Description

Machine Learning HW3
ML TAs
Objective – Image Classification
1. Solve image classification with convolutional neural networks.
2. Improve the performance with data augmentations.
3. Understand popular image model techniques such as residual.
Task Introduction – Food Classification
● The images are collected from the food-11 dataset classified into 11 classes.
● Training set: 9866 labeled images
● Validation set: 3430 labeled images
● Testing set: 3347 images
Rules
● DO NOT attempt to find the original labels of the testing set.
● DO NOT use any external datasets.
● DO NOT use any pretrained models.
○ Also, do not attempt to “test how effective pretraining is” by submitting to kaggle.
○ But make sure you do not use pretrained models. Most code use those.
Baseline
Simple : 0.50099
Medium : 0.73207 Training Augmentation + Train Longer
Strong : 0.81872 Training Augmentation + Model Design + Train Looonger (+ Cross Validation + Ensemble)
Boss : 0.88446 Training Augmentation + Model Design +Test Time
Augmentation + Train Looonger (+ Cross Validation + Ensemble)

Baseline GPU Time Estimation
● All results are benchmarked on kaggle
Simple : 15~20mins Strong : Model X + Augmentation B + Resplit- 12 hrs
Medium : Augmentation A – 6hrs Model Y + Augmentation B + Resplit- 10 hrs
Augmentation B – 80 min Model Z + Augmentation B + Resplit- 6 hrs
Boss : Model Z + Augmentation B + TTA + Resplit-12 hrs
Or Ensemble
Model Y + Augmentation B + TTA + Resplit- 12 hrs
Model X + Augmentation B + TTA + Resplit-12 hrs
Submission Format
The file should contain a header and have the following format:

Both type should be strings. Id corresponds to the jpg filenames in test. Follow the sample code if you have trouble with formatting.
Model Selection
● Visit torchvision.models for a list of model structures, or go to timm for

Data Augmentation
● Modify the image data so non-identical inputs are given to the model each epoch, to prevent overfitting of the model
● Visit torchvision.transforms for a list of choices and their corresponding effect. Diversity is encouraged! Usually, stacking multiple transformations leads to better results.
● Coding : fill in train_tfm to gain this effect

Advanced Data Augmentation – mixup

0 1 0 1
Advanced Data Augmentation – mixup
● Coding :
● In your torch.utils.Dataset, __getitem__()needs to return an image that is the linear combination of two images.
● In your torch.utils.Dataset, __getitem__() needs to return a label that is a vector, to assign probabilities to each class.
● You need to explicitly code out the math formula of the cross entropy loss, as CrossEntropyLoss does not support multiple labels.
Test Time Augmentation
● The sample code tests images using a deterministic “test transformation”
● Coding : You need to fill in train_tfm, change the augmentation method for test_dataset, and modify prediction code to gain this effect train_tfm test_tfm test_tfm
+ >
Pred Pred Pred Pred Pred Pred Pred

Pred
Test Time Augmentation
● Usually, test_tfm will produce images that are more identifiable, so you can assign a larger weight to test_tfm results for better performance.
train_tfm test_tfm

avg_train_tfm_pred
● Ex : Final Prediction = avg_train_tfm_pred * 0.5 + test_tfm_pred* 0.5
Cross Validation
● Cross-validation is a resampling method that uses different portions of the data to validate and train a model on different iterations. Ensembling multiple results lead to better performance.
● Coding : You need to merge the current train and validation paths, and resample form those to form new train and validation sets.
Validation Train
Train Validation Train
Train Validation Train
Train Validation
Ensemble
Cross Validation
● Even if you don’t do cross validation, you are encouraged to resplit the train/validation set to suitable proportions.
○ Currently, train : validation ~ 3 : 1, more training data could be valuable.
Ensemble
● Average of logits or probability : Need to save verbose output, less ambiguous
● Voting : Easier to implement, need to break ties
● Coding : basic math operations with numpy or torch

Kaggle Tutorial

Kaggle Introduction
● Kaggle GPU : 16G NVIDIA TESLA P100
○ https://www.kaggle.com/docs/efficient-gpu-usage
● Faster data IO
● Easier checkpoint reusing
● Limited to 30+ hrs/week depending on usage.
● Limited to 12hrs/run
● We strongly recommend that you run with Kaggle for this homework
How to run
How to get data : In the input section, there should already be data titled
“ml2022spring-hw3”
If there isn’t, click on Add data and find “ml2022spring-hw3”
How to use gpu : Change accelerator to “gpu” when you run your code.
Since GPU time is limited, It is advised to NOT utilize GPU while debugging
How to Run interactively : The commands are very similar to google colab
Any output writing to ./ will end up here, you can download it
How to Run in background: Execute code from start to end, all results would be save permanently. (Output is limited to 20G, max run time = 12hrs)
Make sure your code is bug free, as any error in any code block would result in early stopping

How to train and predict
1. Run your code in background
2. Find the output data “./submission.csv” and upload it to the submission page
How to retrain from a checkpoint
1. Run your code in background
2. Find the output model and save it as a dataset
3. Import your dataset into the notebook via “Add data”
4. Modify your code to load your checkpoint
5. Run your code in background
6. Find the output data “./submission.csv” and upload it to the submission page

Tips and tricks

Time management
=> Run any code with GPU on kaggle today (3/4) to get (possible) extra time next week.
● Time consumption is the sum of notebooks running interactively with gpu and running in background with gpu.
● Please start early
Time management
● You can go over the time limit moderately. Kaggle will not interrupt your code in the background if it is already running. If your time limit is reached, you cannot run any code with GPU.
=> 時間快用完的時候在背景跑一隻程式，等於多12小時runtime
=> 時間快用完的時候在背景跑兩隻程式，等於多24小時runtime

Time management – Parallelization
● You can run two codes in the background
● If you are satisfied with your code, utilize this to run multiple random seeds/multiple train validation split/multiple model structures, so you can ensemble
A sample procedure for beating the boss baseline
The boss baseline could be beaten with a single model trained on kaggle for 12hrs

Train : 12h

Prediction
Your procedure can be ensemble of models with parallelization
multiple random seeds multiple train validation split multiple model structures
Experimental Tips
● Augmentation is a must to prevent overfitting. A good augmentation can carry on to the testing phase with Test Time Augmentation.
● If you build your own network structure and have implemented augmentation, don’t be afraid to scale up your model. (Many predefined models structure are huge and perform great)
Other tricks……
● on Classification
○ Label Smoothing Cross Entropy Loss ○ FocalLoss
● on Optimization
○ Dropout
○ Gradient Accumulation
○ BatchNorm
○ Image Normalization
Running with Google Colab
● We strongly recommend that you run with Kaggle for this homework
● If you would like to use colab, DO NOT store data in your drive and load from there, the input/output is very slow. (store at ./ instead)
● If you mount your google drive in colab : G-suite google drive now has a storage limit. Since models and data can be large, keep an eye on your used space to prevent your account being suspended.

Report Questions

Q1. Augmentation Implementation (2%)
Implement augmentation by finishing train_tfm in the code with image size of your choice. Copy your train_tfm code and paste it onto the GradeScope.
● Your train_tfm must be capable of producing 5+ different results when given an identical image multiple times.
● Your train_tfm in the report can be different from train_tfm in your
X
O
Q2. Residual Connection Implementation (2%)
Residual Connection is widely used in CNNs such as Deep Residual Learning for Image Recognition. Residual is demonstrated in the following graph.
Image Source : https://towardsdatascience.com/wh
at-is-residual-connection-efb07cab0
d55
Q2. Residual Connection Implementation (2%)
Implement Residual Connections in the Residual_Model, following the graph below. Copy your Residual_Model code and paste it on Gradescope.
● You should only modify the forward part of the model
Submission Format
● train_tfm and Residual_Model are present in colab (scroll to bottom) and kaggle (ML2022HW3 – Report Questions), you only need to modify from our sample code.

Regulations and Grading Policy

Grading
● simple (public) +0.5 pts
● simple (private) +0.5 pts
● medium (public) +0.5 pts
● medium (private) +0.5 pts
● strong (public) +0.5 pts
● strong (private) +0.5 pts
● boss (public) +0.5 pts
● boss (private) +0.5 pts
● code submission +2 pts
● report +4 pts
Total : 10 pts
Code Submission
● NTU COOL
○ Compress your code and pack them into .zip file
<student_ID>_hw3.zip
● Your .zip file should include only
○ Code: either .py or .ipynb
● Do not submit models and data
● File Size Limit : 25MB
● Submit the code that corresponds to your chosen submission in Kaggle (One of the best)
Report Submission
Answer the questions on GradeScope
Deadlines
● Kaggle, Code (NTU COOL), Report (GradeScope)
Rules
● DO NOT attempt to find the original labels of the testing set.
● DO NOT use any external datasets.
● DO NOT use any pretrained models.
○ Also, do not attempt to “test how effective pretraining is” by submitting to kaggle.
○ But make sure you do not use pretrained models. Most code use those.
Rules
● You should finish your homework on your own.
● You should not modify your prediction files manually
● Do not share codes or prediction files with any living creatures.
● Do not use any approaches to submit your results more than 5 times a day.
● Your final grade x 0.9 and 0 pt for this HW if you violate any of the above rules, final grade = Fail for repeat offenders
● Prof. Lee & TAs preserve the rights to change the rules & grades.
Links
Kaggle : https://www.kaggle.com/c/ml2022spring-hw3b
Kaggle code (join competition first) :
https://www.kaggle.com/c/ml2022spring-hw3b/code?competitionId=34954&so rtBy=dateRun
Colab :
https://colab.research.google.com/drive/15hMu9YiYjE_6HY99UXon2vKGk2Kwu gWu
Contact us if you have problems…
● Kaggle Discussion (Recommended for this HW)
● NTU COOL
● Email
○ The title should begin with “[hw3]”