Feature Pyramid Network Feature pyramids are a basic component for detecting objects on different scales. Before this paper, a lot of research has been avoiding these pyramid structures due to their high computational and memory costs. Feature Pyramid Network tackles…
Deep Metric Learning Let’s say we want to create a model that can do face recognition(Face Identification and face verification). We CAN use traditional deep learning since it can perform really well but it requires a lot of training data.…
Compared with 2D Object Detection, there are several additional parameters to consider when it comes to 3D object detection 2D Object Detection: X coordinate for the center of the bounding box Y coordinate for the center of the bounding box…
I’d like to share 2 different ways to save and load a model using Pytorch. Saving The Entire Model #save model torch.save(model, PATH) #load model model = torch.load(PATH) model.eval() This save/load process has the least amount of code to implement.…
Polynomial Learning Rate For deep learning models, the learning rate is one of the most important hyper-parameters in any deep neural network optimization process. Polynomial Learning Rate is a proposed technique to apply learning rate decay and optimize such process.…
Today I tried to do transfer learning using PIDNet (Since I just learned about PIDNet). Compared to my first attempt, the output is getting slightly better but still not to the level where it is actually useful.
Today I’ve learned about bilinear interpolation for images, so I’d like to share it here. To simplify the concept, here is an example if we were to upscale an image by a factor of 2 using bilinear interpolation. For semantic…
torch.nn.CrossEntropyLoss() Using torch.nn.CrossEntropyLoss() as a loss function for semantic segmentation models was first confusing for me, so I’d like to share it here. CrossEntropyLoss is for multi-class models and it expects at least 2 arguments. One for the model prediction…
Today I’ve learned about Bagging, so I’d like to share it here. Bagging is when you have multiple models to vote for the correct answer. This helps decrease generalization errors.
Non-Max Suppression is a post-processing method for object detection tasks. In most cases, an object detection model will predict multiple boxes for a single object like the picture in my note. However, we don’t want this crowded output. We instead…