test set. In the experimental setup, the hardware environment was first checked for CUDA support to ensure that the model could run efficiently on the GPU. Image preprocessing was performed using a uniform process, including resizing the images to 224 x 224 pixels and applying normalization (0.5 mean and 0.5 standard deviation for the three RGB channels) to ensure format consistency in the input data. For modeling, we loaded the pre-trained GoogLeNetCBAM model. The model is based on the classic GoogLeNet architecture with the addition of the Convolutional Block Attention Module (CBAM), which improves the feature representation of the model by enhancing attention to important feature regions. The experiments turned off the auxiliary classifiers and focused on the prediction results of the main branch loaded with pre-trained weights. The output of the model was processed by Softmax to generate the probability distribution of each category, and the category with the highest probability was finally selected as the prediction result. The experiment visually displayed the prediction results of the model and output the probability distribution of each category at the terminal, to analyze the classification performance of each model (as shown in Table 4). The values in Table 4 represent the Softmax output probabilities of each model when classifying succulents, which were used to measure the confidence of each model in each category. During the experiment, a random succulent image was selected from the validation set as a sample, and it was input into the AlexNet, VGG16, GoogLeNet, and lightweight GoogLeNet models for prediction. The output of each model was a vector of 10 values, each of which corresponded to the predicted probability of a category. A value of 1 indicates that the model correctly identified the category with the highest confidence; non-zero values for the incorrect category reflect that the model was confused by some similar category during prediction. In this way,the recognition accuracy of each model in the succulent plant classification task and its ability to distinguish similar categories can be analyzed. The experimental results further show that the lightweight GoogLeNet model exhibits stronger discriminative ability in many cases, with higher probability values for the correct category and closer to zero probability values for other categories, which verifies its advantages in improving accuracy and anti-confusion. The design of this experiment effectively evaluates the performance of the lightweight GoogLeNet model in multi-class image classification tasks, providing a reference for further optimizing and improving the model's performance. By comparing the performance of four models, AlexNet, VGG16, GoogLeNet, and lightweight GoogLeNet, in the succulent classification task, this experiment aims to highlight the advantages of lightweight GoogLeNet, especially in terms of classification accuracy and exclusion of incorrect categories. Lightweight GoogLeNet demonstrated excellent classification performance while maintaining a low computational overhead (as shown in Figure 8). First, in the Graptoveria 'Opalina' category, the prediction probability of all the models was 1.0, which indicates that all the models successfully recognized the images in this category. However, of more comparative significance is the model performance in the other categories.