Basic Information

• Instructor: Jie Wang

• Email: jiewangx@ustc.edu.cn

• Time and Location: Wed., 14:00 PM - 15:35 PM (GT-B112). Fri., 14:00 PM - 15:35 PM (GT-B112)

• TAs:

  • Xilin Xia (xilinxia@miralab.ai)
  • Yuhang Jiang (yuhangjiang@miralab.ai)
  • Longdi Pan (longdipan@miralab.ai)
  • Boxuan Niu (boxuanniu@miralab.ai)
  • Rui Liu (ruiliu@miralab.ai)
  • Yuyang Cai (yuyangcai@miralab.ai)
  • Chi Ma (chima@miralab.ai)

Lectures

All course materials will be shared via this page.

Project

1. Description

Glass quality inspection is a common task in industrial production. In many factories, this process still relies heavily on manual inspection, which is both time-consuming and labor-intensive. With the rapid development of AI, training a model to automatically detect glass quality has become an effective way to reduce cost and improve efficiency. In this project, your task is to implement a model for glass defect detection.

2. Dataset

You can download the dataset from here. The dataset is organized as follows:

dataset/  
    |- train/  
    |	| - img/  
    |	| - txt/  
    |
    |- test/  
    |	| - img/  
    |	| - txt/  

• The dataset has already been split into training and test sets with an 8:2 ratio.
• The img folders contain PNG images of glass, each of size 340 × 340.
• The txt folders contain label files with the same filename as the corresponding images.
• Each .txt file describes the types and locations of defects in that image.
• If there is no corresponding .txt file for an image, that image should be treated as non-defective.

The label files follow the standard YOLO format. Each line represents one defect and contains five values:

<class_id> <x_center> <y_center> <width> <height>

Where:

• class_id: defect category, there are 3 defect categories:

  

  0: chipped edge

  

  1: scratch

  

  2: stain

• x_center: x-coordinate of the defect bounding box center, normalized by image width.

• y_center: y-coordinate of the defect bounding box center, normalized by image height.

• width: width of the bounding box, normalized by image width.

• height: height of the bounding box, normalized by image height.

All coordinates and sizes are defined with respect to the top-left corner of the image as the origin, using pixels before normalization.

Class imbalance:

• The dataset is imbalanced: there are fewer defective images than non-defective ones.
• However, the ratio of defective vs. non-defective images is roughly consistent between the training and test sets.

3. Tasks

3.1 Task 1 : Binary Defect Classification

Train a model on the train set to perform binary classification:

• Input: a glass image.
• Output: whether the image is defective or non-defective.

You should:

• Treat an image as defective if its corresponding .txt file exists.
• Treat an image as non-defective if there is no .txt file.

On the test set, you must compute and report at least:

• Precision for defective images
• Recall for defective images
• F1-score for defective images

(You may also report accuracy if you wish, but F1 for defective images is the main focus.)

3.2 Task 2 (Optional): Multi-Label Classification

Formulate the task as a multi-label classification task with four labels:

1. No defect
2. Chipped edge
3. Scratch
4. Stain

For each image, you should assign one or more labels:

• If an image has no defects, then it should be labeled as:no defect = 1, others = 0.
• If an image has defects, you should mark a label as 1 if at least one defect of that type appears in the corresponding .txt file.

You should report the Micro F1-score of your model on the test set.

4. Requirements

4.1 Implementation Constraints

• Task 1 (from scratch):
  • You are not allowed to use any autograd tools or any built-in optimization algorithms from machine learning libraries.
  • For example, if you choose a neural network, you must implement both forward and backward propagation yourself.
  • You can use the packages in the WhiteList. TAs will update the Whitelist if your requirements are reasonable.
• Task 2 (optional, no restrictions):
  • You may use any machine learning libraries and built-in tools you like (e.g. PyTorch, TensorFlow, scikit-learn, etc.).

4.2 Submission Format

You should submit a compressed package named:

[your_student_ID].zip

Inside it, there should be a directory with the same name:

[your_student_ID]/
    |- Task1/
    |	|- main.py 
    |   |- For_TA_test.py
    |   |- ... (your other code, models, scripts, etc.)
    |
    |- Task2/ (optional)
    |   |- main.py
    |   |- For_TA_test.py
    |   |- ... (your other code, models, scripts, etc.)
    |
    |- [your_student_ID]-report.pdf   (your project report)

Send this .zip file to:

ml2025fall_ustc@163.com

4.3 Scripts for Training and Evaluation

• Task1/main.py and Task2/main.py is used by you to train your models.
• Task1/For_TA_test.py and Task2/For_TA_test.py is used by the TAs to evaluate your models.

TAs will run your code using the following commands:

cd [your_student_ID]
python Task*/For_TA_test.py --test_data_path /home/usrs/dataset/test

The script For_TA_test.py must:

• Load your trained model.
• Evaluate it on the given test dataset.
• The evaluation must complete execution within 1 hour.
• Print out the your_student_ID and F1-score only. Do not have any other outputs. For example,

PB23000000:0.85

For Task 2, if your program is complex (e.g., requires additional packages, special environment settings, etc.), please include a readme.md explaining:

• How to set up the environment.
• How to run your code and evaluation script.

4.4 Teamwork

• You may work in a team of up to 3 students. If you are confident in your ability, you may of course complete the assignment as a pair or individually..
• In your report, please list each member’s contribution ratio, e.g.: {San Zhang: 30%, Si Li: 35%, Wu Wang: 35%}.

For team submissions:

• Use the team leader’s student ID as the directory and package name, e.g. leaderID.zip containing leaderID/.
• The team leader should be the one to submit the package.
• At the beginning of the report, list the names and student IDs of all team members.

4.5 Trained Models and Dataset

• Remember to save your trained model(s).
• You must submit your trained model files along with the code and report.
• The dataset is large; do not include the dataset itself in the submitted .zip file.

4.6 Report Requirements

Please submit a detailed report in PDF format, named:

[your_student_ID]-report.pdf

The report should include at least:

1. Introduction
2. Methodology
   • Model architecture
   • Loss functions
   • Optimization algorithm (for Task 1, your own implementation)
   • Data preprocessing, augmentation (if any)
3. Experimental Settings
   • Train/validation strategy
   • Hyperparameters
   • Hardware and software environment
4. Results and Analysis
   • Quantitative results (precision, recall, F1-score, etc.).
   • Plots showing how precision, recall, and F1-score for defective images evolve during training on the training set and the test set.
   • Discussion of overfitting/underfitting, class imbalance handling, etc.
5. Conclusion

5. Grading

The grading for Task 1 is based on two F1-scores for defective glass images.

1. First score $s_1$

   $s_1$ is the F1-score for defective images on the provided test set. You must write this value at the very beginning of your report and highlight it in bold.

2. Second score $s_2$

   $s_2$ is the F1-score for defective images obtained when the TAs run your Task1/For_TA_test.py script on another hidden test set.

3. Overall score $s$

   The final score combines $s_1$ and $s_2$ as: $$ s = 0.3 s_1 + 0.7 s_2, $$ the hidden test performance $s_2$ has higher weight to encourage good generalization.

4. Bonus for Task 2

   If you complete Task 2, the TAs will award extra credit based on:

   • Whether the task is completed correctly.
   • The performance (e.g., Micro F1-score).
   • The quality of your modeling and analysis.

6. System Environment

We will evaluate your model on a GeForce RTX 3090 (about 24G memory) under Ubuntu 18.04 system. Please limit the size of your model to avoid OOM.

7. Important Dates

• Team formation deadline:

  The team leader should inform the TAs of all team members (names and student IDs) before 23:59, December 8, 2025.

• Final submission deadline:

  Please submit your [your_student_ID].zip before 23:59, January 23, 2026.

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