基于迁移学习的天气图像识别

为提高天气图像识别的准确率，达到良好的天气图像分类效果，提出一种基于迁移学习的天气图像识别算法。该算法使用Xception图像分类算法实现网络架构，再基于迁移学习理论将模型和参数应用到天气图像识别中，并在同一数据集上与其他模型进行性能对比。实验结果表明，基于迁移学习的改进Xception模型有效解决了训练样本不足、准确率低的问题，在提高天气图像识别方面取得了较好的效果，实现了对阴天、雾天、雨天、沙尘天、雪天、晴天6类天气的识别，总识别准确率达到94.39%。

Weather Image Recognition Based on Transfer Learning

To improve the recognition accuracy of weather images and achieve a good weather image classification result, a weather image recognition algorithm based on transfer learning is studied. The network architecture is implemented by using the Xception image classification algorithm. And compared the performance of other models on the same data set, the experimental results show that the improved Xception model based on transfer learning effectively solves the problem of insufficient training samples and low accuracy and has achieved good results in improving weather image recognition, and this model can achieve the classification of the six weather conditions of cloudy days, foggy days, rain days, dust days,snow days, sunny days, and the total recognition accuracy rate can reache 94.39%.