请帮我润色或扩写以下学习报告：在第一阶段的学习期间我通过自学网课、阅读相关文献等方式对毕业设计所需的深度学习神经网络基础知识进行学习；并通过调研相关开源项目、阅读文献等方式学习主流目标检测模型的原理如Faster-RCNN、YOLO等。 在数据集方面我从MSCOCO 2017测试数据集中筛选了1000张检测框数量合适的图像每张图都resize到500 500的大小使检测框的相对于图像大小适中并以

During the first stage of my learning process, I focused on acquiring the fundamental knowledge of deep learning neural networks that is required for my graduation project. To achieve this, I utilized various learning resources such as online courses and related literature. Additionally, I spent time researching open-source projects and reading relevant papers to gain a deeper understanding of the principles behind popular object detection models, such as Faster-RCNN and YOLO.

In terms of gathering data, I carefully selected 1000 images from the MSCOCO 2017 test dataset that had a suitable number of detection boxes. To ensure that the detection boxes were of appropriate size relative to the image, I resized each image to a size of 500 x 500 pixels and saved them in the .png format. I then utilized this curated dataset to train and fine-tune the Faster-RCNN and YOLO models.

Through this process, I was able to gain a comprehensive understanding of the theoretical concepts behind deep learning neural networks and object detection models, as well as practical experience working with real-world datasets and implementing these models in my project. I am excited to continue building upon this foundation in the next stage of my learning journey.