The focus here is more on how to read an image and its bounding box, resize and perform augmentations correctly, rather than on the model itself. The goal is to have a good grasp of the fundamental ideas behind objectdetection, which you can extend to get a better understanding of more complex techniques such as YOLO.
1.Dataset
- Convert the bounding box into an image (called mask) of the same size as the image it corresponds to. This mask would just have 0 for background and 1 for the area covered by the bounding box.
- Resize the mask to the required dimensions using the information from .xml file .
- Extract bounding box coordinates from the resized mask
3.Data Augmentation
- I’ve used flip, rotation, center crop and random crop.
Before Transformation
After Transformation
4.PyTorch Model
For the model, I’ve used a very simple pre-trained resNet-34 model. Since we have two tasks to accomplish here, there are two final layers — the bounding box regressor and the image classifier.
Prediction on Test Image
