Week 2

  Week 2: （2025/2/9）

This week, through division of labor and collaboration, we have completed the preliminary coding of the YOLO v11 model, and the model can more accurately identify and segment objects in images.

Aim for week 2:

Debugging, configuration and implementation of YOLO face detection model; Develop and optimize the face recognition model, and gradually improve the recognition performance of the model; Introduce automated scripts

Implementation of tasks:

Siqi Jia, Jinrun Tan: 1. Introduce automated scripts to handle the time-consuming processing of documents in multiple language versions to ensure consistency and accuracy of the documents. 2. Test model export, confirm callback information, and export functionality is normal. 3. Detect image segmentation effects and use segmentation models to train COCO segmentation datasets

FulinYang : 1. The method of early stopping and learning rate self modification was used to reduce the overfitting of the YOLO V11 model. 2. The official dataset of Ultralytics was used for iterative training, and the final model had 25 iterations 

Ziyu Huang, Yicheng Sheng: 1. Hard Negative Mining: Identified and retrained on 38 hard negative samples, allowing the model to handle challenging misclassifications more effectively. 2.Feature Extraction with InceptionResNetV2: Enhanced feature representation by using a more sophisticated deep learning architecture. 3.Optimized Data Augmentation: Improved training data diversity with controlled transformations. 4.Hyperparameter Fine-Tuning: Adjusted SVM parameters to maximize model generalization. 5. Integrate Hard Negative Mining into our pipeline, improve the model’s recall, accuracy, and overall robustness

Peng Wang: The preliminary coding of the YOLO v11 model has been completed and tested in the local environment. The README package now includes a quick start guide and installation steps. A documentation site has been set up in MkDocs, and complete project documentation can now be generated and viewed. 3. All major functional unit tests have been passed, and integration testing has revealed performance bottlenecks in high concurrency situations, which have been optimized. 4. The model has been successfully deployed to the cloud platform, containerized using Docker, and a daily maintenance and monitoring plan has been developed to ensure model performance and stability.

Issues and solutions:

Issue 1：

underperforming classes:

Solution：

integrating Hard Negative Mining into  pipeline

 

Issue 2：

In the case of multiple language versions, manually building and updating documents for document projects can be time-consuming 

Solution：

Construct automated script construction in MkDocs project