代写data编程、代做Java/c++程序
XJTLU Entrepreneur College (Taicang) Cover Sheet
Module code and Title DTS304TC Machine Learning
School Title School of AI and Advanced Computing
Assignment Title Assessment Task 2
Submission Deadline 23:59, 6th April (Sunday), 2025
(China Time, GMT + 8)
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DTS304TC Machine Learning
Coursework – Assessment Task 2
Submission deadline: TBD
Percentage in final mark: 50%
Learning outcomes assessed: C, D
Learning outcome C: apply ML algorithms for specific problems.
Learning outcome D: Demonstrate proficiency in identifying and customizing aspects on ML
algorithms to meet particular needs.
Individual/Group: Individual
Length: This assessment comprises a guided coding and experimentation project, along with an
associated research report.
Late policy: 5% of the total marks available for the assessment shall be deducted from the assessment
mark for each working day after the submission date, up to a maximum of five working days
Risks:
• Please read the coursework instructions and requirements carefully. Not following these instructions
and requirements may result in a loss of marks.
• The formal procedure for submitting coursework at XJTLU is strictly followed. Submission link on
Learning Mall will be provided in due course. The submission timestamp on Learning Mall will be
used to check late submission.
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Guided Project: Street-view Semantic Segmentation with U-Net
In this coursework, you will explore street-view semantic segmentation using a U-Net model across
multiple challenging scenarios.
Part A focuses on evaluating a pretrained daytime segmentation model. You will write your own
evaluation script and compute metrics such as Dice and accuracy. Then, by examining both
successful and failed segmentations, you will highlight the difficulties of segmenting diverse
daytime scenes and discuss why semantic segmentation can be challenging.
Part B shifts attention to night-time images. You will evaluate the same pretrained model on night time data, then implement fine-tuning using various loss functions (e.g., Dice, Cross-Entropy,
combinations of both, or other losses of interest). In addition, you will experiment with one
additional training technique of your choice—such as different data augmentations, different
network learning-rate schedulers, optimizers (including momentum training), or layer-wise fine tuning—and compare the results, explaining the outcomes. You will also discuss from a theoretical
standpoint why combining Dice and Cross-Entropy losses can be advantageous and illustrate how
fine-tuning improves performance on night-time images.
Part C involves retraining a U-Net from scratch on daytime images, with a focus on small or distant
objects (e.g., people or cars far away in the scene). You are encouraged to propose and implement
one or two strategies—such as incorporating object detection, targeted data augmentation,
different loss functions, or multi-scale architectures—to enhance segmentation performance for
these harder-to-segment classes. You will provide both quantitative metrics and qualitative
analyses of your results.
Part D offers an opportunity to reflect on the interesting and novel aspects of the work you
developed during the coursework, the principal challenges you faced, how you addressed them,
and the lessons you learned. Your coding quality will also be evaluated, so focus on clarity,
organization, and efficiency. Completing this coursework will deepen your understanding of
semantic segmentation metrics, fine-tuning techniques, network modifications for small object
detection, and the practical challenges of deploying segmentation models under varied lighting and
scene conditions.
The assessment comprises two parts:
1. Coding and experimentation
2. Completing the assessment work sheet
As you work on the coding and experimentation, it is crucial to record and document all relevant
findings; these will be integrated into your assessment sheet.
Before starting this project, please ensure you have downloaded the pretrained model, the assessment
codebase, and the example Jupyter notebook.
Notes:
1. A sufficiently powerful GPU is required to complete this coursework. You may use the GPU
machines available in our school labs (e.g., FYP labs). Please schedule and coordinate usage
with your classmates well in advance to avoid conflicts.
2. You are strongly encouraged to review the provided code and training notebook, as you will
refer to and modify them throughout the project. Feel free to use code covered during our lab
sessions or other online resources. However, please provide proper citations and links to any
external resources you use.
Important: You may not use ChatGPT to directly generate answers for the coursework. It is
important to think critically and develop your own solutions—by yourself or through reputable
online resources—to strengthen your research and critical thinking skills. You may, however,
use ChatGPT for code understanding or debugging, as well as for grammar and writing
assistance. If you do so, use it responsibly, ensuring you fully understand and take ownership
of all the content in your coursework.
(A) Evaluate the Pretrained Model on Bright Sunny Test Images (10 marks)
Complete the supplied notebook dts304_a2_part1.ipynb. The pretrained model and testing Dataloader
code are provided, and you are required to implement both the testing loop and the evaluation metrics.
(A1) Testing Loop
Implement the testing loop to generate predictions for the test images using the pretrained
model.
Ensure the model is in evaluation mode (model.eval()) and use torch.no_grad() to optimize
performance and accuracy.
(A2) Accuracy Metrics
1. Within the testing loop, calculate global image accuracy and per-class accuracy for each
image, as well as for the overall dataset.
Global Image Accuracy
Defined as the percentage of correctly classified pixels per image.
The overall dataset global image accuracy is the average of the global
accuracies across all images.
Per-Class Accuracy (IOU)
Use Intersection-over-Union (IOU) for each of the 14 categories.
Compute mean-IOU by averaging across the 14 categories.
If a category does not appear in the ground truth or prediction, assign NaN and
use np. nanmean for the mean-IOU calculation.
The overall dataset per-class IOU is obtained by averaging the per-class IOU
scores for each category across all images.
2. Calculate the overall dataset global image accuracy and the overall dataset per-class IOU
scores. Note any significant variations in the IOU scores across different classes and discuss
potential reasons for these differences.
(A3) Rank the images based on accuracy and IoU scores, then select a few examples where the model
performs exceptionally well and others where it struggles. Use these selections to illustrate the
strengths and limitations of your street-view segmentation approach. For example, the model might
accurately segment large, well-lit vehicles but struggle with small or distant pedestrians, poorly
illuminated regions, or overlapping objects. In each case, explain why these challenges arise (e.g.,
limited pixel information, lighting variations, or complex backgrounds), and use your chosen images to
demonstrate these issues.
B) Fine-Tune the Pretrained Model on the Cloudy Dataset (30 marks)
Finish the supplied notebook dts304_a2_part2.ipynb. The pretrained model and testing data loader code
for the cloudy dataset are provided. You will evaluate the model’s current performance, implement fine tuning using various loss functions, and then re-evaluate and analyse the results.
(B1) Evaluate Cloudy Dataset Performance. Use the code from Part 1 (or a similar approach) to
evaluate the model’s performance on the cloudy dataset by calculating Dice and accuracy (or other
relevant metrics). Since cloudy conditions typically present additional challenges, you may observe a
decrease in segmentation quality compared to the sunny scenes tested in Part 1. This lower performance
highlights the need for further enhancements—such as model finetuning—to improve the model’s
robustness under varying weather conditions.
(B2) Fine-Tuning
1. Adapt Training Notebook: Modify the notebook to fine-tune the model on the cloudy
dataset.
2. Loss Functions: Experiment with different loss functions (e.g., Dice, CE, Dice + CE)
during the finetuning and analyse their impact on performance.
3. Additional Training Technique: Implement at least one additional technique of your
choice (e.g., data augmentations, alternative optimizers with momentum, learning-rate
scheduling, or selective layer fine-tuning).
4. Training Process Monitoring. Plot and compare training vs. validation loss to track
improvements. Include at least one plot in your assessment worksheet to demonstrate
how you selected the best fine-tuned model.
(B3) Results and Analysis
1. Loss Function Insights: Discuss the theoretical/algorithm properties of Dice and Cross Entropy (CE) losses, including why their combination may offer unique advantages (e.g.,
gradient considerations). Compare these theoretical insights to your empirical findings by
reporting accuracy and IOU scores for each loss configuration—Dice-only, CE-only, and
Dice + CE.
2. Enhancements & Experiments: Summarize the additional technique(s) you employed—
such as new augmentations, different optimizers, or layer-wise training—by presenting
both theoretical/algorithmic rationale and the observed experimental outcomes.
3. Re-Evaluation: After selecting the most effective loss function and training
enhancements, re-test your best fine-tuned model using the same procedure as the initial
cloudy dataset evaluation. Present global image accuracy and per-class IOU results and
compare them against the baseline to highlight any performance gains.
C) Retrain From Scratch on daytime images & Enhance Small-Object
Segmentation (35 Marks)
You are provided with the notebook dts304_a2_part3.ipynb, which you will use to train a segmentation
model on daytime images. Your primary objective is to improve segmentation accuracy for small
objects—specifically cars, pedestrians, and lane-markings—while maintaining or improving overall
performance on other classes.
(C1) Small-Object Challenge Evaluation (Cars, Pedestrians, Lane-Markings)
1. Why small objects are difficult: Distant cars and pedestrians often occupy only a few pixels,
leading to lower accuracy or missed detections. Lane markings can be thin and fragmented,
causing false negatives and reduced IoU.
2. You are given a function called compute_object_level_stats_percentile that:
Splits objects into “small” or “large” based on a threshold (e.g., number of pixels) for
each category.
Computes precision, recall, and F1-score for each category (small vs. large objects).
Use this function to analyse how well the model segments small vs. large cars,
pedestrians, and lane markings.
3. Tasks:
Calculate Small/Large Object Metrics: Run compute_object_level_stats_percentile
for car, lane-mark, and pedestrian classes.
Compare Precision, Recall, F1: Compare small-object metrics with large-object
metrics to validate that segmentation of small objects is indeed a challenge for the
car, lane-marker, and pedestrian classes.
Provide Visual Examples: Show at least five images with ground-truth
segmentations vs. model prediction results highlighting missed or partially
segmented small objects.
(C2) Strategies to Improve Small-Object Segmentation
After identifying weaknesses in small-object segmentation, choose at least one of the following strategies
(implementing two strategies may yield higher marks):
1. Combine Semantic Segmentation with Object Detection
Use an object detector to first locate small objects (cars/pedestrians), then apply a
segmentation network to those regions.
Or run a segmentation model and a detection model in parallel, fusing their outputs
for improved small-object accuracy.
You may use a pre-trained object detection model downloaded from the internet to
detect cars and pedestrians. If you choose this method, it is acceptable to omit
experiments for the 'lane-marker' class if no suitable object detection model is
available for it.
2. Custom Data Augmentation
Example: Randomly zoom in on regions containing small objects, increasing their
resolution during training.
Alternatively, duplicate or scale small objects synthetically to increase their
representation in the training set.
3. Pixel Weighting Map
Assign higher loss weights to pixels belonging to small objects
Ensures the model pays more attention to the pixels of small objects but requires
careful tuning to avoid overfitting.
4. Multi-Scale / Pyramid Architectures
Incorporate modules like ASPP (Atrous Spatial Pyramid Pooling) to capture features
at multiple scales.
Helps detect smaller objects without losing overall context.
5. Other approaches
You can select other approaches as you wish (for example, different loss function).
However, if your approach is overly simplistic or only involves straightforward
parameter tuning (e.g., adjusting image resolution, learning rate, or network
parameters), it will not be awarded high marks.
Modify or create a new training loop in dts304_a2_part3.ipynb to include your chosen technique(s).
Clearly mark or comment your changes. Provide all source code in your submission files and briefly
mention modifications in your assessment worksheet.
(C3) Results & Analysis
1. Post-Training Evaluation
Recompute overall metrics (global accuracy, mean IoU) as you did for the original
model.
Use compute_object_level_stats_percentile again to see how your improvements
affect small-object metrics for cars, pedestrians, and lane-markings.
Compare (precision_small, recall_small, F1_small) before and after your changes.
2. Highlight Gains
Discuss what strategies (1 or 2 strategies) have you made to improve small object
segmentation and explain their algorithm principles.
If you employed multiple improvements or strategies, clearly explain how each
individual approach/strategy contributed to the enhancement of small-object
segmentation based on experimental evaluation results (for example, the small-object
metrics, mean IOU or global accuracy) and/or your analysis.
Note any trade-offs, such as slightly lower performance on large objects or other
classes.
3. Qualitative Examples
Show side-by-side images (ground truth vs. predicted segmentation) demonstrating
improvements in small-object detection.
Point out any remaining challenges (extremely tiny objects, occlusions, etc.).
D) Coursework Reflections (15 Marks)
Write a brief reflection highlighting unique or interesting algorithms or strategies you implemented in
the coursework.
Reflect on the challenges you personally faced during the coursework, describing the efforts you
made to overcome them and the key lessons you learned.
Based on your reflections, discuss potential future work that could further enhance semantic
segmentation in street-view applications.
E) Coding Quality, Worksheet Quality and Adherence to Submission
Guidelines (10 Marks)
Ensure your code is clear, easy to understand, and includes well-written comments to explain its
functionality.
Follow the worksheet template, formatting guidelines, and all prescribed requirements for the
submission.
Write in clear and proper English, keeping your answers concise and within the specified word limit.
Pay close attention to maintaining excellent formatting throughout.
Submission Guidelines
Worksheet: Please complete and submit a worksheet following the template provided at the end of
this document. Include relevant images, diagrams, or tables to support your findings (these are not
included in the word count).
Submission Format: Submit your worksheet as a PDF file through the Learning Mall platform.
Include all required components: code (Jupyter notebooks), additional experimental scripts or
custom-created code, and the trained model. Do not submit dataset images to the Learning Mall.
Jupyter Notebook Guidelines:
1. Your notebook must be well-organized. Use notebook cells to structure your work with clear
headings and explanations.
2. Include comments in code cells to describe the purpose and functionality of key sections,
facilitating the grading process.
3. All code execution results must be clearly visible in the notebook and match the results
provided in the worksheet.
4. Additionally, the notebook should be fully reproducible—running it from start to finish
should not produce any errors.
5. If you have written supplementary code that is not contained within the Jupyter notebook,
you must submit that as well to guarantee that your Jupyter notebook functions correctly.
Verification: After submission, download and verify that your file is viewable to ensure a successful
submission.
Project Material Access Instructions
To access the complete set of materials for this project, including the dataset, code, and Jupyter notebook
files, please use the links below:
OneDrive Link: https://1drv.ms/f/s!AoRfWDkanfAYoo1wJREiHZvXT_rKgA?e=hw3OcS
When extracting the materials, use the following password to unlock the zip file: DTS304TC (case sensitive, enter in uppercase).
DTS304TC Assessment Task 2 Worksheet
Student ID No:
Please be concise when writing your answers. For most questions, a few paragraphs (around
100 to 300 words, depending on the question) along with optional tables and figures should be
sufficient.
There is no need to write lengthy responses.
For certain questions, specific page or word limits have been set—please ensure your answers
adhere to these limits.
A. Evaluate the Pretrained Model on Bright Sunny Test Images
A1: What are your overall accuracy and IoU scores for each class, as well as the average IoU score
across all classes? There are significant differences in IoU scores between classes—what are the reasons
for these differences?
A2: Can you select a few images where the segmentation model performs well and others where it
struggles? For example, does it accurately segment large, well-lit objects but face difficulties with
small or distant ones, poor lighting, or overlapping objects? Do you have any unique or interesting
observations? Use these images to illustrate and explain the challenges (e.g., limited pixel information,
lighting variations, or complex backgrounds).
B. Fine-Tune the Pretrained Model on the Cloudy Dataset
B1: Summarize the major experimental results in a table, including global accuracy and average IoU
scores (across all classes) before fine-tuning, after fine-tuning with different loss functions (e.g., CE Loss,
Dice Loss, and CE + Dice Loss), and with or without the selected network optimization techniques (e.g.,
new augmentations, alternative optimizers, or layer-wise training). Also, include the global accuracy and
per-class IoU score of the best model after fine-tuning.
B2: Discuss the theoretical/algorithm properties of Dice and Cross-Entropy (CE) losses, including why
their combination may offer unique advantages (e.g., gradient considerations). Compare these theoretical
insights to your empirical findings by reporting accuracy and IOU scores for each loss configuration—
Dice-only, CE-only, and Dice + CE.
B3: Discuss the techniques you employed, such as new augmentations, alternative optimizers, or layer wise training. What are the underlying principles of these techniques, and how do they contribute to your
fine-tuning process? Explain whether they improved their performance (presenting performance scores)
and why. Please refrain your discussion to less than 1 page.
B4: Provide at least one plot showing the training vs. validation loss during the training process to track
improvements. Explain how you used these loss curves to select the best model.
C. Retrain From Scratch & Enhance Small-Object Segmentation
C1: Compare Precision, Recall, F1: Using compute_object_level_stats_percentile function. Compare
small-object metrics with large-object metrics to validate that segmentation of small objects is indeed a
challenge for the car, lane-marker, and pedestrian classes. Show at least five ground-truth vs. prediction
images highlighting missed or partially segmented small objects for the car, lane-marker, and pedestrian
classes
C2: Discuss what strategies (1 or 2 strategies) have you made to improve small object segmentation and
explain the algorithm principles. Highlight your algorithm and code if necessarily. Please refrain your
discussion to less than 1 page.
C3: After retraining the model from scratch, recompute the overall metrics (global accuracy and mean
IoU) for the daytime test set. Utilize the compute_object_level_stats_percentile function to assess the
impact of your improvements on small-object metrics for cars, pedestrians, and lane markings. Pay
particular attention to the small-object F1 improvements and any changes in mean IoU.
If you employed multiple improvements or strategies, clearly explain how each individual
approach/strategy contributed to the enhancement of small-object segmentation based on experimental
evaluation results and/or your analysis.
C4: Present a concise table comparing original model vs. your best retrained model(e.g., small-object F1
improvement, any changes in mean IoU). Discuss any trade-offs, such as slightly lower performance on
large objects or other classes. Show side-by-side images (ground truth vs. predicted segmentation)
demonstrating improvements in small-object detection. Point out any remaining challenges (extremely
tiny objects, occlusions, etc.).
D. Coursework Reflections
(Page Limit: Up to 2 pages or 600 words)
D1. Write a thoughtful and personal reflection highlighting the unique or interesting algorithms or
strategies you implemented in the coursework.
D2. Reflect deeply on the specific challenges you encountered during the coursework. Share your
personal journey in overcoming these obstacles, including the strategies or problem-solving methods you
employed, and the key lessons you learned along the way.
D3. Based on your reflections, discuss potential future work that could further enhance semantic
segmentation in street-view applications.
Avoid generic responses and instead focus on your individual experiences, thoughts, and contributions.