Image-Classfication-Tasks

Week 1

• Learning Objectives: Basic image classification tasks with simple models using Pytorch and Jax

• Learning Outcomes: Finish using Pytorch and Jax to build a Lenet-5 model to do image classification tasks.

• Findings & Conclusion:

Framework/Diff	Model Bulding	Model Training	Others
Pytorch	- Model is a class - Define each layer as variables - Foward Function manually goes through each layer	- Has dataloader that can be enumerate - Usually use built-in loss function - Usually we call optimizer.step() to update	- Provide plenty of datasets
Jax	- Model is treated as a function - stax for example returns model parameters and foward function	- Has to define data_stream method - Has to self-define loss function - Update each batch state and pass to the next	- Has to manually define dataset

Week 2

• Learning Objectives: Getting started on Julia and corresponding libraries.
• Material:
• • https://syl1.gitbook.io/julia-language-a-concise-tutorial/language-core/getting-started
• • https://github.com/denizyuret/Knet.jl/tree/master/tutorial
• Learning Outcomes: Finish using Julia and Knet building Lenet5 and classify MNIST data.
• Findings & Conclusion:
• • Julia is very similar to Python
• • Package Reference is not very clear
• • It can be very easy and clear to build each layer of NN by using the Julia Constructor

Week 3

• Learning Objectives: Try to load self-defined data to each of the three previous learned pipeline.
• Learning Outcomes: Finish using Julia, Pytorch, and Knet to load custome dataset.

Week 4-10: Classify the X-Ray dataset using different models w/ high accuracies

• Outline:
  The NIH Chest X-rays dataset is composed of about 110,000 chest X-ray images with 15 classes (14 diseases, and one for "No findings"). We are going to build/utilize different models to perform classification.
• Dataset & Preparation:
  For loading data only, Zipfile is an easy way to load data. You probably do not want to unzip the whole dataset (~90G) if you do not plan to train. The code below will help you orient all the data paths and, assuming you want to do binary classification, truning lables into binary.

 zf = z.ZipFile(data_path) 
 df = pd.read_csv(zf.open('Data_Entry_2017.csv')) # load paths&labels
 
 img_name = df.iloc[1, 0]
 df = df.loc[:, "Image Index":"Finding Labels"]

 # Data Preparation
 img_paths = {os.path.basename(name): name for name in zf.namelist() if name.endswith('.png')}
 df['path'] = df['Image Index'].map(img_paths.get)
 df.drop(['Image Index'], axis=1,inplace = True) # keep path and labels only

 # Make the data binary
 labels = df.loc[:,"Finding Labels"]
 one_hot = []
 for i in labels:
    if i == "No Finding":
         one_hot.append(0)
    else:
         one_hot.append(1)
 one_hot_series = pd.Series(one_hot)
 one_hot_series.value_counts()
 df['label'] = pd.Series(one_hot_series, index=df.index)
 df.drop(['Finding Labels'], axis=1,inplace = True)

If you print the data frame, you should see something like this:

                                 path  label
0  images_001/images/00000001_000.png      1
1  images_001/images/00000001_001.png      1
2  images_001/images/00000001_002.png      1
3  images_001/images/00000002_000.png      0
4  images_001/images/00000003_000.png      1

• Experiment 1:
  • Model: Pytorch pretrained Resnet50
  • Trainable Layers: Layer2-4 and FC
  • Number of Classes: 2(Positive or Negative)
  • Best Achieved Accuracy: .93
• Experiment 2:
  • Model: ChexNet
  • Trainable Layers: All
  • Number of Classes: 15 (14 Diseases and 1 No Finding)
  • Best Achieved Accuracy: .8
• Next Step:
  • Ensemble
  • Hierarchical-Learning
  • Label-smoothing
• Reference & Resources:
  https://www.kaggle.com/nih-chest-xrays/data
  https://jrzech.medium.com/reproducing-chexnet-with-pytorch-695ff9c3bf66
  https://arxiv.org/pdf/1711.05225.pdf
  https://stanfordmlgroup.github.io/competitions/chexpert/