Task Image Classification Question & Answer Guide (With Explanation)
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What This Question Is About
This question relates to task image classification and requires a structured academic response.
How to Approach This Question
Start by identifying the main issue, then apply relevant academic frameworks.
Key Explanation
This topic involves task image classification. A strong answer should include explanation, application, and examples.
Original Question
# Task 1 – Image Classification In this first task, you will create a deep learning model to classify images of skin lesions into one of seven classes: 1. “MEL” = Melanoma 2. “NV” = Melanocytic nevus 3. “BCC” = Basal cell carcinoma 4. “AKIEC” = Actinic keratosis 5. “BKL” = Benign keratosis 6. “DF” = Dermatofibroma 7. “VASC” = Vascular lesion The data for this task is a subset of: https://challenge2018.isic-archive.com/task3/ The data for this task is inside the `/content/data/img` folder. It contains ~3,800 images named like `ISIC_000000.jpg` and the following label files: * `/content/data/img/train.csv` * `/content/data/img/val.csv` * `/content/data/img/train_small.csv` * `/content/data/img/val_small.csv` The `small` versions are the first 200 lines of each partition and are included for debugging purposes. To save time, ensure your code runs on the `small` versions first. ## Task 1a. Explore the training set **INSTRUCTIONS**: Check for data issues, as we have done in the labs. Check the class distribution and at least 1 other potential data issue. Hint: Look in `explore.py` for a function that can plot the class distribution. **REPORT**: What did you check for? What data issues are present in this dataset? import pandas as pd IMG_CLASS_NAMES = [“MEL”, “NV”, “BCC”, “AKIEC”, “BKL”, “DF”, “VASC”] train_df = pd.read_csv(‘/content/data/img/train.csv’) val_df = pd.read_csv(‘/content/data/img/val.csv’) train_df.head() from PIL import Image # Change the filename to view other examples from the dataset display(Image.open(‘/content/data/img/ISIC_0024306.jpg’)) import explore # TODO – Check for data issues # Hint: You can convert from one-hot to integers with argmax # This way you can convert 1, 0, 0, 0, 0, 0, 0 to class 0 # 0, 1, 0, 0, 0, 0, 0 to class 1 # 0, 0, 1, 0, 0, 0, 0 to class 2 # so it should be something like the following: # train_labels = train_df.values[….].argmax(….) # val_labels = val_df.values[….].argmax(….) # – you need to fill in the … parts with the correct values. # You should then print output the contents of train_labels to see if # it matches the contents of train.csv # # Next you can plot the class distributions like the following: # explore.plot_label_distribution(….) # – do the above for both the train and val labels. # # Following this look for other potential problems with the data # You may also think of any other potential problems with the data.
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