Improve Object Localization in PyTorch: Part 2

Using the PyTorch library, we plotted images from a data set and applied bounding boxes to them. We then split the data set into training and validation sets. To augment the data set for localization or detection, we used the popular library Alimentations. We created custom data sets to retrieve image and bounding box pairs according to the given index.

Selecting and Plotting Images πŸ–ΌοΈ

Selecting the Example Image

In this task, we will select an image from the dataset using the iloc function. We’ll read the image using the CV2 function and convert it from BGR to RGB format. Then, we will plot the image and the bounding box.

RowImage Path

Plotting the Image and Bounding Box

We’ll use the CV2 function to create the bounding box and plot the image with the bounding box. We’ll color the bounding box in red and set the line size to 2.

Splitting the Dataset πŸ“Š

Training and Validation Split

Due to the small size of the dataset (186 examples), we will split it into training and validation sets using the train_test_split function. We will not create a specific test set, as we can use the validation set for inference.

Data Augmentation Using Albumentations πŸ“ˆ

Augmentation for Localization Task

We will use Albumentations, a well-known library for data augmentation, to perform augmentation specific to object localization tasks. The augmentation process differs from that of classification tasks, as it also involves adjusting the bounding boxes according to the transformations applied to the images.

Writing Augmentations for Training and Validation

For the training dataset, we will define a set of augmentations including resize, horizontal and vertical flips, and rotation. Additionally, we will specify the parameters for bounding box transformations. For the validation dataset, only resizing will be applied.

Conclusion 🎯

In this part, we successfully selected and plotted images from the dataset, split the dataset into training and validation, and defined data augmentations using Albumentations. The next task will involve creating a custom dataset for image and bounding box pairs.

Key Takeaways:

  • Albumentations library is essential for object localization data augmentation.
  • Unique augmentation strategies are required for classification and localization tasks.


Q: Why is data augmentation different for object localization tasks compared to classification tasks?
A: Data augmentation for localization tasks involves adjusting the bounding boxes along with transformations applied to the images, unlike classification where labels remain unchanged.

Q: What is the purpose of splitting the dataset into training and validation sets?
A: Splitting the dataset allows us to train the model on part of the data and evaluate its performance on a separate portion.

Q: Why is Albumentations library specifically used for object detection tasks?
A: Albumentations provides a wide range of augmentations, including those designed for object detection and localization tasks.


For more information on Albumentations library, visit Albumentations Documentation

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